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Record W4220939572 · doi:10.1155/2022/6852276

Biogenic Phytochemicals Modulating Obesity: From Molecular Mechanism to Preventive and Therapeutic Approaches

2022· review· en· W4220939572 on OpenAlexaff
Vikram Kumar, Desh Deepak Singh, Sudarshan Singh Lakhawat, Nusrath Yasmeen, Aishwarya Pandey, Rajeev K. Singla

Bibliographic record

VenueEvidence-based Complementary and Alternative Medicine · 2022
Typereview
Languageen
FieldMedicine
TopicPharmacology and Obesity Treatment
Canadian institutionsInstitut National de la Recherche Scientifique
FundersAmity University
KeywordsObesityMedicineAdipokineDiabetes mellitusBioinformaticsMechanism (biology)InflammationPathophysiologyDiseaseEpigeneticsSleep apneaLeptinAdipocyteMetabolic syndromeInternal medicineEndocrinologyAdipose tissueBiology

Abstract

fetched live from OpenAlex

The incidence of obesity and over bodyweight is emerging as a major health concern. Obesity is a complex metabolic disease with multiple pathophysiological clinical conditions as comorbidities are associated with obesity such as diabetes, hypertension, cardiovascular disorders, sleep apnea, osteoarthritis, some cancers, and inflammation-based clinical conditions. In obese individuals, adipocyte cells increased the expression of leptin, angiotensin, adipocytokines, plasminogen activators, and C-reactive protein. Currently, options for treatment and lifestyle behaviors interventions are limited, and keeping a healthy lifestyle is challenging. Various types of phytochemicals have been investigated for antiobesity potential. Here, we discuss pathophysiology and signaling pathways in obesity, epigenetic regulations, regulatory mechanism, functional ingredients in natural antiobesity products, and therapeutic application of phytochemicals in obesity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.297
GPT teacher head0.409
Teacher spread0.113 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations61
Published2022
Admission routes1
Has abstractyes

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