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Kaempferol: A Dietary Flavonol in Alleviating Obesity

2023· article· en· W4385563528 on OpenAlexaff
Hamid Reza Nejabati, Sadeneh Nikzad, Leila Roshangar

Bibliographic record

VenueCurrent Pharmaceutical Design · 2023
Typearticle
Languageen
FieldMedicine
TopicNatural Antidiabetic Agents Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsObesityOxidative stressKaempferolLipid metabolismDiabetes mellitusAutophagyPharmacologyDiseaseQuercetinMedicineBiologyInternal medicineEndocrinologyAntioxidantBiochemistryApoptosis

Abstract

fetched live from OpenAlex

Obesity is considered as a chronic and high-prevalence disease on a global scale which affects all genders and ages. Although various drugs have been confirmed for the treatment of obesity, these medications have been shown to have a number of adverse effects on health. It is highlighted that natural products have an alleviative role in a broad spectrum of diseases, in particular obesity, and diabetes. Kaempferol (KMP), a plant- derived flavonol, is considerably engaged in the suppression of oxidative stress, radical scavenging, opposing cellular toxicity, and induction of the production and release of growth factors. This flavonol combats obesity by suppressing adipogenesis, regulating lipid and glucose metabolism, changing gut microbiota, and activating autophagy. Also, studies have shown that KMP exerts its anti-obesity actions by decreasing the accumulation of lipids and triglycerides (TGs), increasing fatty acid oxidation, and regulating multiple metabolic genes in the adipocytes. Considering that KMP may be a potential candidate for combating obesity, this paper summarizes the possible therapeutic roles of KMP in the treatment and prevention of this disease.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.248
GPT teacher head0.431
Teacher spread0.183 · 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 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

Citations8
Published2023
Admission routes1
Has abstractyes

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