MétaCan
Menu
Back to cohort
Record W4392389479 · doi:10.1002/vjch.202300222

Potential oxidoreductases inhibitors from medicinal plants‐based phytochemicals: A review

2024· review· en· W4392389479 on OpenAlexaff
Tarsila Gomes, Gracebio Alejandro, Thao Thi Phuong Tran, Amandio Vieira

Bibliographic record

VenueVietnam Journal of Chemistry · 2024
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAldose Reductase and Taurine
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAldose reductaseSorbitol dehydrogenaseEnzymeBiochemistryXanthine dehydrogenaseBiologyDehydrogenaseDiabetes mellitusPharmacologyTraditional medicineChemistryMedicineXanthine oxidase

Abstract

fetched live from OpenAlex

Abstract Oxidoreductases (ORases) are enzymes that catalyze oxidation and reduction reactions in cells or extracellular compartments of the body. Genetic mutations and other factors that affect ORase activity have been implicated in a range of diseases, especially inherited metabolic disorders. Aldose reductase, sorbitol dehydrogenase, xanthine oxidoreductase are the ORases with relevance to diabetes pathology. Herein, the strategies for screening modulators of ORase activities, and medicinal plants whose phytochemicals have the potential to influence ORase activity, are described. The overall goal is to test and identify natural products and other compounds that may have a therapeutic benefit in relation to hyperglycemia and diabetes.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.450
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.309
Teacher spread0.294 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueVietnam Journal of ChemistrySame topicAldose Reductase and TaurineFrench-language works237,207