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Record W4401967163 · doi:10.1002/9781394238071.ch15

Molecular Mechanisms of Flavonoids in Chronic Metabolic Diseases and Path to Clinical Trials

2024· other· en· W4401967163 on OpenAlexaff
Mahnoor Zafar, Neelum Gul Qazi, Waqas Nawaz, Muhammad Imran Khan

Bibliographic record

Venuenot available
Typeother
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacological Effects of Natural Compounds
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsPath (computing)Clinical trialMedicineComputational biologyBioinformaticsBiologyIntensive care medicineComputer scienceComputer network

Abstract

fetched live from OpenAlex

This book chapter provides a comprehensive insight into the molecular mechanisms of flavonoids and their essential contribution in the management and treatment of chronic metabolic diseases. The effect of flavonoids on metabolic pathways at the molecular level is explored in detail along with their potential to influence the progression of a disease. Furthermore, it explores the components of dosage administration and delivery systems and seeks a balanced clinical outcome and avoids potential toxic effects associated with the use of flavonoids. Specific strategies for measuring and mitigating these risks are discussed, emphasizing the need for a careful balance between safety and efficacy. The chapter also addresses the way to take flavonoids from preclinical investigations in an animal model and an in vitro model to clinical trials. This will assist in identifying potential hurdles and providing valuable insights to hasten the transition from bench to bed. Overall, this chapter provides an important reference to researchers, clinicians, pharmacologists, and the herbal medicine field by highlighting the promising role of flavonoids in combating chronic metabolic diseases. It critically outlines the various steps needed for effective translation of flavonoid research into clinical practice.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.189
GPT teacher head0.548
Teacher spread0.359 · 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
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

Citations5
Published2024
Admission routes1
Has abstractyes

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