bkbit.models.donor module

class bkbit.models.donor.Activity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Activity', 'biolink:Activity']] = ['biolink:Activity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None)

Bases: ActivityAndBehavior, NamedThing

An activity is something that occurs over a period of time and acts upon or with entities; it may include consuming, processing, transforming, modifying, relocating, using, or generating entities.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Activity', 'biolink:Activity']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Activity', 'definition_uri': 'https://w3id.org/biolink/vocab/Activity', 'exact_mappings': ['prov:Activity', 'NCIT:C43431', 'STY:T052'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixins': ['activity and behavior'], 'narrow_mappings': ['STY:T056', 'STY:T057', 'STY:T064', 'STY:T066', 'STY:T062', 'STY:T065', 'STY:T058']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.ActivityAndBehavior

Bases: Occurrent

Activity or behavior of any independent integral living, organization or mechanical actor in the world

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:ActivityAndBehavior', 'definition_uri': 'https://w3id.org/biolink/vocab/ActivityAndBehavior', 'exact_mappings': ['UMLSSG:ACTI'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.AgingType(value)

Bases: str, Enum

An enumeration.

Other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'Other (specify)'

arteriolosclerosis = 'arteriolosclerosis'

The thickening of the wall of the small arteries and arterioles. It is caused by deposition of hyaline material in the wall or concentric smooth muscle wall hypertrophy. It results in lumen narrowing and tissue ischemia.

atherosclerosis = 'atherosclerosis'

Build-up of fatty material and calcium deposition in the arterial wall resulting in partial or complete occlusion of the arterial lumen.

pTDP_43 = 'pTDP-43'

1 ortholog thereof.

Type:

A protein that is a translation product of the human TARDBP gene or a 1

pTau = 'pTau'

A microtubule-associated protein tau that includes at least one phosphorylated residue.

class bkbit.models.donor.Annotation

Bases: ConfiguredBaseModel

Biolink Model root class for entity annotations.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'class_uri': 'biolink:Annotation', 'definition_uri': 'https://w3id.org/biolink/vocab/Annotation', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.ArtifactType(value)

Bases: str, Enum

An enumeration.

Other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'Other (specify)'

grossCOLON_brain_removalSOLIDUSdissection = 'gross: brain removal/dissection'

A planned process that separates and isolates the brain or tissues thereof for surgical purposes, or for the analysis of their structures.

microscopicCOLON_autolysis = 'microscopic: autolysis'

A programmed cell death process observed in bacteria and filamentous fungi and leading to spontaneous death by lysis. Examples are lysis of the mother cell during sporulation of Bacillus subtilis and self-degradation of fungal cells in Aspergillus nidulans. Autolysis is also involved in bacterial biofilm formation. [GO]

class bkbit.models.donor.Attribute(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Attribute', 'biolink:Attribute']] = ['biolink:Attribute'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, has_attribute_type: str, has_quantitative_value: list[QuantityValue] | None = None, has_qualitative_value: str | None = None)

Bases: NamedThing, OntologyClass

A property or characteristic of an entity. For example, an apple may have properties such as color, shape, age, crispiness. An environmental sample may have attributes such as depth, lat, long, material.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Attribute', 'biolink:Attribute']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

has_attribute_type: str

has_qualitative_value: str | None

has_quantitative_value: list[QuantityValue] | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Attribute', 'definition_uri': 'https://w3id.org/biolink/vocab/Attribute', 'exact_mappings': ['SIO:000614'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['EDAM-DATA', 'EDAM-FORMAT', 'EDAM-OPERATION', 'EDAM-TOPIC'], 'in_subset': ['samples'], 'mixins': ['ontology class'], 'slot_usage': {'name': {'description': "The human-readable 'attribute name' can be set to a string which reflects its context of interpretation, e.g. SEPIO evidence/provenance/confidence annotation or it can default to the name associated with the 'has attribute type' slot ontology term.", 'name': 'name'}}})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.BiologicalEntity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/BiologicalEntity', 'biolink:BiologicalEntity']] = ['biolink:BiologicalEntity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None)

Bases: ThingWithTaxon, NamedThing

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/BiologicalEntity', 'biolink:BiologicalEntity']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'aliases': ['bioentity'], 'class_uri': 'biolink:BiologicalEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/BiologicalEntity', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixins': ['thing with taxon'], 'narrow_mappings': ['WIKIDATA:Q28845870', 'STY:T050', 'SIO:010046', 'STY:T129']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.BirthCountry(value)

Bases: str, Enum

Namibia

AD = 'AD'

Andorra

AE = 'AE'

United Arab Emirates (the)

AF = 'AF'

Afghanistan

AG = 'AG'

Antigua and Barbuda

AI = 'AI'

Anguilla

AL = 'AL'

Albania

AM = 'AM'

Armenia

AO = 'AO'

Angola

AQ = 'AQ'

Antarctica

AR = 'AR'

Argentina

AS = 'AS'

American Samoa

AT = 'AT'

Austria

AU = 'AU'

Australia

AW = 'AW'

Aruba

AX = 'AX'

Åland Islands

AZ = 'AZ'

Azerbaijan

BA = 'BA'

Bosnia and Herzegovina

BB = 'BB'

Barbados

BD = 'BD'

Bangladesh

BE = 'BE'

Belgium

BF = 'BF'

Burkina Faso

BG = 'BG'

Bulgaria

BH = 'BH'

Bahrain

BI = 'BI'

Burundi

BJ = 'BJ'

Benin

BL = 'BL'

Saint Barthélemy

BM = 'BM'

Bermuda

BN = 'BN'

Brunei Darussalam

BO = 'BO'

Bolivia (Plurinational State of)

BQ = 'BQ'

Bonaire, Sint Eustatius and Saba

BR = 'BR'

Brazil

BS = 'BS'

Bahamas (the)

BT = 'BT'

Bhutan

BV = 'BV'

Bouvet Island

BW = 'BW'

Botswana

BY = 'BY'

Belarus

BZ = 'BZ'

Belize

CA = 'CA'

Canada

CC = 'CC'

Cocos (Keeling) Islands (the)

CD = 'CD'

Congo (the Democratic Republic of the)

CF = 'CF'

Central African Republic (the)

CG = 'CG'

Congo (the)

CH = 'CH'

Switzerland

CI = 'CI'

Côte d’Ivoire

CK = 'CK'

Cook Islands (the)

CL = 'CL'

Chile

CM = 'CM'

Cameroon

CN = 'CN'

China

CO = 'CO'

Colombia

CR = 'CR'

Costa Rica

CU = 'CU'

Cuba

CV = 'CV'

Cabo Verde

CW = 'CW'

Curaçao

CX = 'CX'

Christmas Island

CY = 'CY'

Cyprus

CZ = 'CZ'

Czechia

DE = 'DE'

Germany

DJ = 'DJ'

Djibouti

DK = 'DK'

Denmark

DM = 'DM'

Dominica

DO = 'DO'

Dominican Republic (the)

DZ = 'DZ'

Algeria

EC = 'EC'

Ecuador

EE = 'EE'

Estonia

EG = 'EG'

Egypt

EH = 'EH'

Western Sahara*

ER = 'ER'

Eritrea

ES = 'ES'

Spain

ET = 'ET'

Ethiopia

FI = 'FI'

Finland

FJ = 'FJ'

Fiji

FK = 'FK'

Falkland Islands (the) [Malvinas]

FM = 'FM'

Micronesia (Federated States of)

FO = 'FO'

Faroe Islands (the)

FR = 'FR'

France

GA = 'GA'

Gabon

GB = 'GB'

United Kingdom of Great Britain and Northern Ireland (the)

GD = 'GD'

Grenada

GE = 'GE'

Georgia

GF = 'GF'

French Guiana

GG = 'GG'

Guernsey

GH = 'GH'

Ghana

GI = 'GI'

Gibraltar

GL = 'GL'

Greenland

GM = 'GM'

Gambia (the)

GN = 'GN'

Guinea

GP = 'GP'

Guadeloupe

GQ = 'GQ'

Equatorial Guinea

GR = 'GR'

Greece

GS = 'GS'

South Georgia and the South Sandwich Islands

GT = 'GT'

Guatemala

GU = 'GU'

Guam

GW = 'GW'

Guinea-Bissau

GY = 'GY'

Guyana

HK = 'HK'

Hong Kong

HM = 'HM'

Heard Island and McDonald Islands

HN = 'HN'

Honduras

HR = 'HR'

Croatia

HT = 'HT'

Haiti

HU = 'HU'

Hungary

ID = 'ID'

Indonesia

IE = 'IE'

Ireland

IL = 'IL'

Israel

IM = 'IM'

Isle of Man

IN = 'IN'

India

IO = 'IO'

British Indian Ocean Territory (the)

IQ = 'IQ'

Iraq

IR = 'IR'

Iran (Islamic Republic of)

IS = 'IS'

Iceland

IT = 'IT'

Italy

JE = 'JE'

Jersey

JM = 'JM'

Jamaica

JO = 'JO'

Jordan

JP = 'JP'

Japan

KE = 'KE'

Kenya

KG = 'KG'

Kyrgyzstan

KH = 'KH'

Cambodia

KI = 'KI'

Kiribati

KM = 'KM'

Comoros (the)

KN = 'KN'

Saint Kitts and Nevis

KP = 'KP'

Korea (the Democratic People’s Republic of)

KR = 'KR'

Korea (the Republic of)

KW = 'KW'

Kuwait

KY = 'KY'

Cayman Islands (the)

KZ = 'KZ'

Kazakhstan

LA = 'LA'

Lao People’s Democratic Republic (the)

LB = 'LB'

Lebanon

LC = 'LC'

Saint Lucia

LI = 'LI'

Liechtenstein

LK = 'LK'

Sri Lanka

LR = 'LR'

Liberia

LS = 'LS'

Lesotho

LT = 'LT'

Lithuania

LU = 'LU'

Luxembourg

LV = 'LV'

Latvia

LY = 'LY'

Libya

MA = 'MA'

Morocco

MC = 'MC'

Monaco

MD = 'MD'

Moldova (the Republic of)

ME = 'ME'

Montenegro

MF = 'MF'

Saint Martin (French part)

MG = 'MG'

Madagascar

MH = 'MH'

Marshall Islands (the)

MK = 'MK'

North Macedonia

ML = 'ML'

Mali

MM = 'MM'

Myanmar

MN = 'MN'

Mongolia

MO = 'MO'

Macao

MP = 'MP'

Northern Mariana Islands (the)

MQ = 'MQ'

Martinique

MR = 'MR'

Mauritania

MS = 'MS'

Montserrat

MT = 'MT'

Malta

MU = 'MU'

Mauritius

MV = 'MV'

Maldives

MW = 'MW'

Malawi

MX = 'MX'

Mexico

MY = 'MY'

Malaysia

MZ = 'MZ'

Mozambique

NC = 'NC'

New Caledonia

NE = 'NE'

Niger (the)

NF = 'NF'

Norfolk Island

NG = 'NG'

Nigeria

NI = 'NI'

Nicaragua

NL = 'NL'

Netherlands (Kingdom of the)

NO = 'NO'

Norway

NP = 'NP'

Nepal

NR = 'NR'

Nauru

NU = 'NU'

Niue

NZ = 'NZ'

New Zealand

OM = 'OM'

Oman

PA = 'PA'

Panama

PE = 'PE'

Peru

PF = 'PF'

French Polynesia

PG = 'PG'

Papua New Guinea

PH = 'PH'

Philippines (the)

PK = 'PK'

Pakistan

PL = 'PL'

Poland

PM = 'PM'

Saint Pierre and Miquelon

PN = 'PN'

Pitcairn

PR = 'PR'

Puerto Rico

PS = 'PS'

Palestine, State of

PT = 'PT'

Portugal

PW = 'PW'

Palau

PY = 'PY'

Paraguay

QA = 'QA'

Qatar

RE = 'RE'

Réunion

RO = 'RO'

Romania

RS = 'RS'

Serbia

RU = 'RU'

Russian Federation (the)

RW = 'RW'

Rwanda

SA = 'SA'

Saudi Arabia

SB = 'SB'

Solomon Islands

SC = 'SC'

Seychelles

SD = 'SD'

Sudan (the)

SE = 'SE'

Sweden

SG = 'SG'

Singapore

SH = 'SH'

Saint Helena, Ascension and Tristan da Cunha

SI = 'SI'

Slovenia

SJ = 'SJ'

Svalbard and Jan Mayen

SK = 'SK'

Slovakia

SL = 'SL'

Sierra Leone

SM = 'SM'

San Marino

SN = 'SN'

Senegal

SO = 'SO'

Somalia

SR = 'SR'

Suriname

SS = 'SS'

South Sudan

ST = 'ST'

Sao Tome and Principe

SV = 'SV'

El Salvador

SX = 'SX'

Sint Maarten (Dutch part)

SY = 'SY'

Syrian Arab Republic (the)

SZ = 'SZ'

Eswatini

TC = 'TC'

Turks and Caicos Islands (the)

TD = 'TD'

Chad

TF = 'TF'

French Southern Territories (the)

TG = 'TG'

Togo

TH = 'TH'

Thailand

TJ = 'TJ'

Tajikistan

TK = 'TK'

Tokelau

TL = 'TL'

Timor-Leste

TM = 'TM'

Turkmenistan

TN = 'TN'

Tunisia

TO = 'TO'

Tonga

TR = 'TR'

Türkiye

TT = 'TT'

Trinidad and Tobago

TV = 'TV'

Tuvalu

TW = 'TW'

Taiwan (Province of China)

TZ = 'TZ'

Tanzania, the United Republic of

UA = 'UA'

Ukraine

UG = 'UG'

Uganda

UM = 'UM'

United States Minor Outlying Islands (the)

US = 'US'

United States of America (the)

UY = 'UY'

Uruguay

UZ = 'UZ'

Uzbekistan

VA = 'VA'

Holy See (the)

VC = 'VC'

Saint Vincent and the Grenadines

VE = 'VE'

Venezuela (Bolivarian Republic of)

VG = 'VG'

Virgin Islands (British)

VI = 'VI'

Virgin Islands (U.S.)

VN = 'VN'

Viet Nam

VU = 'VU'

Vanuatu

WF = 'WF'

Wallis and Futuna

WS = 'WS'

Samoa

YE = 'YE'

Yemen

YT = 'YT'

Mayotte

ZA = 'ZA'

South Africa

ZM = 'ZM'

Zambia

ZW = 'ZW'

Zimbabwe

class bkbit.models.donor.BrainTissueWeightType(value)

Bases: str, Enum

An enumeration.

Fixed_hemibrain = 'Fixed hemibrain'

A hemibrain specimen that has been treated for preservation (typically by formalin).

Fixed_whole_brain = 'Fixed whole brain'

A whole brain specimen that has been treated for preservation (typically by formalin).

Fresh_hemibrain = 'Fresh hemibrain'

A hemibrain specimen that has not been frozen or fixed – i.e., a whole brain that has not yet been treated by a preserving method.

Fresh_whole_brain = 'Fresh whole brain'

A whole brain specimen that has not been frozen or fixed – i.e., a whole brain that has not yet been treated by a preserving method.

Unknown = 'Unknown'

class bkbit.models.donor.CauseOfDeath(value)

Bases: str, Enum

An enumeration.

Abdominal_wall_defect = 'Abdominal wall defect'

Abortion = 'Abortion'

Alcohol_use_disorders = 'Alcohol use disorders'

All_Causes = 'All Causes'

Alzheimer_and_other_dementias = 'Alzheimer and other dementias'

Anencephaly = 'Anencephaly'

Anorectal_atresia = 'Anorectal atresia'

Appendicitis = 'Appendicitis'

Ascariasis = 'Ascariasis'

Asthma = 'Asthma'

Back_pain = 'Back pain'

Benign_prostatic_hypertrophy = 'Benign prostatic hypertrophy'

Bipolar_disorder = 'Bipolar disorder'

Birth_asphyxia_and_birth_trauma = 'Birth asphyxia and birth trauma'

Bladder_cancer = 'Bladder cancer'

Brain_and_nervous_system_cancers = 'Brain and nervous system cancers'

Breast_cancer = 'Breast cancer'

COVID_19 = 'COVID-19'

Cardiovascular_diseases = 'Cardiovascular diseases'

Cataracts = 'Cataracts'

Cerebrovascular_disease = 'Cerebrovascular disease'

Cervix_uteri_cancer = 'Cervix uteri cancer'

Chagas_disease = 'Chagas disease'

Childhood_cluster_diseases = 'Childhood-cluster diseases'

Chlamydia = 'Chlamydia'

Chronic_obstructive_pulmonary_disease = 'Chronic obstructive pulmonary disease'

Cirrhosis_of_the_liver = 'Cirrhosis of the liver'

Cleft_lip = 'Cleft lip'

Cleft_palate = 'Cleft palate'

Colon_and_rectum_cancers = 'Colon and rectum cancers'

Communicable_maternal_perinatal_and_nutritional_conditions = 'Communicable, maternal, perinatal and nutritional conditions'

Congenital_anomalies = 'Congenital anomalies'

Congenital_heart_anomalies = 'Congenital heart anomalies'

Corpus_uteri_cancer = 'Corpus uteri cancer'

Dengue = 'Dengue'

Dental_caries = 'Dental caries'

Diabetes_mellitus = 'Diabetes mellitus'

Diabetes_mellitus_and_endocrine_disorders = 'Diabetes mellitus and endocrine disorders'

Diarrhoeal_diseases = 'Diarrhoeal diseases'

Digestive_diseases = 'Digestive diseases'

Diphtheria = 'Diphtheria'

Down_syndrome = 'Down syndrome'

Drownings = 'Drownings'

Drug_use_disorders = 'Drug use disorders'

Edentulism = 'Edentulism'

Endocrine_disorders = 'Endocrine disorders'

Epilepsy = 'Epilepsy'

Exposure_to_mechanical_forces = 'Exposure to mechanical forces'

Falls = 'Falls'

Fires = 'Fires'

Gallbladder_and_biliary_diseases = 'Gallbladder and biliary diseases'

Gallbladder_and_biliary_tract_cancer = 'Gallbladder and biliary tract cancer'

Gastritis_and_duodenitis = 'Gastritis and duodenitis'

Genitourinary_diseases = 'Genitourinary diseases'

Glaucoma = 'Glaucoma'

Gonorrhoea = 'Gonorrhoea'

Gout = 'Gout'

HIVSOLIDUSAIDS = 'HIV/AIDS'

Hearing_loss_adult_onset = 'Hearing loss, adult onset'

Hepatitis_B = 'Hepatitis B'

Hepatitis_C = 'Hepatitis C'

Hookworm_disease = 'Hookworm disease'

Hypertensive_disorders = 'Hypertensive disorders'

Hypertensive_heart_disease = 'Hypertensive heart disease'

Ill_defined_circulatory_diseases = 'Ill-defined circulatory diseases'

Ill_defined_diseases = 'Ill-defined diseases'

Ill_defined_injuriesSOLIDUSaccidents = 'Ill-defined injuries/accidents'

Ill_defined_malignant_neoplasms = 'Ill-defined malignant neoplasms'

Infectious_and_parasitic_diseases = 'Infectious and parasitic diseases'

Inflammatory_bowel_disease = 'Inflammatory bowel disease'

Inflammatory_heart_diseases = 'Inflammatory heart diseases'

Injuries = 'Injuries'

Insomnia_LEFT_PARENTHESISprimaryRIGHT_PARENTHESIS = 'Insomnia (primary)'

Intentional_injuries = 'Intentional injuries'

Intestinal_nematode_infections = 'Intestinal nematode infections'

Iodine_deficiency = 'Iodine deficiency'

Iron_deficiency_anaemia = 'Iron-deficiency anaemia'

Ischaemic_heart_disease = 'Ischaemic heart disease'

Japanese_encephalitis = 'Japanese encephalitis'

Kidney_cancer = 'Kidney cancer'

Larynx_cancer = 'Larynx cancer'

Leishmaniasis = 'Leishmaniasis'

Leprosy = 'Leprosy'

Leukaemia = 'Leukaemia'

Liver_cancer = 'Liver cancer'

Low_birth_weight = 'Low birth weight'

Lower_respiratory_infections = 'Lower respiratory infections'

Lymphatic_filariasis = 'Lymphatic filariasis'

Lymphomas_multiple_myeloma = 'Lymphomas, multiple myeloma'

Malaria = 'Malaria'

Malignant_neoplasms = 'Malignant neoplasms'

Maternal_conditions = 'Maternal conditions'

Maternal_haemorrhage = 'Maternal haemorrhage'

Maternal_sepsis = 'Maternal sepsis'

Measles = 'Measles'

Melanoma_and_other_skin_cancers = 'Melanoma and other skin cancers'

Meningitis = 'Meningitis'

Mental_Retardation = 'Mental Retardation'

Mesothelioma = 'Mesothelioma'

Migraine = 'Migraine'

Mouth_and_oropharynx_cancers = 'Mouth and oropharynx cancers'

Multiple_sclerosis = 'Multiple sclerosis'

Musculoskeletal_diseases = 'Musculoskeletal diseases'

Natural_disasters = 'Natural disasters'

Nephritis_and_nephrosis = 'Nephritis and nephrosis'

Neuropsychiatric_conditions = 'Neuropsychiatric conditions'

Noncommunicable_diseases = 'Noncommunicable diseases'

Nutritional_deficiencies = 'Nutritional deficiencies'

Obsessive_compulsive_disorder = 'Obsessive-compulsive disorder'

Obstructed_labour = 'Obstructed labour'

Oesophageal_atresia = 'Oesophageal atresia'

Oesophagus_cancer = 'Oesophagus cancer'

Onchocerciasis = 'Onchocerciasis'

Oral_conditions = 'Oral conditions'

Osteoarthritis = 'Osteoarthritis'

Other_Congenital_anomalies = 'Other Congenital anomalies'

Other_STDs = 'Other STDs'

Other_circulatory_diseases = 'Other circulatory diseases'

Other_digestive_diseases = 'Other digestive diseases'

Other_genitourinary_system_diseases = 'Other genitourinary system diseases'

Other_infectious_diseases = 'Other infectious diseases'

Other_intentional_injuries = 'Other intentional injuries'

Other_intestinal_infections = 'Other intestinal infections'

Other_malignant_neoplasms = 'Other malignant neoplasms'

Other_maternal_conditions = 'Other maternal conditions'

Other_musculoskeletal_disorders = 'Other musculoskeletal disorders'

Other_neoplasms = 'Other neoplasms'

Other_neuropsychiatric_disorders = 'Other neuropsychiatric disorders'

Other_nutritional_disorders = 'Other nutritional disorders'

Other_oral_diseases = 'Other oral diseases'

Other_perinatal_conditions = 'Other perinatal conditions'

Other_respiratory_diseases = 'Other respiratory diseases'

Other_sense_organ_disorders = 'Other sense organ disorders'

Other_unintentional_injuries = 'Other unintentional injuries'

Otitis_media = 'Otitis media'

Ovary_cancer = 'Ovary cancer'

Pancreas_cancer = 'Pancreas cancer'

Pancreatitis = 'Pancreatitis'

Panic_disorder = 'Panic disorder'

Paralytic_ileus_and_intestinal_obstruction = 'Paralytic ileus and intestinal obstruction'

Parkinson_disease = 'Parkinson disease'

Peptic_ulcer_disease = 'Peptic ulcer disease'

Perinatal_conditions = 'Perinatal conditions'

Periodontal_disease = 'Periodontal disease'

Pertussis = 'Pertussis'

Poisonings = 'Poisonings'

Poliomyelitis = 'Poliomyelitis'

Post_traumatic_stress_disorder = 'Post-traumatic stress disorder'

Prostate_cancer = 'Prostate cancer'

Protein_energy_malnutrition = 'Protein-energy malnutrition'

Renal_agenesis = 'Renal agenesis'

Respiratory_diseases = 'Respiratory diseases'

Respiratory_infections = 'Respiratory infections'

Rheumatic_heart_disease = 'Rheumatic heart disease'

Rheumatoid_arthritis = 'Rheumatoid arthritis'

Road_traffic_accidents = 'Road traffic accidents'

STDs_excluding_HIV = 'STDs excluding HIV'

Schistosomiasis = 'Schistosomiasis'

Schizophrenia = 'Schizophrenia'

Self_inflicted_injuries = 'Self-inflicted injuries'

Sense_organ_diseases = 'Sense organ diseases'

Skin_diseases = 'Skin diseases'

Spina_bifida = 'Spina bifida'

Stomach_cancer = 'Stomach cancer'

Sudden_infant_death_syndrome = 'Sudden infant death syndrome'

Syphilis = 'Syphilis'

Testicular_cancer = 'Testicular cancer'

Tetanus = 'Tetanus'

Thyroid_cancer = 'Thyroid cancer'

Trachea_bronchus_lung_cancers = 'Trachea, bronchus, lung cancers'

Trachoma = 'Trachoma'

Trichuriasis = 'Trichuriasis'

Tropical_cluster_diseases = 'Tropical-cluster diseases'

Trypanosomiasis = 'Trypanosomiasis'

Tuberculosis = 'Tuberculosis'

Unintentional_injuries = 'Unintentional injuries'

Unipolar_depressive_disorders = 'Unipolar depressive disorders'

Upper_respiratory_infections = 'Upper respiratory infections'

Violence = 'Violence'

Vision_disorders_age_related = 'Vision disorders, age-related'

Vitamin_A_deficiency = 'Vitamin A deficiency'

War = 'War'

aFULL_STOP_Hodgkin_lymphoma = 'a. Hodgkin lymphoma'

aFULL_STOP_Lip_and_oral_cavity_cancers = 'a. Lip and oral cavity cancers'

aFULL_STOP_Malignant_skin_melanoma = 'a. Malignant skin melanoma'

bFULL_STOP_Nasopharynx_cancers = 'b. Nasopharynx cancers'

bFULL_STOP_Non_Hodgkin_lymphoma = 'b. Non-Hodgkin lymphoma'

bFULL_STOP_Non_melanoma_skin_cancer = 'b. Non-melanoma skin cancer'

cFULL_STOP_Multiple_myeloma = 'c. Multiple myeloma'

cFULL_STOP_Other_pharynx_cancer = 'c. Other pharynx cancer'

class bkbit.models.donor.ChemicalEntityOrGeneOrGeneProduct

Bases: ConfiguredBaseModel

A union of chemical entities and children, and gene or gene product. This mixin is helpful to use when searching across chemical entities that must include genes and their children as chemical entities.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:ChemicalEntityOrGeneOrGeneProduct', 'definition_uri': 'https://w3id.org/biolink/vocab/ChemicalEntityOrGeneOrGeneProduct', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.ConditionType(value)

Bases: str, Enum

An enumeration.

Alzheimers_disease = 'Alzheimers disease'

A progressive, neurodegenerative disease characterized by loss of function and death of nerve cells in several areas of the brain leading to loss of cognitive function such as memory and language.

ParkinsonAPOSTROPHEs_disease = "Parkinson's disease"

A progressive degenerative disorder of the central nervous system characterized by loss of dopamine producing neurons in the substantia nigra and the presence of Lewy bodies in the substantia nigra and locus coeruleus. Signs and symptoms include tremor which is most pronounced during rest, muscle rigidity, slowing of the voluntary movements, a tendency to fall back, and a mask-like facial expression.

amyotrophic_lateral_sclerosis_LEFT_PARENTHESISALSRIGHT_PARENTHESIS = 'amyotrophic lateral sclerosis (ALS)'

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscular paralysis reflecting degeneration of motor neurons in the primary motor cortex, corticospinal tracts, brainstem and spinal cord.

ataxia = 'ataxia'

A central nervous system disease that consists of gait impairment, unclear (“scanning”) speech, visual blurring due to nystagmus, hand incoordination, and tremor with movement.

autism = 'autism'

Persistent deficits in social interaction and communication and interaction as well as a markedly restricted repertoire of activity and interest as well as repetitive patterns of behavior.

bi_polar_disorder = 'bi-polar disorder'

A disorder of the brain that causes unusual shifts in mood, energy, activity levels and the ability to carry out day-to-day tasks. Often these moods range and shift from periods of elation and energized behavior to those of hopelessness and depression.

brain_aneurysm = 'brain aneurysm'

A congenital or acquired aneurysm within the cranium.

cancer = 'cancer'

A tumor composed of atypical neoplastic, often pleomorphic cells that invade other tissues. Malignant neoplasms often metastasize to distant anatomic sites and may recur after excision. The most common malignant neoplasms are carcinomas (adenocarcinomas or squamous cell carcinomas), Hodgkin and non-Hodgkin lymphomas, leukemias, melanomas, and sarcomas.

dementia = 'dementia'

Loss of intellectual abilities interfering with an individual’s social and occupational functions. Causes include Alzheimer’s disease, brain injuries, brain tumors, and vascular disorders.

depression = 'depression'

A melancholy feeling of sadness and despair.

diabetes_mellitus = 'diabetes mellitus'

A metabolic disorder characterized by abnormally high blood sugar levels due to diminished production of insulin or insulin resistance/desensitization.

dystonia = 'dystonia'

A movement disorder characterized by sustained or intermittent muscle contractions, resulting in abnormal movements and/or postures.

epilepsy = 'epilepsy'

A brain disorder characterized by episodes of abnormally increased neuronal discharge resulting in transient episodes of sensory or motor neurological dysfunction, or psychic dysfunction. These episodes may or may not be associated with loss of consciousness or convulsions.

heart_disease = 'heart disease'

A disease involving the heart and/or pericardium.

hypertension = 'hypertension'

Persistently high systemic arterial blood pressure. Based on multiple readings (blood pressure determination), hypertension is currently defined as when systolic pressure is consistently greater than 140 mm Hg or when diastolic pressure is consistently 90 mm Hg or more.

memory_loss = 'memory loss'

Systematic and extensive loss of memory caused by organic or psychological factors. The loss may be temporary or permanent, and may involve old or recent memories.

migraines = 'migraines'

A common, severe type of vascular headache often associated with increased sympathetic activity, resulting in nausea, vomiting, and light sensitivity.

multiple_sclerosis = 'multiple sclerosis'

A progressive autoimmune disorder affecting the central nervous system resulting in demyelination. Patients develop physical and cognitive impairments that correspond with the affected nerve fibers.

muscle_disease = 'muscle disease'

A disease involving the muscle tissue.

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'

schizophrenia = 'schizophrenia'

A major psychotic disorder characterized by abnormalities in the perception or expression of reality. It affects the cognitive and psychomotor functions. Common clinical signs and symptoms include delusions, hallucinations, disorganized thinking, and retreat from reality.

stroke = 'stroke'

A sudden loss of neurological function secondary to hemorrhage or ischemia in the brain parenchyma due to a vascular event. Stroke includes two main types, hemorrhagic and ischemic. Both types of stroke can cause acute seizures at the time of the acute event, as well as epilepsy as a long-term complication. In the elderly, cerebrovascular disease and stroke are the most common cause of acute seizures and epilepsy.

suicide_or_suicide_attempt = 'suicide or suicide attempt'

Any behaviour related to suicide, including suicide ideation and self-harm

tourette_syndrome = 'tourette syndrome'

A neurologic disorder caused by defective metabolism of the neurotransmitters in the brain. It is characterized by repeated involuntary movements (motor tics) and uncontrollable vocal sounds (vocal tics). The symptoms are usually manifested before the age of eighteen.

class bkbit.models.donor.ConfidenceLevel(value)

Bases: str, Enum

An enumeration.

confirmed_diagnosis = 'confirmed diagnosis'

1. make a clinical diagnosis based on medical history and neuropathological exam or genetic data or 2) make a clinical diagnosis by the consensus of expert clinicians or 3) confirm the absence of any brain-related diagnoses (i.e. Unaffected Control)

Type:

Sufficient evidence exists to

insufficient_data = 'insufficient data'

The available information is insufficient to form a reasonable impression of or to exclude any brain-related diagnoses

investigator_impression = 'investigator impression'

Diagnosis is based on the clinical impressions of the clinician reviewing the available information but that information is insufficient to render a confirmed diagnosis

class bkbit.models.donor.ConfiguredBaseModel

Bases: BaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.Dataset(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Dataset', 'biolink:Dataset']] = ['biolink:Dataset'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, license: str | None = None, rights: str | None = None, format: str | None = None, creation_date: date | None = None)

Bases: InformationContentEntity

an item that refers to a collection of data from a data source.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Dataset', 'biolink:Dataset']]

creation_date: date | None

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

format: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

license: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Dataset', 'definition_uri': 'https://w3id.org/biolink/vocab/Dataset', 'exact_mappings': ['IAO:0000100', 'dctypes:Dataset', 'schema:dataset', 'dcid:Dataset'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

rights: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.Donor(*, id: str, iri: str | None = None, category: list[Literal['https://identifiers.org/brain-bican/vocab/Donor', 'bican:Donor']] = ['bican:Donor'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, was_derived_from: str | None = None, was_generated_by: str | None = None, repository: Repository | None = None, donor_source: DonorSource | None = None, ethnicity: Ethnicity | None = None, race: Race | None = None, secondary_race: Race | None = None, sex: Sex | None = None, gender: Gender | None = None, sex_orientation: SexualOrientation | None = None, age_of_death: Annotated[int | None, Ge(ge=0), Le(le=200)] = None, birth_country_name: BirthCountry | None = None, primary_language: Language | None = None, secondary_language: Language | None = None, marital_status: MaritalStatus | None = None, education_years_number: Annotated[int | None, Ge(ge=0), Le(le=30)] = None, handedness: Handedness | None = None, confirmed_hbcac: str | None = None, age_at_death_value: float | None = None, age_at_death_unit: str | None = None, age_at_death_reference_point: str | None = None, donor_species: str | None = None, consent_status: str | None = None, positive_test_result: str | None = None, donor_project: str | None = None, donor_labs: str | None = None, death_causes_data_available: str | None = None, rins_data_available: str | None = None, rines_data_available: str | None = None, hemisphere: Hemisphere | None = None, post_mortem_interval: Annotated[float | None, Ge(ge=0), Le(le=8888)] = None, left_hemisphere_preparation: Preparation | None = None, left_hemisphere_prep_2: str | None = None, right_hemisphere_preparation: Preparation | None = None, right_hemisphere_prep_2: str | None = None, rin: Annotated[float | None, Ge(ge=0), Le(le=99)] = None, rine: Annotated[float | None, Ge(ge=0), Le(le=99)] = None, ph: Annotated[float | None, Ge(ge=0), Le(le=99)] = None, brain_weight: Annotated[int | None, Ge(ge=0), Le(le=9998)] = None, weighed_type: BrainTissueWeightType | None = None, photo_2d_available: str | None = None, scan_3d_available: str | None = None, antemortem_mri_available: str | None = None, postmortem_mri_available: str | None = None, postmortem_mri_type: PostmortemMRI | None = None, non_brain_specimen_data_available: str | None = None, neurologic_events_data_available: str | None = None, infectious_testings_data_available: str | None = None, tox_screening_data_available: str | None = None, nn_diagnoses_data_available: str | None = None, non_brain_tissue_available: str | None = None, tissue_type: Tissue | None = None, clinical_brain_diagnosis: str | None = None, clinical_brain_diagnosis_confidence_level: ConfidenceLevel | None = None, genetic_diagnosis_code: str | None = None, genetic_diagnosis_confidence_level: ConfidenceLevel | None = None, non_brain_diagnosis_code: str | None = None, non_brain_diagnosis_confidence_level: ConfidenceLevel | None = None, birth_weight_lbs: Annotated[float | None, Ge(ge=0), Le(le=100)] = None, birth_weight_oz: Annotated[float | None, Ge(ge=0), Le(le=100)] = None, apgar_5_minute_score: Annotated[int | None, Ge(ge=1), Le(le=10)] = None, apgar_10_minute_score: Annotated[int | None, Ge(ge=1), Le(le=10)] = None, neuropathology_diagnosis_code: str | None = None, artifacts_type: ArtifactType | None = None, developmental_type_specify: str | None = None, inflammatory_type_specify: str | None = None, infectious_type_specify: str | None = None, traumatic_type: TraumaticType | None = None, vascular_type: VascularType | None = None, neoplastic_type: NeoplasticType | None = None, aging_type: AgingType | None = None, neurodegenerative_type: NeurodegenerativeType | None = None)

Bases: ProvEntity, ThingWithTaxon, PhysicalEntity

A person or organism that is the source of a biological sample for scientific study. Many biological samples are generated from a single donor.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

age_at_death_reference_point: str | None

age_at_death_unit: str | None

age_at_death_value: float | None

age_of_death: int | None

aging_type: AgingType | None

antemortem_mri_available: str | None

apgar_10_minute_score: int | None

apgar_5_minute_score: int | None

artifacts_type: ArtifactType | None

birth_country_name: BirthCountry | None

birth_weight_lbs: float | None

birth_weight_oz: float | None

brain_weight: int | None

category: list[Literal['https://identifiers.org/brain-bican/vocab/Donor', 'bican:Donor']]

clinical_brain_diagnosis: str | None

clinical_brain_diagnosis_confidence_level: ConfidenceLevel | None

confirmed_hbcac: str | None

consent_status: str | None

death_causes_data_available: str | None

deprecated: bool | None

description: str | None

developmental_type_specify: str | None

donor_labs: str | None

donor_project: str | None

donor_source: DonorSource | None

donor_species: str | None

education_years_number: int | None

equivalent_identifiers: list[str] | None

ethnicity: Ethnicity | None

full_name: str | None

gender: Gender | None

genetic_diagnosis_code: str | None

genetic_diagnosis_confidence_level: ConfidenceLevel | None

handedness: Handedness | None

has_attribute: list[str] | None

hemisphere: Hemisphere | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

infectious_testings_data_available: str | None

infectious_type_specify: str | None

inflammatory_type_specify: str | None

information_content: float | None

iri: str | None

left_hemisphere_prep_2: str | None

left_hemisphere_preparation: Preparation | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'exact_mappings': ['NIMP:Donor'], 'from_schema': 'https://w3id.org/brain-bican/donor', 'mixins': ['thing with taxon', 'ProvEntity'], 'slot_usage': {'name': {'description': 'Donor identifier given by the brain bank or the institution that holds the brain', 'exact_mappings': ['bican:f8af20f7-e8b8-47b5-8a68-9ec1f913ffdf', 'NIMP:PD-QLQLFB34'], 'local_names': {'NIMP_resource_type': {'local_name_source': 'NIMP_resource_type', 'local_name_value': 'Donor'}, 'NIMP_variable_name': {'local_name_source': 'NIMP_variable_name', 'local_name_value': 'donor_local_name'}}, 'name': 'name'}, 'xref': {'description': 'NIMP generated globally unique identifier for a Donor', 'exact_mappings': ['bican:98b28574-f97f-4436-b723-10a6508431b5', 'NIMP:PD-DEXRDO36'], 'local_names': {'NIMP_resource_type': {'local_name_source': 'NIMP_resource_type', 'local_name_value': 'Donor'}, 'NIMP_variable_name': {'local_name_source': 'NIMP_variable_name', 'local_name_value': 'donor_nhash_id'}}, 'name': 'xref'}}})

marital_status: MaritalStatus | None

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

neoplastic_type: NeoplasticType | None

neurodegenerative_type: NeurodegenerativeType | None

neurologic_events_data_available: str | None

neuropathology_diagnosis_code: str | None

nn_diagnoses_data_available: str | None

non_brain_diagnosis_code: str | None

non_brain_diagnosis_confidence_level: ConfidenceLevel | None

non_brain_specimen_data_available: str | None

non_brain_tissue_available: str | None

ph: float | None

photo_2d_available: str | None

positive_test_result: str | None

post_mortem_interval: float | None

postmortem_mri_available: str | None

postmortem_mri_type: PostmortemMRI | None

primary_language: Language | None

provided_by: list[str] | None

race: Race | None

repository: Repository | None

right_hemisphere_prep_2: str | None

right_hemisphere_preparation: Preparation | None

rin: float | None

rine: float | None

rines_data_available: str | None

rins_data_available: str | None

scan_3d_available: str | None

secondary_language: Language | None

secondary_race: Race | None

sex: Sex | None

sex_orientation: SexualOrientation | None

synonym: list[str] | None

tissue_type: Tissue | None

tox_screening_data_available: str | None

traumatic_type: TraumaticType | None

type: list[str] | None

vascular_type: VascularType | None

was_derived_from: str | None

was_generated_by: str | None

weighed_type: BrainTissueWeightType | None

xref: list[str] | None

class bkbit.models.donor.DonorSource(value)

Bases: str, Enum

An enumeration.

BICAN_NBB = 'BICAN-NBB'

BICAN_UM1 = 'BICAN-UM1'

Donor_from_NIH_NeuroBioBank = 'Donor from NIH NeuroBioBank'

Other = 'Other'

class bkbit.models.donor.DrugsFound(value)

Bases: str, Enum

An enumeration.

Acetaminophen = 'Acetaminophen'

Acetone = 'Acetone'

Acetyl_Fentanyl = 'Acetyl Fentanyl'

Acrylfentanyl = 'Acrylfentanyl'

Alfentanil = 'Alfentanil'

Alpha_Hydroxyalprazolam = 'Alpha-Hydroxyalprazolam'

Alpha_Hydroxyetizolam = 'Alpha-Hydroxyetizolam'

Alprazolam = 'Alprazolam'

Amitriptyline = 'Amitriptyline'

Amlodipine = 'Amlodipine'

Amoxapine = 'Amoxapine'

Amphetamine = 'Amphetamine'

Aripiprazole = 'Aripiprazole'

Atomoxetine = 'Atomoxetine'

Atropine = 'Atropine'

Barbiturates = 'Barbiturates'

Benzodiazepines = 'Benzodiazepines'

Benzoylecgonine = 'Benzoylecgonine'

Benztropine = 'Benztropine'

Bromazepam = 'Bromazepam'

Brompheniramine = 'Brompheniramine'

Buprenorphine = 'Buprenorphine'

Buprenorphine_SOLIDUS_Metabolite = 'Buprenorphine / Metabolite'

Bupropion = 'Bupropion'

Bupropion_Metabolite = 'Bupropion Metabolite'

Buspirone = 'Buspirone'

Butalbital = 'Butalbital'

Butorphanol = 'Butorphanol'

Butylone = 'Butylone'

Butyrylfentanyl = 'Butyrylfentanyl'

Caffeine = 'Caffeine'

Cannabinoids = 'Cannabinoids'

Carbamazepine = 'Carbamazepine'

Carbamazepine_1011_Epoxide = 'Carbamazepine-10,11-Epoxide'

Carfentanil = 'Carfentanil'

Carisoprodol = 'Carisoprodol'

Chlordiazepoxide = 'Chlordiazepoxide'

Chlorpheniramine = 'Chlorpheniramine'

Chlorpromazine = 'Chlorpromazine'

Citalopram_SOLIDUS_Escitalopram = 'Citalopram / Escitalopram'

Clobazam = 'Clobazam'

Clomipramine = 'Clomipramine'

Clonazepam = 'Clonazepam'

Clonazolam = 'Clonazolam'

Clonidine = 'Clonidine'

Clozapine = 'Clozapine'

Cocaethylene = 'Cocaethylene'

Cocaine = 'Cocaine'

Cocaine_SOLIDUS_Metabolites = 'Cocaine / Metabolites'

Codeine = 'Codeine'

Cotinine = 'Cotinine'

Cyclobenzaprine = 'Cyclobenzaprine'

Cyclopropylfentanyl = 'Cyclopropylfentanyl'

Delorazepam = 'Delorazepam'

Desalkylflurazepam = 'Desalkylflurazepam'

Descarboethoxyloratadine = 'Descarboethoxyloratadine'

Desipramine = 'Desipramine'

Desmethylcitalopram = 'Desmethylcitalopram'

Desmethylclomipramine = 'Desmethylclomipramine'

Desmethyldoxepin = 'Desmethyldoxepin'

Desmethylloperamide = 'Desmethylloperamide'

Desmethylmirtazapine = 'Desmethylmirtazapine'

Desmethylsertraline = 'Desmethylsertraline'

Desmethyltrimipramine = 'Desmethyltrimipramine'

Dextro_SOLIDUS_Levo_Methorphan = 'Dextro / Levo Methorphan'

Dextrorphan_SOLIDUS_Levorphanol = 'Dextrorphan / Levorphanol'

Diazepam = 'Diazepam'

Dibutylone = 'Dibutylone'

Dicyclomine = 'Dicyclomine'

Dihydrocodeine_SOLIDUS_Hydrocodol = 'Dihydrocodeine / Hydrocodol'

Diltiazem = 'Diltiazem'

Diphenhydramine = 'Diphenhydramine'

Donepezil = 'Donepezil'

Doxepin = 'Doxepin'

Doxylamine = 'Doxylamine'

Duloxetine = 'Duloxetine'

Duloxetine_Breakdown_Product = 'Duloxetine Breakdown Product'

EDDP = 'EDDP'

Ephedrine = 'Ephedrine'

Ephedrine_SOLIDUS_Pseudoephedrine = 'Ephedrine / Pseudoephedrine'

Estazolam = 'Estazolam'

Eszopiclone_SOLIDUS_Zopiclone = 'Eszopiclone / Zopiclone'

Eszopiclone_SOLIDUS_Zopiclone_Breakdown = 'Eszopiclone / Zopiclone Breakdown'

Ethanol = 'Ethanol'

Ethinamate = 'Ethinamate'

Ethosuximide = 'Ethosuximide'

Ethotoin = 'Ethotoin'

Ethylmorphine = 'Ethylmorphine'

Etizolam = 'Etizolam'

Felbamate = 'Felbamate'

Fentanyl = 'Fentanyl'

Flecainide = 'Flecainide'

Flubromazepam = 'Flubromazepam'

Flubromazolam = 'Flubromazolam'

Fluconazole = 'Fluconazole'

Flunitrazepam = 'Flunitrazepam'

Fluoxetine = 'Fluoxetine'

Fluphenazine = 'Fluphenazine'

Flurazepam = 'Flurazepam'

Fluvoxamine = 'Fluvoxamine'

Gabapentin = 'Gabapentin'

Gabapentin_Breakdown_Product = 'Gabapentin Breakdown Product'

Guaifenesin = 'Guaifenesin'

Haloperidol = 'Haloperidol'

Hexobarbital = 'Hexobarbital'

Hydrocodone = 'Hydrocodone'

Hydromorphone = 'Hydromorphone'

Hydroxybupropion = 'Hydroxybupropion'

Hydroxychloroquine = 'Hydroxychloroquine'

Hydroxyethylflurazepam = 'Hydroxyethylflurazepam'

Hydroxytriazolam = 'Hydroxytriazolam'

Hydroxyzine = 'Hydroxyzine'

Ibuprofen = 'Ibuprofen'

Iloperidone = 'Iloperidone'

Imipramine = 'Imipramine'

Isopropanol = 'Isopropanol'

Ketamine = 'Ketamine'

LSD = 'LSD'

Lacosamide = 'Lacosamide'

Lamotrigine = 'Lamotrigine'

Levamisole = 'Levamisole'

Levetiracetam = 'Levetiracetam'

Lidocaine = 'Lidocaine'

Loperamide = 'Loperamide'

Loratadine = 'Loratadine'

Lorazepam = 'Lorazepam'

Loxapine = 'Loxapine'

MDA = 'MDA'

MDEA = 'MDEA'

MDMA = 'MDMA'

Meclizine = 'Meclizine'

Memantine = 'Memantine'

Meperidine = 'Meperidine'

Mepivacaine = 'Mepivacaine'

Meprobamate = 'Meprobamate'

Mescaline = 'Mescaline'

Mesoridazine = 'Mesoridazine'

Metaxalone = 'Metaxalone'

Methadone = 'Methadone'

Methamphetamine = 'Methamphetamine'

Methanol = 'Methanol'

Methaqualone = 'Methaqualone'

Methocarbamol = 'Methocarbamol'

Methoxetamine = 'Methoxetamine'

Methylphenidate = 'Methylphenidate'

Metoprolol = 'Metoprolol'

Metronidazole = 'Metronidazole'

Midazolam = 'Midazolam'

Milnacipran = 'Milnacipran'

Mirtazapine = 'Mirtazapine'

Mitragynine = 'Mitragynine'

Monoethylglycinexylidide_LEFT_PARENTHESISMEGXRIGHT_PARENTHESIS = 'Monoethylglycinexylidide (MEGX)'

Morphine = 'Morphine'

N_Acetylprocainamide = 'N-Acetylprocainamide'

N_ethyl_Pentylone = 'N-ethyl Pentylone'

Nalbuphine = 'Nalbuphine'

Naloxone = 'Naloxone'

Naltrexone = 'Naltrexone'

Naltrexone___Free = 'Naltrexone - Free'

Naproxen = 'Naproxen'

Naproxen_Breakdown_Product = 'Naproxen Breakdown Product'

Nicotine = 'Nicotine'

Norbuprenorphine___Free = 'Norbuprenorphine - Free'

Norclozapine = 'Norclozapine'

Norcodeine = 'Norcodeine'

Norcyclobenzaprine = 'Norcyclobenzaprine'

Nordiazepam = 'Nordiazepam'

Norfentanyl = 'Norfentanyl'

Norflunitrazepam = 'Norflunitrazepam'

Norfluoxetine = 'Norfluoxetine'

Norhydroxyzine = 'Norhydroxyzine'

Norketamine = 'Norketamine'

Normeperidine = 'Normeperidine'

Norpropoxyphene = 'Norpropoxyphene'

Norpseudoephedrine = 'Norpseudoephedrine'

Norpseudoephedrine_SOLIDUS_Phenylpropanolamine = 'Norpseudoephedrine / Phenylpropanolamine'

Nortriptyline = 'Nortriptyline'

O_Desmethyltramadol = 'O-Desmethyltramadol'

O_Desmethylvenlafaxine = 'O-Desmethylvenlafaxine'

Olanzapine = 'Olanzapine'

Ondansetron = 'Ondansetron'

Opiates = 'Opiates'

Other_Findings = 'Other Findings'

Other_findings_specify = 'Other findings, specify'

Oxazepam = 'Oxazepam'

Oxycodone = 'Oxycodone'

Oxycodone_SOLIDUS_Oxymorphone = 'Oxycodone / Oxymorphone'

Oxymorphone = 'Oxymorphone'

Papaverine = 'Papaverine'

Paroxetine = 'Paroxetine'

Pentazocine = 'Pentazocine'

Pentobarbital = 'Pentobarbital'

Perphenazine = 'Perphenazine'

Phenazepam = 'Phenazepam'

Phencyclidine = 'Phencyclidine'

Pheniramine = 'Pheniramine'

Phenobarbital = 'Phenobarbital'

Phensuximide = 'Phensuximide'

Phentermine = 'Phentermine'

Phenylethylmalonamide_LEFT_PARENTHESISPEMARIGHT_PARENTHESIS = 'Phenylethylmalonamide (PEMA)'

Phenylpropanolamine = 'Phenylpropanolamine'

Phenyltoloxamine = 'Phenyltoloxamine'

Phenytoin = 'Phenytoin'

Primidone = 'Primidone'

Procainamide = 'Procainamide'

Prochlorperazine = 'Prochlorperazine'

Promazine = 'Promazine'

Promethazine = 'Promethazine'

Propofol = 'Propofol'

Propoxyphene = 'Propoxyphene'

Protriptyline = 'Protriptyline'

Pseudoephedrine = 'Pseudoephedrine'

Psilocin = 'Psilocin'

Pyrilamine = 'Pyrilamine'

Quetiapine = 'Quetiapine'

Quinidine = 'Quinidine'

Quinine = 'Quinine'

Risperidone = 'Risperidone'

Rufinamide = 'Rufinamide'

Salicylates = 'Salicylates'

Scope_Statement = 'Scope Statement'

Secobarbital = 'Secobarbital'

Sertraline = 'Sertraline'

Sildenafil = 'Sildenafil'

Strychnine = 'Strychnine'

Sufentanil = 'Sufentanil'

Tadalafil = 'Tadalafil'

Tapentadol = 'Tapentadol'

Temazepam = 'Temazepam'

Tetrahydrozoline = 'Tetrahydrozoline'

Theobromine = 'Theobromine'

Theophylline = 'Theophylline'

Thioridazine = 'Thioridazine'

Thiothixene = 'Thiothixene'

Ticlopidine = 'Ticlopidine'

Topiramate = 'Topiramate'

Topiramate_Breakdown_Product = 'Topiramate Breakdown Product'

Tramadol = 'Tramadol'

Tramadol_Metabolite = 'Tramadol Metabolite'

Trazodone = 'Trazodone'

Triazolam = 'Triazolam'

Trifluoperazine = 'Trifluoperazine'

Trimethoprim = 'Trimethoprim'

Trimipramine = 'Trimipramine'

Triprolidine = 'Triprolidine'

U_47700 = 'U-47700'

Valerylfentanyl = 'Valerylfentanyl'

Vardenafil = 'Vardenafil'

Venlafaxine = 'Venlafaxine'

Venlafaxine_Metabolite = 'Venlafaxine Metabolite'

Verapamil = 'Verapamil'

Warfarin = 'Warfarin'

Xylazine = 'Xylazine'

Yohimbine = 'Yohimbine'

Zaleplon = 'Zaleplon'

Ziprasidone = 'Ziprasidone'

Zolpidem = 'Zolpidem'

Zonisamide = 'Zonisamide'

alpha_PVP = 'alpha-PVP'

cis_3_Methylfentanyl = 'cis-3-Methylfentanyl'

mCPP = 'mCPP'

number_10_Hydroxycarbazepine = '10-Hydroxycarbazepine'

number_2_Furanylfentanyl = '2-Furanylfentanyl'

number_4_ANPP = '4-ANPP'

number_6_Beta_Naltrexol___Free = '6-Beta-Naltrexol - Free'

number_6_Monoacetylmorphine = '6-Monoacetylmorphine'

number_7_Amino_Clonazepam = '7-Amino Clonazepam'

number_7_Amino_Flunitrazepam = '7-Amino Flunitrazepam'

number_9_Hydroxyrisperidone = '9-Hydroxyrisperidone'

para_Fluoroisobutyrylfentanyl = 'para-Fluoroisobutyrylfentanyl'

trans_3_Methylfentanyl = 'trans-3-Methylfentanyl'

class bkbit.models.donor.Entity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Entity', 'biolink:Entity']] = ['biolink:Entity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None)

Bases: ConfiguredBaseModel

Root Biolink Model class for all things and informational relationships, real or imagined.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Entity', 'biolink:Entity']]

deprecated: bool | None

description: str | None

has_attribute: list[str] | None

id: str

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'class_uri': 'biolink:Entity', 'definition_uri': 'https://w3id.org/biolink/vocab/Entity', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

type: list[str] | None

class bkbit.models.donor.Ethnicity(value)

Bases: str, Enum

An enumeration.

Hispanic_or_Latino = 'Hispanic or Latino'

A person of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin, regardless of race. The term, “Spanish origin” can be used in addition to “Hispanic or Latino”.

Not_Hispanic_or_Latino = 'Not Hispanic or Latino'

A person not of Cuban, Mexican, Puerto Rican, South or Central American, or other Spanish culture or origin, regardless of race.

Not_reported = 'Not reported'

Not provided or available.

Prefer_not_to_answer = 'Prefer not to answer'

A response indicating that an individual prefers or preferred not to answer.

Unknown = 'Unknown'

Not known, observed, recorded; or reported as unknown by the data contributor.

class bkbit.models.donor.Gender(value)

Bases: str, Enum

An enumeration.

female = 'female'

The gender identity of female self-identified by the subject/participant.

gender_fluid = 'gender fluid'

The gender identity of gender fluid self-identified by the subject/participant.

male = 'male'

The gender identity of male self-identified by the subject/participant.

non_binary = 'non-binary'

The gender identity of non-binary self-identified by the subject/participant.

not_reported = 'not reported'

Gender identity not reported.

other_specify = 'other, specify'

A gender identity of a type not included in this list.

transgender_female = 'transgender female'

The gender identity of transgender female self-identified by the subject/participant.

transgender_male = 'transgender male'

The gender identify of transgender male self-identified by the subject/participant.

unknown = 'unknown'

Not known, observed, recorded; or reported as unknown by the data contributor.

class bkbit.models.donor.Gene(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Gene', 'biolink:Gene']] = ['biolink:Gene'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, has_biological_sequence: str | None = None, symbol: str | None = None)

Bases: GeneOrGeneProduct, ChemicalEntityOrGeneOrGeneProduct, GenomicEntity, BiologicalEntity, PhysicalEssence, OntologyClass

A region (or regions) that includes all of the sequence elements necessary to encode a functional transcript. A gene locus may include regulatory regions, transcribed regions and/or other functional sequence regions.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Gene', 'biolink:Gene']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

has_biological_sequence: str | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'broad_mappings': ['NCIT:C45822'], 'class_uri': 'biolink:Gene', 'definition_uri': 'https://w3id.org/biolink/vocab/Gene', 'exact_mappings': ['SO:0000704', 'SIO:010035', 'WIKIDATA:Q7187', 'dcid:Gene'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['NCBIGene', 'ENSEMBL', 'HGNC', 'MGI', 'ZFIN', 'dictyBase', 'WB', 'WormBase', 'FB', 'RGD', 'SGD', 'PomBase', 'OMIM', 'KEGG.GENES', 'UMLS', 'Xenbase', 'AspGD', 'PHARMGKB.GENE'], 'in_subset': ['translator_minimal', 'model_organism_database'], 'mixins': ['gene or gene product', 'genomic entity', 'chemical entity or gene or gene product', 'physical essence', 'ontology class'], 'narrow_mappings': ['bioschemas:gene']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

symbol: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.GeneOrGeneProduct(*, name: str | None = None)

Bases: MacromolecularMachineMixin

A union of gene loci or gene products. Frequently an identifier for one will be used as proxy for another

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:GeneOrGeneProduct', 'definition_uri': 'https://w3id.org/biolink/vocab/GeneOrGeneProduct', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['CHEMBL.TARGET', 'IUPHAR.FAMILY'], 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class bkbit.models.donor.Genome(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Genome', 'biolink:Genome']] = ['biolink:Genome'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, in_taxon: list[str] | None = None, in_taxon_label: str | None = None, has_biological_sequence: str | None = None)

Bases: GenomicEntity, BiologicalEntity, PhysicalEssence, OntologyClass

A genome is the sum of genetic material within a cell or virion.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Genome', 'biolink:Genome']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

has_biological_sequence: str | None

id: str

in_taxon: list[str] | None

in_taxon_label: str | None

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Genome', 'close_mappings': ['dcid:GenomeAssemblyUnit'], 'definition_uri': 'https://w3id.org/biolink/vocab/Genome', 'exact_mappings': ['SO:0001026', 'SIO:000984', 'WIKIDATA:Q7020'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'in_subset': ['model_organism_database'], 'mixins': ['genomic entity', 'physical essence', 'ontology class']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.GenomicEntity(*, has_biological_sequence: str | None = None)

Bases: ConfiguredBaseModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

has_biological_sequence: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:GenomicEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/GenomicEntity', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'in_subset': ['translator_minimal'], 'mixin': True, 'narrow_mappings': ['STY:T028', 'GENO:0000897']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.Handedness(value)

Bases: str, Enum

An enumeration.

either = 'either'

Handedness where the organism exhibits no overall dominance in the use of right or left hand or foot in the performance of tasks that require one hand or foot or a dominant hand or foot.

left = 'left'

Handedness where the organism preferentially uses the left hand or foot for tasks requiring the use of a single hand or foot or a dominant hand or foot

right = 'right'

Handedness where the organism preferentially uses the right hand or foot for tasks requiring the use of a single hand or foot or a dominant hand or foot.

class bkbit.models.donor.Hemisphere(value)

Bases: str, Enum

An enumeration.

both = 'both'

Part of the telencephalon consisting of both of the two smooth, elongated halves of the cerebrum.

left = 'left'

A cerebral hemisphere that is in the left side of a brain.

right = 'right'

A cerebral hemisphere that is in the right side of a brain.

unknown = 'unknown'

class bkbit.models.donor.IncomeRange(value)

Bases: str, Enum

An enumeration.

DOLLAR_SIGN100000_and_over = '$100000 and over'

Annual income greater than 100000 USD

DOLLAR_SIGN15000_to_DOLLAR_SIGN24999 = '$15000 to $24999'

Annual income between 15000 and 24999 USD

DOLLAR_SIGN25000_to_DOLLAR_SIGN34999 = '$25000 to $34999'

Annual income between 25000 and 34999 USD

DOLLAR_SIGN35000_to_DOLLAR_SIGN49999 = '$35000 to $49999'

Annual income between 35000 and 49999 USD

DOLLAR_SIGN50000_to_DOLLAR_SIGN74999 = '$50000 to $74999'

Annual income between 50000 and 74999 USD

DOLLAR_SIGN75000_to_DOLLAR_SIGN99999 = '$75000 to $99999'

Annual income between 75000 and 99999 USD

Refused = 'Refused'

Annual income disclosure refused.

Under_DOLLAR_SIGN15000 = 'Under $15000'

Annual income under 15000 USD

Unknown = 'Unknown'

Annual income unknown.

class bkbit.models.donor.InformantRelationship(value)

Bases: str, Enum

An enumeration.

child = 'child'

A person who is either the progeny of the subject or the adoptive child of the subject.

friendSOLIDUSneighbor = 'friend/neighbor'

Either one of a friend of the subject or a person who lives near the subject.

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'

other_relative_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other relative (specify)'

parent = 'parent'

A caretaker and/or immediate progenitor of a child.

professional_caregiver = 'professional caregiver'

A person who bears the caretaker role for the subject.

sibling = 'sibling'

A person who shares a parent.

spouseSOLIDUSpartner = 'spouse/partner'

A partner in marriage.

teammate = 'teammate'

A person who is a member of a team whose membership includes the subject.

themself = 'themself'

A person who is identical to the subject, i.e., the subject themselves.

class bkbit.models.donor.InformationContentEntity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/InformationContentEntity', 'biolink:InformationContentEntity']] = ['biolink:InformationContentEntity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, license: str | None = None, rights: str | None = None, format: str | None = None, creation_date: date | None = None)

Bases: NamedThing

a piece of information that typically describes some topic of discourse or is used as support.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/InformationContentEntity', 'biolink:InformationContentEntity']]

creation_date: date | None

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

format: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

license: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'aliases': ['information', 'information artefact', 'information entity'], 'class_uri': 'biolink:InformationContentEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/InformationContentEntity', 'exact_mappings': ['IAO:0000030'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['doi'], 'narrow_mappings': ['UMLSSG:CONC', 'STY:T077', 'STY:T078', 'STY:T079', 'STY:T080', 'STY:T081', 'STY:T082', 'STY:T089', 'STY:T102', 'STY:T169', 'STY:T171', 'STY:T185']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

rights: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.Language(value)

Bases: str, Enum

An enumeration.

aar = 'aar'

Afar

abk = 'abk'

Abkhazian

ace = 'ace'

Achinese

ach = 'ach'

Acoli

ada = 'ada'

Adangme

ady = 'ady'

Adyghe; Adygei

afa = 'afa'

Afro-Asiatic languages

afh = 'afh'

Afrihili

afr = 'afr'

Afrikaans

ain = 'ain'

Ainu

aka = 'aka'

Akan

akk = 'akk'

Akkadian

alb_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'alb (B)'

Albanian

ale = 'ale'

Aleut

alg = 'alg'

Algonquian languages

alt = 'alt'

Southern Altai

amh = 'amh'

Amharic

ang = 'ang'

English, Old (ca.450-1100)

anp = 'anp'

Angika

apa = 'apa'

Apache languages

ara = 'ara'

Arabic

arc = 'arc'

Official Aramaic (700-300 BCE); Imperial Aramaic (700-300 BCE)

arg = 'arg'

Aragonese

arm_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'arm (B)'

Armenian

arn = 'arn'

Mapudungun; Mapuche

arp = 'arp'

Arapaho

art = 'art'

Artificial languages

arw = 'arw'

Arawak

asm = 'asm'

Assamese

ast = 'ast'

Asturian; Bable; Leonese; Asturleonese

ath = 'ath'

Athapascan languages

aus = 'aus'

Australian languages

ava = 'ava'

Avaric

ave = 'ave'

Avestan

awa = 'awa'

Awadhi

aym = 'aym'

Aymara

aze = 'aze'

Azerbaijani

bad = 'bad'

Banda languages

bai = 'bai'

Bamileke languages

bak = 'bak'

Bashkir

bal = 'bal'

Baluchi

bam = 'bam'

Bambara

ban = 'ban'

Balinese

baq_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'baq (B)'

Basque

bas = 'bas'

Basa

bat = 'bat'

Baltic languages

bej = 'bej'

Beja; Bedawiyet

bel = 'bel'

Belarusian

bem = 'bem'

Bemba

ben = 'ben'

Bengali

ber = 'ber'

Berber languages

bho = 'bho'

Bhojpuri

bih = 'bih'

Bihari languages

bik = 'bik'

Bikol

bin = 'bin'

Bini; Edo

bis = 'bis'

Bislama

bla = 'bla'

Siksika

bnt = 'bnt'

Bantu languages

bod_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'bod (T)'

bos = 'bos'

Bosnian

bra = 'bra'

Braj

bre = 'bre'

Breton

btk = 'btk'

Batak languages

bua = 'bua'

Buriat

bug = 'bug'

Buginese

bul = 'bul'

Bulgarian

bur_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'bur (B)'

Burmese

byn = 'byn'

Blin; Bilin

cad = 'cad'

Caddo

cai = 'cai'

Central American Indian languages

car = 'car'

Galibi Carib

cat = 'cat'

Catalan; Valencian

cau = 'cau'

Caucasian languages

ceb = 'ceb'

Cebuano

cel = 'cel'

Celtic languages

ces_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'ces (T)'

cha = 'cha'

Chamorro

chb = 'chb'

Chibcha

che = 'che'

Chechen

chg = 'chg'

Chagatai

chi_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'chi (B)'

Chinese

chk = 'chk'

Chuukese

chm = 'chm'

Mari

chn = 'chn'

Chinook jargon

cho = 'cho'

Choctaw

chp = 'chp'

Chipewyan; Dene Suline

chr = 'chr'

Cherokee

chu = 'chu'

Church Slavic; Old Slavonic; Church Slavonic; Old Bulgarian; Old Church Slavonic

chv = 'chv'

Chuvash

chy = 'chy'

Cheyenne

cmc = 'cmc'

Chamic languages

cnr = 'cnr'

Montenegrin

cop = 'cop'

Coptic

cor = 'cor'

Cornish

cos = 'cos'

Corsican

cpe = 'cpe'

Creoles and pidgins, English based

cpf = 'cpf'

Creoles and pidgins, French-based

cpp = 'cpp'

Creoles and pidgins, Portuguese-based

cre = 'cre'

Cree

crh = 'crh'

Crimean Tatar; Crimean Turkish

crp = 'crp'

Creoles and pidgins

csb = 'csb'

Kashubian

cus = 'cus'

Cushitic languages

cym_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'cym (T)'

cze_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'cze (B)'

Czech

dak = 'dak'

Dakota

dan = 'dan'

Danish

dar = 'dar'

Dargwa

day = 'day'

Land Dayak languages

del_ = 'del'

Delaware

den = 'den'

Slave (Athapascan)

deu_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'deu (T)'

dgr = 'dgr'

Dogrib

din = 'din'

Dinka

div = 'div'

Divehi; Dhivehi; Maldivian

doi = 'doi'

Dogri

dra = 'dra'

Dravidian languages

dsb = 'dsb'

Lower Sorbian

dua = 'dua'

Duala

dum = 'dum'

Dutch, Middle (ca.1050-1350)

dut_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'dut (B)'

Dutch; Flemish

dyu = 'dyu'

Dyula

dzo = 'dzo'

Dzongkha

efi = 'efi'

Efik

egy = 'egy'

Egyptian (Ancient)

eka = 'eka'

Ekajuk

ell_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'ell (T)'

elx = 'elx'

Elamite

eng = 'eng'

English

enm = 'enm'

English, Middle (1100-1500)

epo = 'epo'

Esperanto

est = 'est'

Estonian

eus_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'eus (T)'

ewe = 'ewe'

Ewe

ewo = 'ewo'

Ewondo

fan = 'fan'

Fang

fao = 'fao'

Faroese

fas_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'fas (T)'

fat = 'fat'

Fanti

fij = 'fij'

Fijian

fil = 'fil'

Filipino; Pilipino

fin = 'fin'

Finnish

fiu = 'fiu'

Finno-Ugrian languages

fon = 'fon'

Fon

fra_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'fra (T)'

fre_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'fre (B)'

French

frm = 'frm'

French, Middle (ca.1400-1600)

fro = 'fro'

French, Old (842-ca.1400)

frr = 'frr'

Northern Frisian

frs = 'frs'

Eastern Frisian

fry = 'fry'

Western Frisian

ful = 'ful'

Fulah

fur = 'fur'

Friulian

gaa = 'gaa'

Ga

gay = 'gay'

Gayo

gba = 'gba'

Gbaya

gem = 'gem'

Germanic languages

geo_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'geo (B)'

Georgian

ger_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'ger (B)'

German

gez = 'gez'

Geez

gil = 'gil'

Gilbertese

gla = 'gla'

Gaelic; Scottish Gaelic

gle = 'gle'

Irish

glg = 'glg'

Galician

glv = 'glv'

Manx

gmh = 'gmh'

German, Middle High (ca.1050-1500)

goh = 'goh'

German, Old High (ca.750-1050)

gon = 'gon'

Gondi

gor = 'gor'

Gorontalo

got = 'got'

Gothic

grb = 'grb'

Grebo

grc = 'grc'

Greek, Ancient (to 1453)

gre_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'gre (B)'

Greek, Modern (1453-)

grn = 'grn'

Guarani

gsw = 'gsw'

Swiss German; Alemannic; Alsatian

guj = 'guj'

Gujarati

gwi = 'gwi'

Gwich’in

hai = 'hai'

Haida

hat = 'hat'

Haitian; Haitian Creole

hau = 'hau'

Hausa

haw = 'haw'

Hawaiian

heb = 'heb'

Hebrew

her = 'her'

Herero

hil = 'hil'

Hiligaynon

him = 'him'

Himachali languages; Western Pahari languages

hin = 'hin'

Hindi

hit = 'hit'

Hittite

hmn = 'hmn'

Hmong; Mong

hmo = 'hmo'

Hiri Motu

hrv = 'hrv'

Croatian

hsb = 'hsb'

Upper Sorbian

hun = 'hun'

Hungarian

hup = 'hup'

Hupa

hye_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'hye (T)'

iba = 'iba'

Iban

ibo = 'ibo'

Igbo

ice_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'ice (B)'

Icelandic

ido = 'ido'

Ido

iii = 'iii'

Sichuan Yi; Nuosu

ijo = 'ijo'

Ijo languages

iku = 'iku'

Inuktitut

ile = 'ile'

Interlingue; Occidental

ilo = 'ilo'

Iloko

ina = 'ina'

Interlingua (International Auxiliary Language Association)

inc = 'inc'

Indic languages

ind = 'ind'

Indonesian

ine = 'ine'

Indo-European languages

inh = 'inh'

Ingush

ipk = 'ipk'

Inupiaq

ira = 'ira'

Iranian languages

iro = 'iro'

Iroquoian languages

isl_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'isl (T)'

ita = 'ita'

Italian

jav = 'jav'

Javanese

jbo = 'jbo'

Lojban

jpn = 'jpn'

Japanese

jpr = 'jpr'

Judeo-Persian

jrb = 'jrb'

Judeo-Arabic

kaa = 'kaa'

Kara-Kalpak

kab = 'kab'

Kabyle

kac = 'kac'

Kachin; Jingpho

kal = 'kal'

Kalaallisut; Greenlandic

kam = 'kam'

Kamba

kan = 'kan'

Kannada

kar = 'kar'

Karen languages

kas = 'kas'

Kashmiri

kat_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'kat (T)'

kau = 'kau'

Kanuri

kaw = 'kaw'

Kawi

kaz = 'kaz'

Kazakh

kbd = 'kbd'

Kabardian

kha = 'kha'

Khasi

khi = 'khi'

Khoisan languages

khm = 'khm'

Central Khmer

kho = 'kho'

Khotanese; Sakan

kik = 'kik'

Kikuyu; Gikuyu

kin = 'kin'

Kinyarwanda

kir = 'kir'

Kirghiz; Kyrgyz

kmb = 'kmb'

Kimbundu

kok = 'kok'

Konkani

kom = 'kom'

Komi

kon = 'kon'

Kongo

kor = 'kor'

Korean

kos = 'kos'

Kosraean

kpe = 'kpe'

Kpelle

krc = 'krc'

Karachay-Balkar

krl = 'krl'

Karelian

kro = 'kro'

Kru languages

kru = 'kru'

Kurukh

kua = 'kua'

Kuanyama; Kwanyama

kum = 'kum'

Kumyk

kur = 'kur'

Kurdish

kut = 'kut'

Kutenai

lad = 'lad'

Ladino

lah = 'lah'

Lahnda

lam = 'lam'

Lamba

lao = 'lao'

Lao

lat = 'lat'

Latin

lav = 'lav'

Latvian

lez = 'lez'

Lezghian

lim = 'lim'

Limburgan; Limburger; Limburgish

lin = 'lin'

Lingala

lit = 'lit'

Lithuanian

lol = 'lol'

Mongo

loz = 'loz'

Lozi

ltz = 'ltz'

Luxembourgish; Letzeburgesch

lua = 'lua'

Luba-Lulua

lub = 'lub'

Luba-Katanga

lug = 'lug'

Ganda

lui = 'lui'

Luiseno

lun = 'lun'

Lunda

luo = 'luo'

Luo (Kenya and Tanzania)

lus = 'lus'

Lushai

mac_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'mac (B)'

Macedonian

mad = 'mad'

Madurese

mag = 'mag'

Magahi

mah = 'mah'

Marshallese

mai = 'mai'

Maithili

mak = 'mak'

Makasar

mal = 'mal'

Malayalam

man = 'man'

Mandingo

mao_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'mao (B)'

Maori

map = 'map'

Austronesian languages

mar = 'mar'

Marathi

mas = 'mas'

Masai

may_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'may (B)'

Malay

mdf = 'mdf'

Moksha

mdr = 'mdr'

Mandar

men = 'men'

Mende

mga = 'mga'

Irish, Middle (900-1200)

mic = 'mic'

Mi’kmaq; Micmac

min = 'min'

Minangkabau

mis = 'mis'

Uncoded languages

mkd_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'mkd (T)'

mkh = 'mkh'

Mon-Khmer languages

mlg = 'mlg'

Malagasy

mlt = 'mlt'

Maltese

mnc = 'mnc'

Manchu

mni = 'mni'

Manipuri

mno = 'mno'

Manobo languages

moh = 'moh'

Mohawk

mon = 'mon'

Mongolian

mos = 'mos'

Mossi

mri_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'mri (T)'

msa_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'msa (T)'

mul = 'mul'

Multiple languages

mun = 'mun'

Munda languages

mus = 'mus'

Creek

mwl = 'mwl'

Mirandese

mwr = 'mwr'

Marwari

mya_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'mya (T)'

myn = 'myn'

Mayan languages

myv = 'myv'

Erzya

nah = 'nah'

Nahuatl languages

nai = 'nai'

North American Indian languages

nap = 'nap'

Neapolitan

nau = 'nau'

Nauru

nav = 'nav'

Navajo; Navaho

nbl = 'nbl'

Ndebele, South; South Ndebele

nde = 'nde'

Ndebele, North; North Ndebele

ndo = 'ndo'

Ndonga

nds = 'nds'

Low German; Low Saxon; German, Low; Saxon, Low

nep = 'nep'

Nepali

new = 'new'

Nepal Bhasa; Newari

nia = 'nia'

Nias

nic = 'nic'

Niger-Kordofanian languages

niu = 'niu'

Niuean

nld_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'nld (T)'

nno = 'nno'

Norwegian Nynorsk; Nynorsk, Norwegian

nob = 'nob'

Bokmål, Norwegian; Norwegian Bokmål

nog = 'nog'

Nogai

non = 'non'

Norse, Old

nor = 'nor'

Norwegian

nqo = 'nqo'

N’Ko

nso = 'nso'

Pedi; Sepedi; Northern Sotho

nub = 'nub'

Nubian languages

nwc = 'nwc'

Classical Newari; Old Newari; Classical Nepal Bhasa

nya = 'nya'

Chichewa; Chewa; Nyanja

nym = 'nym'

Nyamwezi

nyn = 'nyn'

Nyankole

nyo = 'nyo'

Nyoro

nzi = 'nzi'

Nzima

oci = 'oci'

Occitan (post 1500)

oji = 'oji'

Ojibwa

ori = 'ori'

Oriya

orm = 'orm'

Oromo

osa = 'osa'

Osage

oss = 'oss'

Ossetian; Ossetic

ota = 'ota'

Turkish, Ottoman (1500-1928)

oto = 'oto'

Otomian languages

paa = 'paa'

Papuan languages

pag = 'pag'

Pangasinan

pal = 'pal'

Pahlavi

pam = 'pam'

Pampanga; Kapampangan

pan = 'pan'

Panjabi; Punjabi

pap = 'pap'

Papiamento

pau = 'pau'

Palauan

peo = 'peo'

Persian, Old (ca.600-400 B.C.)

per_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'per (B)'

Persian

phi = 'phi'

Philippine languages

phn = 'phn'

Phoenician

pli = 'pli'

Pali

pol = 'pol'

Polish

pon = 'pon'

Pohnpeian

por = 'por'

Portuguese

pra = 'pra'

Prakrit languages

pro = 'pro'

Provençal, Old (to 1500);Occitan, Old (to 1500)

pus = 'pus'

Pushto; Pashto

qaa_qtz = 'qaa-qtz'

Reserved for local use

que = 'que'

Quechua

raj = 'raj'

Rajasthani

rap = 'rap'

Rapanui

rar = 'rar'

Rarotongan; Cook Islands Maori

roa = 'roa'

Romance languages

roh = 'roh'

Romansh

rom = 'rom'

Romany

ron_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'ron (T)'

rum_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'rum (B)'

Romanian; Moldavian; Moldovan

run = 'run'

Rundi

rup = 'rup'

Aromanian; Arumanian; Macedo-Romanian

rus = 'rus'

Russian

sad = 'sad'

Sandawe

sag = 'sag'

Sango

sah = 'sah'

Yakut

sai = 'sai'

South American Indian languages

sal = 'sal'

Salishan languages

sam = 'sam'

Samaritan Aramaic

san = 'san'

Sanskrit

sas = 'sas'

Sasak

sat = 'sat'

Santali

scn = 'scn'

Sicilian

sco = 'sco'

Scots

sel = 'sel'

Selkup

sem = 'sem'

Semitic languages

sga = 'sga'

Irish, Old (to 900)

sgn = 'sgn'

Sign Languages

shn = 'shn'

Shan

sid = 'sid'

Sidamo

sin = 'sin'

Sinhala; Sinhalese

sio = 'sio'

Siouan languages

sit = 'sit'

Sino-Tibetan languages

sla = 'sla'

Slavic languages

slk_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'slk (T)'

slo_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'slo (B)'

Slovak

slv = 'slv'

Slovenian

sma = 'sma'

Southern Sami

sme = 'sme'

Northern Sami

smi = 'smi'

Sami languages

smj = 'smj'

Lule Sami

smn = 'smn'

Inari Sami

smo = 'smo'

Samoan

sms = 'sms'

Skolt Sami

sna = 'sna'

Shona

snd = 'snd'

Sindhi

snk = 'snk'

Soninke

sog = 'sog'

Sogdian

som = 'som'

Somali

son = 'son'

Songhai languages

sot = 'sot'

Sotho, Southern

spa = 'spa'

Spanish; Castilian

sqi_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'sqi (T)'

srd = 'srd'

Sardinian

srn = 'srn'

Sranan Tongo

srp = 'srp'

Serbian

srr = 'srr'

Serer

ssa = 'ssa'

Nilo-Saharan languages

ssw = 'ssw'

Swati

suk = 'suk'

Sukuma

sun = 'sun'

Sundanese

sus = 'sus'

Susu

sux = 'sux'

Sumerian

swa = 'swa'

Swahili

swe = 'swe'

Swedish

syc = 'syc'

Classical Syriac

syr = 'syr'

Syriac

tah = 'tah'

Tahitian

tai = 'tai'

Tai languages

tam = 'tam'

Tamil

tat = 'tat'

Tatar

tel = 'tel'

Telugu

tem = 'tem'

Timne

ter = 'ter'

Tereno

tet = 'tet'

Tetum

tgk = 'tgk'

Tajik

tgl = 'tgl'

Tagalog

tha = 'tha'

Thai

tib_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'tib (B)'

Tibetan

tig = 'tig'

Tigre

tir = 'tir'

Tigrinya

tiv = 'tiv'

Tiv

tkl = 'tkl'

Tokelau

tlh = 'tlh'

Klingon; tlhIngan-Hol

tli = 'tli'

Tlingit

tmh = 'tmh'

Tamashek

tog = 'tog'

Tonga (Nyasa)

ton = 'ton'

Tonga (Tonga Islands)

tpi = 'tpi'

Tok Pisin

tsi = 'tsi'

Tsimshian

tsn = 'tsn'

Tswana

tso = 'tso'

Tsonga

tuk = 'tuk'

Turkmen

tum = 'tum'

Tumbuka

tup = 'tup'

Tupi languages

tur = 'tur'

Turkish

tut = 'tut'

Altaic languages

tvl = 'tvl'

Tuvalu

twi = 'twi'

Twi

tyv = 'tyv'

Tuvinian

udm = 'udm'

Udmurt

uga = 'uga'

Ugaritic

uig = 'uig'

Uighur; Uyghur

ukr = 'ukr'

Ukrainian

umb = 'umb'

Umbundu

und = 'und'

Undetermined

urd = 'urd'

Urdu

uzb = 'uzb'

Uzbek

vai = 'vai'

Vai

ven = 'ven'

Venda

vie = 'vie'

Vietnamese

vol = 'vol'

Volapük

vot = 'vot'

Votic

wak = 'wak'

Wakashan languages

wal = 'wal'

Wolaitta; Wolaytta

war = 'war'

Waray

was = 'was'

Washo

wel_LEFT_PARENTHESISBRIGHT_PARENTHESIS = 'wel (B)'

Welsh

wen = 'wen'

Sorbian languages

wln = 'wln'

Walloon

wol = 'wol'

Wolof

xal = 'xal'

Kalmyk; Oirat

xho = 'xho'

Xhosa

yao = 'yao'

Yao

yap = 'yap'

Yapese

yid = 'yid'

Yiddish

yor = 'yor'

Yoruba

ypk = 'ypk'

Yupik languages

zap = 'zap'

Zapotec

zbl = 'zbl'

Blissymbols; Blissymbolics; Bliss

zen = 'zen'

Zenaga

zgh = 'zgh'

Standard Moroccan Tamazight

zha = 'zha'

Zhuang; Chuang

zho_LEFT_PARENTHESISTRIGHT_PARENTHESIS = 'zho (T)'

znd = 'znd'

Zande languages

zul = 'zul'

Zulu

zun = 'zun'

Zuni

zxx = 'zxx'

No linguistic content; Not applicable

zza = 'zza'

Zaza; Dimili; Dimli; Kirdki; Kirmanjki; Zazaki

class bkbit.models.donor.LinkMLMeta(root: RootModelRootType = PydanticUndefined)

Bases: RootModel

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

model_config: ClassVar[ConfigDict] = {'frozen': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

root: dict[str, Any]

class bkbit.models.donor.MacromolecularMachineMixin(*, name: str | None = None)

Bases: ConfiguredBaseModel

A union of gene locus, gene product, and macromolecular complex. These are the basic units of function in a cell. They either carry out individual biological activities, or they encode molecules which do this.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:MacromolecularMachineMixin', 'definition_uri': 'https://w3id.org/biolink/vocab/MacromolecularMachineMixin', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True, 'slot_usage': {'name': {'description': 'genes are typically designated by a short symbol and a full name. We map the symbol to the default display name and use an additional slot for full name', 'name': 'name', 'range': 'symbol type'}}})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class bkbit.models.donor.MannerOfDeath(value)

Bases: str, Enum

An enumeration.

accidental = 'accidental'

A manner of death in which death is caused by inadvertent chance happening(s).

homicide = 'homicide'

A manner of death in which death is caused by a person’s intentional action to kill or harm another.

manner_of_death_undetermined = 'manner of death undetermined'

A manner of death where little information is known about the circumstances surrounding death or where information equally supports and/or conflicts with more than one manner of death.

natural = 'natural'

A manner of death in which death is caused solely by disease or natural process.

not_deceased = 'not deceased'

suicide = 'suicide'

A manner of death in which death is caused by self-inflicted injurty with evidence of intent to die.

unknown_if_subject_is_deceased_or_not = 'unknown if subject is deceased or not'

class bkbit.models.donor.MaritalStatus(value)

Bases: str, Enum

An enumeration.

divorced = 'divorced'

A person whose marriage has been legally dissolved and has not remarried.

domestic_partnership = 'domestic partnership'

A person who is a member of an unmarried couple, including same sex couples, living together in longstanding relationships, that are registered or unregistered.

married = 'married'

A person currently joined in matrimony. Classify common law marriage as married. Includes married couples living together and not living together.

never_married = 'never married'

A person who has never been married or whose only marriages have been annulled.

separated = 'separated'

A person living apart from his/her spouse by legal arrangement.

widowed = 'widowed'

A person who is no longer married because of the death of his/her spouse and who has not remarried.

class bkbit.models.donor.MaterialSample(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/MaterialSample', 'biolink:MaterialSample']] = ['biolink:MaterialSample'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None)

Bases: SubjectOfInvestigation, PhysicalEntity

A sample is a limited quantity of something (e.g. an individual or set of individuals from a population, or a portion of a substance) to be used for testing, analysis, inspection, investigation, demonstration, or trial use. [SIO]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/MaterialSample', 'biolink:MaterialSample']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'aliases': ['biospecimen', 'sample', 'biosample', 'physical sample'], 'class_uri': 'biolink:MaterialSample', 'definition_uri': 'https://w3id.org/biolink/vocab/MaterialSample', 'exact_mappings': ['OBI:0000747', 'SIO:001050'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['BIOSAMPLE', 'GOLD.META'], 'mixins': ['subject of investigation']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.NamedThing(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/NamedThing', 'biolink:NamedThing']] = ['biolink:NamedThing'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None)

Bases: Entity

a databased entity or concept/class

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/NamedThing', 'biolink:NamedThing']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:NamedThing', 'definition_uri': 'https://w3id.org/biolink/vocab/NamedThing', 'exact_mappings': ['BFO:0000001', 'WIKIDATA:Q35120', 'UMLSSG:OBJC', 'STY:T071', 'dcid:Thing'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'slot_usage': {'category': {'name': 'category', 'required': True}}})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.NeoplasticType(value)

Bases: str, Enum

An enumeration.

intrinsic = 'intrinsic'

A disposition inhering in a tumour by virtue of the bearer’s disposition not to invade surrounding tissues.

metastasis = 'metastasis'

A disposition inhering in a tumour by virtue of the bearer’s disposition to spread and invade distant tissues.

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'

A structural quality which is held by a bearer when the latter’s disposition the presence of abnormally proliferating masses of cells.

class bkbit.models.donor.NeurodegenerativeType(value)

Bases: str, Enum

An enumeration.

Other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'Other (specify)'

alpha_synuclein = 'alpha-synuclein'

1 ortholog thereof.

Type:

A protein that is a translation product of the human SNCA gene or a 1

beta_amyloid = 'beta amyloid'

A peptide of 36-43 amino acids that is processed from the amyloid precursor protein. Appears to be the main constituent of amyloid plaques (deposits found in the brains of Alzheimer’s disease patients).

pTDP_43 = 'pTDP-43'

1 ortholog thereof.

Type:

A protein that is a translation product of the human TARDBP gene or a 1

pTau = 'pTau'

A microtubule-associated protein tau that includes at least one phosphorylated residue.

class bkbit.models.donor.Occurrent

Bases: PhysicalEssenceOrOccurrent

A processual entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Occurrent', 'definition_uri': 'https://w3id.org/biolink/vocab/Occurrent', 'exact_mappings': ['BFO:0000003'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.OntologyClass(*, id: str)

Bases: ConfiguredBaseModel

a concept or class in an ontology, vocabulary or thesaurus. Note that nodes in a biolink compatible KG can be considered both instances of biolink classes, and OWL classes in their own right. In general you should not need to use this class directly. Instead, use the appropriate biolink class. For example, for the GO concept of endocytosis (GO:0006897), use bl:BiologicalProcess as the type.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

id: str

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:OntologyClass', 'comments': ["This is modeled as a mixin. 'ontology class' should not be the primary type of a node in the KG. Instead you should use an informative bioloink category, such as AnatomicalEntity (for Uberon classes), ChemicalSubstance (for CHEBI or CHEMBL), etc", "Note that formally this is a metaclass. Instances of this class are instances in the graph, but can be the object of 'type' edges. For example, if we had a node in the graph representing a specific brain of a specific patient (e.g brain001), this could have a category of bl:Sample, and by typed more specifically with an ontology class UBERON:nnn, which has as category bl:AnatomicalEntity"], 'definition_uri': 'https://w3id.org/biolink/vocab/OntologyClass', 'exact_mappings': ['owl:Class', 'schema:Class'], 'examples': [{'description': "the class 'brain' from the Uberon anatomy ontology", 'value': 'UBERON:0000955'}], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['MESH', 'UMLS', 'KEGG.BRITE'], 'mixin': True, 'see_also': ['https://github.com/biolink/biolink-model/issues/486']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.OrganismTaxon(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/OrganismTaxon', 'biolink:OrganismTaxon']] = ['biolink:OrganismTaxon'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, has_taxonomic_rank: str | None = None, exact_match: list[str] | None = None)

Bases: NamedThing

A classification of a set of organisms. Example instances: NCBITaxon:9606 (Homo sapiens), NCBITaxon:2 (Bacteria). Can also be used to represent strains or subspecies.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/OrganismTaxon', 'biolink:OrganismTaxon']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

exact_match: list[str] | None

full_name: str | None

has_attribute: list[str] | None

has_taxonomic_rank: str | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'aliases': ['taxon', 'taxonomic classification'], 'class_uri': 'biolink:OrganismTaxon', 'definition_uri': 'https://w3id.org/biolink/vocab/OrganismTaxon', 'exact_mappings': ['WIKIDATA:Q16521', 'STY:T001', 'bioschemas:Taxon'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['NCBITaxon', 'MESH', 'UMLS'], 'in_subset': ['model_organism_database'], 'narrow_mappings': ['dcid:BiologicalSpecies'], 'slot_usage': {'has taxonomic rank': {'mappings': ['WIKIDATA:P105'], 'multivalued': False, 'name': 'has taxonomic rank', 'range': 'taxonomic rank'}}, 'values_from': ['NCBITaxon']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.PerinatalEvent(value)

Bases: str, Enum

An enumeration.

hypoxia = 'hypoxia'

Reduced oxygenation of body tissues resulting in the decreased pressure of this component of body gases; commonly due to hypoxemia.

intraparenchymal_hemorrhage = 'intraparenchymal hemorrhage'

Bleeding into the intraparenchymal space.

intraventricular_hemorrhage = 'intraventricular hemorrhage'

Bleeding into the brain ventricles.

none = 'none'

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'

subarachnoid_hemorrhage = 'subarachnoid hemorrhage'

Intracranial hemorrhage into the subarachnoid space.

subdural_hemorrhage = 'subdural hemorrhage'

Bleeding into the subdural space, the potential space between the dura and arachnoid mater of the meninges around the brain.

class bkbit.models.donor.PhysicalEntity(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/PhysicalEntity', 'biolink:PhysicalEntity']] = ['biolink:PhysicalEntity'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None)

Bases: PhysicalEssence, NamedThing

An entity that has material reality (a.k.a. physical essence).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/PhysicalEntity', 'biolink:PhysicalEntity']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:PhysicalEntity', 'definition_uri': 'https://w3id.org/biolink/vocab/PhysicalEntity', 'exact_mappings': ['STY:T072'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixins': ['physical essence'], 'narrow_mappings': ['STY:T073']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.PhysicalEssence

Bases: PhysicalEssenceOrOccurrent

Semantic mixin concept. Pertains to entities that have physical properties such as mass, volume, or charge.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:PhysicalEssence', 'definition_uri': 'https://w3id.org/biolink/vocab/PhysicalEssence', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.PhysicalEssenceOrOccurrent

Bases: ConfiguredBaseModel

Either a physical or processual entity.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:PhysicalEssenceOrOccurrent', 'definition_uri': 'https://w3id.org/biolink/vocab/PhysicalEssenceOrOccurrent', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.PostmortemMRI(value)

Bases: str, Enum

An enumeration.

cadaveric = 'cadaveric'

A piece of tissue or a sample that is taken from a dead body (cadaver).

fixed_ex_vivo = 'fixed ex vivo'

A piece of tissue or a sample that is taken from a living organism and fixed.

fresh_ex_vivo = 'fresh ex vivo'

A piece of tissue or a sample that is taken from a living organism and not fixed.

class bkbit.models.donor.Preparation(value)

Bases: str, Enum

An enumeration.

formalin_fixed = 'formalin fixed'

A tissue sample that has been fixed with formalin.

frozen = 'frozen'

A tissue sample that has been frozen.

hemisphere_not_available = 'hemisphere not available'

number_4PERCENT_SIGN_paraformaldehyde_fixed = '4% paraformaldehyde fixed'

A tissue sample that has been fixed with 4% paraformaldehyde.

other = 'other'

A tissue sample that has been fixed by some other method.

class bkbit.models.donor.Procedure(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/Procedure', 'biolink:Procedure']] = ['biolink:Procedure'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None)

Bases: ActivityAndBehavior, NamedThing

A series of actions conducted in a certain order or manner

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/Procedure', 'biolink:Procedure']]

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:Procedure', 'definition_uri': 'https://w3id.org/biolink/vocab/Procedure', 'exact_mappings': ['UMLSSG:PROC', 'dcid:MedicalProcedure'], 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['CPT'], 'mixins': ['activity and behavior'], 'narrow_mappings': ['STY:T059', 'STY:T060', 'STY:T061', 'STY:T063']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.ProvActivity(*, used: str | None = None)

Bases: ConfiguredBaseModel

An activity is something that occurs over a period of time and acts upon or with entities; it may include consuming, processing, transforming, modifying, relocating, using, or generating entities.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'prov:Activity', 'from_schema': 'https://identifiers.org/brain-bican/bican-prov-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

used: str | None

class bkbit.models.donor.ProvEntity(*, was_derived_from: str | None = None, was_generated_by: str | None = None)

Bases: ConfiguredBaseModel

An entity is a physical, digital, conceptual, or other kind of thing with some fixed aspects; entities may be real or imaginary.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'prov:Entity', 'from_schema': 'https://identifiers.org/brain-bican/bican-prov-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

was_derived_from: str | None

was_generated_by: str | None

class bkbit.models.donor.QuantityValue(*, has_unit: str | None = None, has_numeric_value: float | None = None)

Bases: Annotation

A value of an attribute that is quantitative and measurable, expressed as a combination of a unit and a numeric value

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

has_numeric_value: float | None

has_unit: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:QuantityValue', 'definition_uri': 'https://w3id.org/biolink/vocab/QuantityValue', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.Race(value)

Bases: str, Enum

An enumeration.

American_Indian_or_Alaska_Native = 'American Indian or Alaska Native'

A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.

Asian = 'Asian'

A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent, including for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.

Black_or_African_American = 'Black or African American'

A person having origins in any of the Black racial groups of Africa. Terms such as “Haitian” or “Negro” can be used in addition to “Black or African American”.

More_than_one_race = 'More than one race'

Having ancestors of several or various races.

Native_Hawaiian_or_Other_Pacific_Islander = 'Native Hawaiian or Other Pacific Islander'

A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.

Other_Race = 'Other Race'

Individuals who do not necessarily identify with any particular race or others who do not wish to self select into a racial category(ies).

Prefer_not_to_Answer = 'Prefer not to Answer'

A response indicating that an individual prefers or preferred not to answer.

Unknown = 'Unknown'

Not known, observed, recorded; or reported as unknown by the data contributor.

White = 'White'

A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.

class bkbit.models.donor.RelativeFrequencyAnalysisResult(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/RelativeFrequencyAnalysisResult', 'biolink:RelativeFrequencyAnalysisResult']] = ['biolink:RelativeFrequencyAnalysisResult'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, license: str | None = None, rights: str | None = None, format: str | None = None, creation_date: date | None = None)

Bases: StudyResult

A result of a relative frequency analysis.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/RelativeFrequencyAnalysisResult', 'biolink:RelativeFrequencyAnalysisResult']]

creation_date: date | None

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

format: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

license: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:RelativeFrequencyAnalysisResult', 'definition_uri': 'https://w3id.org/biolink/vocab/RelativeFrequencyAnalysisResult', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema'})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

rights: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.RelativeType(value)

Bases: str, Enum

An enumeration.

biological_father = 'biological father'

A male who contributes to the genetic makeup of his offspring through the fertilization of an ovum by his sperm.

biological_mother = 'biological mother'

A female who contributes to the genetic makeup of her offspring from the fertilization of her ovum.

child = 'child'

A first-generation, biological descendant of any age.

full_sibling = 'full sibling'

A sibling with whom one shares both parents.

grandchild = 'grandchild'

A child of one’s son or daughter.

great_grandchild = 'great-grandchild'

A child of one’s grandchild.

half_sibling = 'half sibling'

A sibling with whom one shares a single parent.

maternal_aunt = 'maternal aunt'

A female relative who is a sibling of the biological mother, and who both share a common ancestor.

maternal_cousin = 'maternal cousin'

A relative who is the offspring of a sibling of the biological mother and thus sharing a common ancestor.

maternal_grandfather = 'maternal grandfather'

A male relative who is the biological father of the biological mother.

maternal_grandmother = 'maternal grandmother'

A female relative who is the biological mother of the biological mother.

maternal_nieceSOLIDUSnephew = 'maternal niece/nephew'

A son or daughter of one’s female biological sibling.

maternal_uncle = 'maternal uncle'

A male relative who is a sibling of the biological mother, and who both share a common ancestor.

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'

paternal_aunt = 'paternal aunt'

A female relative who is a sibling of the biological father, and who both share a common ancestor.

paternal_cousin = 'paternal cousin'

A relative who is the offspring of a sibling of the biological father and thus sharing a common ancestor.

paternal_grandfather = 'paternal grandfather'

A male relative who is the biological father of the biological father.

paternal_grandmother = 'paternal grandmother'

A female relative who is the biological mother of the biological father.

paternal_nieceSOLIDUSnephew = 'paternal niece/nephew'

A son or daughter of one’s male biological sibling.

paternal_uncle = 'paternal uncle'

A male relative who is a sibling of the biological father, and who both share a common ancestor.

class bkbit.models.donor.Repository(value)

Bases: str, Enum

An enumeration.

HBCC = 'HBCC'

Harvard = 'Harvard'

Maryland = 'Maryland'

Maryland_Psychiatric = 'Maryland Psychiatric'

Miami = 'Miami'

MtFULL_STOP_Sinai = 'Mt. Sinai'

Pittsburgh = 'Pittsburgh'

Sepulveda = 'Sepulveda'

TBD = 'TBD'

UCLA = 'UCLA'

class bkbit.models.donor.Sex(value)

Bases: str, Enum

An enumeration.

female = 'female'

A person who belongs to the sex that normally produces ova. The term is used to indicate biological sex distinctions, or cultural gender role distinctions, or both.

intersex = 'intersex'

A person (one of unisexual specimens) who is born with genitalia and/or secondary sexual characteristics of indeterminate sex, or which combine features of both sexes.

male = 'male'

A person who belongs to the sex that normally produces sperm. The term is used to indicate biological sex distinctions, cultural gender role distinctions, or both.

none_of_these_describe_me = 'none of these describe me'

A response indicating that an individual feels that no option listed is a good description for them.

Type:

Suggested Definition

prefer_not_to_answer = 'prefer not to answer'

A response indicating that an individual prefers or preferred not to answer.

unknown = 'unknown'

Not known, observed, recorded; or reported as unknown by the data contributor.

class bkbit.models.donor.SexualOrientation(value)

Bases: str, Enum

An enumeration.

asexual = 'asexual'

A person who identifies as asexual.

bisexual = 'bisexual'

Historically, bisexual referred to attraction to persons of two gender identities, typically, but not always, defined as male and female. In modern use, it is usually used to refer to persons who are sexually and/or romantically attracted to persons with a gender or genders like theirs and those with a gender or genders unlike theirs. Note that sometimes bisexual may be used as a synonym of pansexual.

do_not_know = 'do not know'

heterosexual = 'heterosexual'

Sexually attracted to a person defined as being of the “opposite” sex (i.e. male attracted to female or female attracted to male); more broadly, attracted to a gender that is not one’s own.

homosexual_LEFT_PARENTHESISgaySOLIDUSlesbianRIGHT_PARENTHESIS = 'homosexual (gay/lesbian)'

A person who is attracted solely or primarily to others of the same gender (or sex, depending on precise usage), i.e. being either a male androphile or a female gynephile.

not_reported = 'not reported'

Not provided or available.

other = 'other'

A sexual orientation that is not present in this list.

prefer_not_to_say = 'prefer not to say'

unknown = 'unknown'

Not known, observed, recorded; or reported as unknown by the data contributor.

class bkbit.models.donor.StudyResult(*, id: str, iri: str | None = None, category: list[Literal['https://w3id.org/biolink/vocab/StudyResult', 'biolink:StudyResult']] = ['biolink:StudyResult'], type: list[str] | None = None, name: str | None = None, description: str | None = None, has_attribute: list[str] | None = None, deprecated: bool | None = None, provided_by: list[str] | None = None, xref: list[str] | None = None, full_name: str | None = None, synonym: list[str] | None = None, information_content: float | None = None, equivalent_identifiers: list[str] | None = None, license: str | None = None, rights: str | None = None, format: str | None = None, creation_date: date | None = None)

Bases: InformationContentEntity

A collection of data items from a study that are about a particular study subject or experimental unit (the ‘focus’ of the Result) - optionally with context/provenance metadata that may be relevant to the interpretation of this data as evidence.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

category: list[Literal['https://w3id.org/biolink/vocab/StudyResult', 'biolink:StudyResult']]

creation_date: date | None

deprecated: bool | None

description: str | None

equivalent_identifiers: list[str] | None

format: str | None

full_name: str | None

has_attribute: list[str] | None

id: str

information_content: float | None

iri: str | None

license: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'abstract': True, 'class_uri': 'biolink:StudyResult', 'definition_uri': 'https://w3id.org/biolink/vocab/StudyResult', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'notes': ["The data/metadata included in a Study Result object are typically a subset of data from a larger study data set, that are selected by a curator because they may be useful as evidence for deriving knowledge about a specific focus of the study. The notion of a 'study' here is defined broadly to include any research activity at any scale that is aimed at generating knowledge or hypotheses. This may include a single assay or computational analyses, or a larger scale clinical trial or experimental research investigation."]})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

provided_by: list[str] | None

rights: str | None

synonym: list[str] | None

type: list[str] | None

xref: list[str] | None

class bkbit.models.donor.SubjectOfInvestigation

Bases: ConfiguredBaseModel

An entity that has the role of being studied in an investigation, study, or experiment

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:SubjectOfInvestigation', 'definition_uri': 'https://w3id.org/biolink/vocab/SubjectOfInvestigation', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.TaxonomicRank(*, id: str)

Bases: OntologyClass

A descriptor for the rank within a taxonomic classification. Example instance: TAXRANK:0000017 (kingdom)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

id: str

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:TaxonomicRank', 'definition_uri': 'https://w3id.org/biolink/vocab/TaxonomicRank', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'id_prefixes': ['TAXRANK'], 'mappings': ['WIKIDATA:Q427626']})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.TestName(value)

Bases: str, Enum

An enumeration.

COVID_19_Virus_LEFT_PARENTHESISRT_PCRRIGHT_PARENTHESIS = 'COVID-19 Virus (RT-PCR)'

HIV_1SOLIDUSHIV_2 = 'HIV-1/HIV-2'

HIV_1SOLIDUSHIV_2_Plus_O_Antibody = 'HIV-1/HIV-2 Plus O Antibody'

HIV_1_Antibody = 'HIV-1 Antibody'

Hepatitis_A = 'Hepatitis A'

Hepatitis_B_Core_Antibody = 'Hepatitis B Core Antibody'

Hepatitis_B_Surface_Antibody = 'Hepatitis B Surface Antibody'

Hepatitis_B_Surface_Antigen = 'Hepatitis B Surface Antigen'

Hepatitis_C_Antibody = 'Hepatitis C Antibody'

Other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'Other (specify)'

class bkbit.models.donor.ThingWithTaxon(*, in_taxon: list[str] | None = None, in_taxon_label: str | None = None)

Bases: ConfiguredBaseModel

A mixin that can be used on any entity that can be taxonomically classified. This includes individual organisms; genes, their products and other molecular entities; body parts; biological processes

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

in_taxon: list[str] | None

in_taxon_label: str | None

linkml_meta: ClassVar[LinkMLMeta] = LinkMLMeta(root={'class_uri': 'biolink:ThingWithTaxon', 'definition_uri': 'https://w3id.org/biolink/vocab/ThingWithTaxon', 'from_schema': 'https://w3id.org/biolink/bican-biolink-schema', 'mixin': True})

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': True, 'extra': 'forbid', 'serialize_by_alias': True, 'strict': False, 'use_enum_values': True, 'validate_assignment': True, 'validate_by_name': True, 'validate_default': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class bkbit.models.donor.Tissue(value)

Bases: str, Enum

An enumeration.

CSF = 'CSF'

A clear, colorless, bodily fluid, that occupies the subarachnoid space and the ventricular system around and inside the brain and spinal cord.

DNA = 'DNA'

A long linear double-stranded polymer formed from nucleotides attached to a deoxyribose backbone and found in the nucleus of a cell; associated with the transmission of genetic information.

DRG_cervical = 'DRG cervical'

A group of nerve cell bodies located on the dorsal spinal roots within the vertebral column at the level of the cervical vertebrae.

DRG_lumbar = 'DRG lumbar'

The group of nerve cell bodies located on the dorsal spinal roots within the vertebral column at the level of the lumbar vertebrae.

DRG_sacral = 'DRG sacral'

A group of nerve cell bodies located on the dorsal spinal roots within the vertebral column at the level of the sacral vertebrae.

DRG_thoracic = 'DRG thoracic'

A dorsal root ganglion that is part of a thorax.

RBC = 'RBC'

A red blood cell. In mammals, mature erythrocytes are biconcave disks containing hemoglobin whose function is to transport oxygen.

RNA = 'RNA'

High molecular weight, linear polymers, composed of nucleotides containing ribose and linked by phosphodiester bonds; RNA is central to the synthesis of proteins.

adipose = 'adipose'

Portion of connective tissue composed of adipocytes enmeshed in areolar tissue.

adrenal = 'adrenal'

Tissue that is part of some adrenal gland.

anus = 'anus'

matter which the animal cannot digest, such as bones; food material after all the nutrients have been extracted, for example cellulose or lignin; ingested matter which would be toxic if it remained in the digestive tract; and dead or excess gut bacteria and other endosymbionts.

Type:

Orifice at the opposite end of an animal’s digestive tract from the mouth. Its function is to expel feces, unwanted semi-solid matter produced during digestion, which, depending on the type of animal, may be one or more of

aorta = 'aorta'

The main trunk of the systemic arterial system that carries blood from the heart to all the organs and other structures of the body, bringing oxygenated blood to all parts of the body in the systemic circulation.

appendix = 'appendix'

A blind-ended tube connected to the cecum, from which it develops embryologically.

artery = 'artery'

An epithelial tube or tree of tibes that transports blood away from the heart.

ascending_colon = 'ascending colon'

Section of colon which is distal to the cecum and proximal to the transversecolon.

astrocytes = 'astrocytes'

A class of large neuroglial (macroglial) cells in the central nervous system - the largest and most numerous neuroglial cells in the brain and spinal cord. Astrocytes (from ‘star’ cells) are irregularly shaped with many long processes, including those with ‘end feet’ which form the glial (limiting) membrane and directly and indirectly contribute to the blood-brain barrier. They regulate the extracellular ionic and chemical environment, and ‘reactive astrocytes’ (along with microglia) respond to injury. Astrocytes are reportedly CD68-negative, CD121a-positive, CD184-positive, CD192-positive, CRF-positive, EGFR-positive, GFAP-positive, GLUT1-positive, MBP-negative, and NGFR-positive.

bladder = 'bladder'

Distensible musculomembranous organ situated in the anterior part of the pelvic cavity in which urine collects before excretion.

bone_rib = 'bone rib'

An intersegmental rod-shaped bone that forms in the peritoneal membrane and attach to the vertebral parapophyses.

breast = 'breast'

The upper ventral region of the torso of an organism.

bronchus = 'bronchus'

The upper conducting airways of the lung; these airways arise from the terminus of the trachea.

cervical_spinal_cord = 'cervical spinal cord'

A spinal cord segment that adjacent_to a cervical region.

colon_ascending = 'colon ascending'

Section of colon which is distal to the cecum and proximal to the transversecolon.

colon_descending = 'colon descending'

The portion of the colon between the left colic flexure and the sigmoid colon at the pelvic brim; the portion of the descending colon lying in the left iliac fossa is sometimes called the iliac colon.

diaphragm = 'diaphragm'

A thin musculomebranous barrier that separates the abdominal and thoracic cavities. Often used for breathing control.

duodenum = 'duodenum'

The first part of the small intestine. At the junction of the stomach and the duodenum the alimentary canal is inflected. The duodenum first goes anteriorly for a short distance, turns dorsally, and eventually caudally, thus it is a U-shaped structure with two horizontal sections (a ventral and a dorsal one).

esophagus = 'esophagus'

Tube that connects the pharynx to the stomach. In mammals, the oesophagus connects the buccal cavity with the stomach. The stratified squamous non-keratinised epithelium lining the buccal cavity is continued through the pharynx down into the oesophagus. The lowest part of the oesophagus (ca. 2 cm) is lined with gastric mucosa and covered by peritoneum. The main body of the oesophagus is lined with small, simple mucous glands. Each gland opens into the lumen by a long duct which pierces the muscularis mucosae (Wilson and Washington, 1989). A sphincter is situated at the point where the oesophagus enters the stomach to prevent gastro-oesophageal reflux, i.e. to prevent acidic gastric contents from reaching stratified epithelia of the oesophagus, where they can cause inflammation and irritation (Wilson and Washington, 1989; Brown et al., 1993).

eyes = 'eyes'

An organ that detects light.

fallopian_tube = 'fallopian tube'

Initial section of the oviduct through which the ova pass from the ovary to the uterus.

fibroblasts = 'fibroblasts'

A connective tissue cell which secretes an extracellular matrix rich in collagen and other macromolecules. Flattened and irregular in outline with branching processes; appear fusiform or spindle-shaped. These cells may be vimentin-positive, fibronectin-positive, fsp1-positive, MMP-1-positive, collagen I-positive, collagen III-positive, and alpha-SMA-negative.

gallbladder = 'gallbladder'

An organ that aids digestion and stores bile produced by the liver. Rats do not have a gallbladder, but produce bile. The bile flows directly from the liver through the (hepatic) bile duct into the small intestine (Hebel and Stromberg, 1988).

hair = 'hair'

A filament, mostly protein, that grows from follicles found in the dermis.

heart = 'heart'

10.1101/gad.1485706

Type:

A myogenic muscular circulatory organ found in the vertebrate cardiovascular system composed of chambers of cardiac muscle. It is the primary circulatory organ. Taxon notes

Type:

“ the ascidian tube-like heart lacks chambers….The ascidian heart is formed after metamorphosis as a simple tube-like structure with a single-layered myoepithelium that is continuous with a single-layered pericar- dial wall. It lacks chambers and endocardium…. The innovation of the chambered heart was a key event in vertebrate evolution, because the chambered heart generates one-way blood flow with high pressure, a critical requirement for the efficient blood supply of large-body vertebrates… all extant vertebrates have hearts with two or more chambers (Moorman and Christoffels 2003)” DOI

ileum = 'ileum'

The portion of the small intestine that extends from the jejunum to the colon.

jejunum = 'jejunum'

The portion of the small intestine that extends from the duodenum to the ileum.

kidney = 'kidney'

A paired organ of the urinary tract that produces urine and maintains bodily fluid homeostasis, blood pressure, pH levels, red blood cell production and skeleton mineralization.

liver = 'liver'

An exocrine gland which secretes bile and functions in metabolism of protein and carbohydrate and fat, synthesizes substances involved in the clotting of the blood, synthesizes vitamin A, detoxifies poisonous substances, stores glycogen, and breaks down worn-out erythrocytes.

long_bone = 'long bone'

A long bone is a bone that has a shaft and 2 ends and is longer than it is wide. Long bones have a thick outside layer of compact bone and an inner medullary cavity containing bone marrow. The ends of a long bone contain spongy bone and an epiphyseal plate or line depending on the stage of development. The epiphyseal plate is a hyaline cartilage, where new bone growth takes place, lengthening the bone prior to adulthood. Bone lengthening ceases when this cartilage is lost, leaving a remnant referred to as an epiphyseal line.

lumbar_spinal_cord = 'lumbar spinal cord'

lung = 'lung'

Respiration organ that develops as an outpocketing of the esophagus.

lymph_node = 'lymph node'

Any of the rounded masses of lymphoid tissue that are surrounded by a capsule of connective tissue, are distributed along the lymphatic vessels, and contain numerous lymphocytes which filter the flow of lymph.

middle_ear_bone = 'middle ear bone'

One of 3 small bones contained within the middle ear space and serve to transmit sounds from the air to the fluid-filled labyrinth. The absence of the auditory ossicles would constitute a moderate-to-severe hearing loss. The term ‘ossicles’ literally means ‘tiny bones’ and commonly refers to the auditory ossicles, though the term may refer to any small bone throughout the body.

nail = 'nail'

A horn-like keratin structure covering the dorsal aspect of the terminal phalanges of fingers and toes.

ovary = 'ovary'

The gonad of a female organism which contains germ cells.

pancreas = 'pancreas'

An endoderm derived structure that produces precursors of digestive enzymes and blood glucose regulating hormones.

peripheral_nerve = 'peripheral nerve'

An enclosed, cable-like bundle of axons in the peripheral nervous system originating in a nerve root in the central nervous system (or a condensed nervous structure) connecting with peripheral structures.

pineal = 'pineal'

A midline, cone like structure located in the dorso-caudal roof of the 3rd ventricle, attached by peduncles to the habenular and posterior commissures. The stalk contains nerve fibers, blood vessels, connective tissue and parenchymal cells (Paxinos, The Rat Central Nervous System, 2nd ed, pg 399).

pituitary = 'pituitary'

The pituitary gland is an endocrine gland that secretes hormones that regulate many other glands.

placenta = 'placenta'

Organ of metabolic interchange between fetus and mother, partly of embryonic origin and partly of maternal origin[GO]. The fetal portion of the placenta is known as the villous chorion. The maternal portion is known as the decidua basalis. The two portions are held together by anchoring villi that are anchored to the decidua basalis by the cytotrophoblastic shell. Marsupials possess only a rudimentary yolk-type placenta, with reduced nutrient and oxygen exchanging capabilities.

plasma = 'plasma'

The liquid component of blood, in which erythrocytes are suspended.

prostate = 'prostate'

The prostate gland is a partly muscular, partly glandular body that is situated near the base of the mammalian male urethra and secretes an alkaline viscid fluid which is a major constituent of the ejaculatory fluid.

psoas_muscle = 'psoas muscle'

A powerful flexor of the thigh at the hip joint (psoas major) and a weak flexor of the trunk and lumbar spinal column (psoas minor). Psoas is derived from the Greek ‘psoa’, the plural meaning ‘muscles of the loin’. It is a common site of infection manifesting as abscess (PSOAS ABSCESS). The psoas muscles and their fibers are also used frequently in experiments in muscle physiology.

sacral_spinal_cord = 'sacral spinal cord'

A spinal cord segment that adjacent_to a sacral region.

serum = 'serum'

The portion of blood plasma that excludes clotting factors.

skin = 'skin'

The organ covering the body that consists of the dermis and epidermis.

spleen = 'spleen'

The organ that functions to filter blood and to store red corpuscles and platelets.

stomach = 'stomach'

An expanded region of the vertebrate alimentary tract that serves as a food storage compartment and digestive organ. A stomach is lined, in whole or in part by a glandular epithelium.

systemic_tissue = 'systemic tissue'

Tissue with cells that deposit non-polarized extracellular matrix including connective tissue fibers and ground substance.

temporalis_muscle = 'temporalis muscle'

Pharyngeal arch 1 muscle that is innervated by the deep temporal branches of the trigeminal nerve and attaches to the wall of the braincase (including the cranial vault) and mandible.

testicle = 'testicle'

A typically paired male reproductive gland that produces sperm and that in most mammals is contained within the scrotum at sexual maturity.

thoracic_spinal_cord = 'thoracic spinal cord'

The thoracic nerves are the spinal nerves emerging from the thoracic vertebrae.

thymus = 'thymus'

Anatomical structure of largely lymphoid tissue that functions in cell-mediated immunity by being the site where T cells develop.

thyroid = 'thyroid'

A two-lobed endocrine gland found in all vertebrates, located in front of and on either side of the trachea in humans, and producing various hormones, such as triiodothyronine and calcitonin.

tissue = 'tissue'

Multicellular anatomical structure that consists of many cells of one or a few types, arranged in an extracellular matrix such that their long-range organisation is at least partly a repetition of their short-range organisation.

tissue_other = 'tissue-other'

tongue = 'tongue'

A fleshy movable process of the floor of the mouths of most vertebrates that bears sensory end organs and small glands and functions especially in taking and swallowing food and in humans as a speech organ.

tonsil = 'tonsil'

Either of the two small almond-shaped masses of lymph tissue found on either side of the oropharynx.

trachea = 'trachea'

The trachea is the portion of the airway that attaches to the bronchi as it branches.

umbilical_cord = 'umbilical cord'

The connecting cord from the developing embryo to the placenta.

ureter = 'ureter'

Muscular duct that propels urine from the kidneys to the urinary bladder, or related organs.

urinary_bladder = 'urinary bladder'

Distensible musculomembranous organ situated in the anterior part of the pelvic cavity in which urine collects before excretion.

urine = 'urine'

Excretion that is the output of a kidney.

uterus = 'uterus'

The female muscular organ of gestation in which the developing embryo or fetus is nourished until birth.

vein = 'vein'

Any of the tubular branching vessels that carry blood from the capillaries toward the heart.

vitreous = 'vitreous'

vitreous_humor = 'vitreous humor'

A transparent, semigelatinous substance that fills the cavity behind the crystalline lens of the eye and in front of the retina.

whole_blood = 'whole blood'

An achromatic cell of the myeloid or lymphoid lineages capable of ameboid movement, found in blood or other tissue.

class bkbit.models.donor.TissueCollectionState(value)

Bases: str, Enum

An enumeration.

autopsy = 'autopsy'

A postmortem examination of the body that includes an examination of the internal organs and structures after dissection to determine the cause of death and the nature of pathological changes.

surgery = 'surgery'

Surgery is a medical procedure that uses operative manual and instrumental techniques on a patient to investigate and/or treat a pathological condition such as disease or injury, to help improve bodily function or appearance. The key difference between a surgery and a general procedure is the need for an incision. Making an incision, or cutting into the skin to gain access to the body’s deeper tissues or organs, is a defining characteristic of surgery.

class bkbit.models.donor.TissueSource(value)

Bases: str, Enum

An enumeration.

brain = 'brain'

The brain is the center of the nervous system in all vertebrate, and most invertebrate, animals. Some primitive animals such as jellyfish and starfish have a decentralized nervous system without a brain, while sponges lack any nervous system at all. In vertebrates, the brain is located in the head, protected by the skull and close to the primary sensory apparatus of vision, hearing, balance, taste, and smell.

nasal_swab = 'nasal swab'

A specimen obtained using a cotton swab on a stick, passed up the nostril to obtain a sample of exudate and epithelial debris for microbiological or cellular examination.

other = 'other'

plasma = 'plasma'

The liquid component of blood, in which erythrocytes are suspended.

serum = 'serum'

The portion of blood plasma that excludes clotting factors.

unknown = 'unknown'

whole_blood = 'whole blood'

A fluid that is composed of blood plasma and erythrocytes.

class bkbit.models.donor.TraumaticType(value)

Bases: str, Enum

An enumeration.

chronic_traumatic_encephalopathy = 'chronic traumatic encephalopathy'

A tauopathy that is characterized by an abundance of hyperphosphorylated tau protein in neurons, astrocytes and cell processes around blood vessels and that is associated with repetitive head impacts or exposure to blast waves.

contusion = 'contusion'

An injury to biological tissue in which the capillaries are damaged, allowing blood to seep into the surrounding tissue

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'

class bkbit.models.donor.VascularType(value)

Bases: str, Enum

An enumeration.

acute_infarct = 'acute infarct'

Tissue necrosis in any area of the brain, including the cerebral hemispheres, the cerebellum, and the brain stem. Brain infarction is the result of a cascade of events initiated by inadequate blood flow through the brain that is followed by hypoxia and hypoglycemia in brain tissue. Damage may be temporary, permanent, selective or pan-necrosis.

chronic_infarct = 'chronic infarct'

Tissue necrosis in any area of the brain, including the cerebral hemispheres, the cerebellum, and the brain stem. Brain infarction is the result of a cascade of events initiated by inadequate blood flow through the brain that is followed by hypoxia and hypoglycemia in brain tissue. Damage may be temporary, permanent, selective or pan-necrosis.

hemorrhage = 'hemorrhage'

Loss of blood from the vascular compartment to the exterior or into nonvascular body space as a result of rupture or severance of the blood vessels.

other_LEFT_PARENTHESISspecifyRIGHT_PARENTHESIS = 'other (specify)'