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Record W2002772742 · doi:10.1155/2014/272968

Mechanisms of Action of Indigenous Antidiabetic Plants from the Boreal Forest of Northeastern Canada

2014· article· en· W2002772742 on OpenAlexafffundabout
Hoda M. Eid, Pierre S. Haddad

Bibliographic record

VenueAdvances in Endocrinology · 2014
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsUniversité de MontréalCanadian Institutes of Health Research
FundersCanadian Institutes of Health ResearchCree Board of Health and Social Services of James Bay
KeywordsIndigenousTraditional medicineTraditional knowledgeEthnobotanyMedicinal plantsBiologyGeographyMedicineEcology

Abstract

fetched live from OpenAlex

Indigenous populations in Canada possess a wealth of native traditional knowledge. However, their rates of Type 2 diabetes mellitus (T2DM), a disease that was unheard of in their midst 50 years ago, are the highest in the country. In an effort to cut the impact of T2DM epidemic on Indigenous health, the Canadian Institutes of Health Research funded the “CIHR Team in Aboriginal Antidiabetic Medicines (CIHR-TAAM).” The goal was to explore Boreal forest medicinal plants stemming from Indigenous Traditional Medicine to be included in T2DM care. Six out of nine communities of the Cree of Eeyou Istchee (CEI) participated in ethnobotanical studies that resulted in the identification of 17 potential antidiabetic plant species. These species were screened for antidiabetic activities using a platform of in vitro bioassays and in vivo models of T2DM. This paper summarizes results on the 10 most promising plant species, their active constituents, and the mechanisms behind their antidiabetic activities. In addition, potential herb-drug interactions were examined at the level of drug-metabolizing enzymes, notably the cytochrome P450 family. This review serves as a canvas onto which is discussed the value of Indigenous medicinal plants, future avenues of research, and the ethical approach required in this field.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.256
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2014
Admission routes3
Has abstractyes

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