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Resveratrol as a Protective Molecule for Neuroinflammation: A Review of Mechanisms

2014· review· en· W1982678998 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

VenueCurrent Pharmaceutical Biotechnology · 2014
Typereview
Languageen
FieldMedicine
TopicSirtuins and Resveratrol in Medicine
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNeuroinflammationResveratrolChemistryPharmacologyMedicineNeuroscienceInflammationImmunologyBiology

Abstract

fetched live from OpenAlex

Under normal conditions, most of the central nervous system (CNS) is protected by the blood brain barrier (BBB) from systemic inflammation progression and from the infiltration of immune cells. As a consequence, the CNS developed an original way to provide surveillance, defense and repair, which relies on the complex process of neuroinflammation. Despite tight regulation, neuroinflammation is frequently the cause of irreversible nerve cell loss but it is also where the solution lies. Specific immune crosstalk taking place in the CNS needs to be decoded in order to identify the best therapeutic strategies aimed at helping the CNS restore homeostasis in difficult conditions such as in neurodegenerative disorders. This review deals with the double-edged sword nature of neuroinflammation and the use of resveratrol in various models as one of the most promising therapeutic molecules for preventing the consequences of nerve cell autodestruction.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.082
GPT teacher head0.459
Teacher spread0.377 · 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