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Record W2908894911 · doi:10.1139/bcb-2018-0159

Fisetin, a potential caloric restriction mimetic, attenuates senescence biomarkers in rat erythrocytes

2019· article· en· W2908894911 on OpenAlexvenueno aff
Sandeep Singh, Geetika Garg, Abhishek Kumar Singh, Akalabya Bissoyi, Syed Ibrahim Rizvi

Bibliographic record

VenueBiochemistry and Cell Biology · 2019
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsnot available
Fundersnot available
KeywordsFisetinOxidative stressChemistryLipid peroxidationReactive oxygen speciesSenescenceAntioxidantCaloric theoryEndocrinologyOxidative phosphorylationInternal medicineBiochemistryPharmacologyFlavonoidMedicine

Abstract

fetched live from OpenAlex

An imbalanced redox status is a hallmark of the aging process. Caloric restriction mimetics (CRMs) are compounds that produce caloric restriction benefits at the molecular, cellular, and physiological level, translating into health-promoting effects. Fisetin is the least explored CRM, and its role in modulating oxidative stress during aging is not clearly known. This study investigated the antioxidative and protective potential of fisetin in a rat model of d-galactose (D-gal)-induced accelerated senescence, and in naturally aged rat erythrocytes. Young rats (4 months), aged D-gal-induced rats [24 months; 500 mg/kg body mass (b.m.); subcutaneous injection] and naturally aged D-gal-induced rats [24 months; 500 mg/kg b.m.; subcutaneous injection] were supplemented with fisetin (15 mg/kg b.m.; orally) for 6 weeks. The resulting data indicated that supplementation with fisetin suppresses aging-induced increases in the levels of reactive oxygen species, eryptosis, lipid peroxidation, and protein oxidation. Our data also show that fisetin significantly increases the levels of antioxidants and activates the plasma membrane redox system. Taken together, the findings show that a fisetin-rich diet could be an anti-aging intervention strategy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.577

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.224
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations26
Published2019
Admission routes1
Has abstractyes

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