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Record W1850082263 · doi:10.18433/j3t60g

Strategies for the Discovery and Development of Anti-Diabetic Drugs from the Natural Products of Traditional Medicines

2013· review· en· W1850082263 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2013
Typereview
Languageen
FieldChemistry
TopicChromatography in Natural Products
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilRMIT University
KeywordsMateria medicaTraditional medicineEngineering ethicsChinaNatural (archaeology)Natural medicineMedicineAlternative medicineEngineeringPolitical scienceGeography

Abstract

fetched live from OpenAlex

This review discusses issues largely from the biological point of view about the targeted approaches for the use of natural products for the discovery of anti-diabetic drugs in collaboration with medicinal chemists and computer-aided drug design. A major thrust of this review reflects the collaborative research of four institutions: RMIT University (Australia), Garvan Institute of Medical Research (Australia), Shanghai Institute of Materia Medica of the Chinese Academy of Science (China) and Sun-Yat Sen University (China) in the past eight years. By joining forces of biomedical research in diabetes and medicinal chemistry with a focus on traditional medicine, they are trying to bridge the West (the latest research discoveries in biomedical research) with the East (traditional medicine) to step forward in drug discovery from natural products.

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.

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)0.002
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.146
GPT teacher head0.403
Teacher spread0.258 · 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