MétaCan
Menu
Back to cohort
Record W4409959262 · doi:10.14293/pr2199.001643.v1

Beneficial Effects of Phenolic Compounds to Attenuate Neuronal Stress

2025· preprint· en· W4409959262 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMedicinal Plants and Bioactive Compounds
Canadian institutionsUniversity of Manitoba
FundersBanaras Hindu University
KeywordsStress (linguistics)ChemistryPharmacologyBusinessNeurosciencePsychologyMedicinePhilosophy

Abstract

fetched live from OpenAlex

Phenolic compounds are found in natural products like fruits, vegetables, roots and leaves of plants, or present in the form of drinks like wine and tea. It's evident from numerous studies that phenolic chemicals have neuroprotective properties and are potent for reducing stress. Furthermore, these substances include flavonoids, phenolic acids, stilbenes and strong antioxidants and anti-inflammatory properties. They mainly focus on the oxidative stress and non-motor symptoms of the body. Also significantly play roles in apoptosis, neurogenesis, and inflammation. Research studies showed that flavonoid like quercetin, resveratrol and epigallocatechin gallate (EGCG) enhanced the antioxidant properties of the body immune system and protected brain cells from oxidative damage. With that, phenolic chemicals also play crucial roles in distinct neuronal activity like preserving neuronal integrity and lessening the cellular stress. Hence, Phenolic chemicals aid in the therapeutic approach for the distinct neurodegenerative disorder.

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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicMedicinal Plants and Bioactive CompoundsFrench-language works237,207