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Nutrition, Neuroinflammation and Cognition

2015· article· en· W2173758291 on OpenAlexvenueno aff
Neha Vaidya, Subhadra Mandalika

Bibliographic record

VenueJournal of Nutritional Therapeutics · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroinflammation and Neurodegeneration Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroinflammationNeuroprotectionMicrogliaNeuroscienceDementiaCognitive declineInflammationOxidative stressNeurodegenerationDiseaseMedicineBiologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Activation of microglia and astrocytes leads to the production of cytokines and other inflammatory mediators which may contribute to the apoptotic cell death of neurons observed in many neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. Vulnerability of the central nervous system (CNS) to oxidative and inflammatory stress increases with age and has been postulated to be a leading contributing factor to the cognitive impairment and thereby development of neurodegenerative diseases. Suppression of microglial production of neurotoxic mediators may result in neuroprotection. This heightens the interest in the development of neuroinflammation-targeted therapeutics. Nutrition is involved in the pathogenesis of age-related cognitive decline and also neurodegenerative diseases. Certain nutrients facilitate human brain function with their immediate and long term effects. On the other hand, malnutrition influences the brain throughout life, with profound implications on cognitive decline and dementia. Several phytochemicals with potent antioxidant and anti-inflammatory activities, have been shown to repress microglial activation and exert neuroprotective effects. Thus this review highlights the role of foods, nutrients and phytochemicals in suppressing neuro-inflammation and also enhancing cognition.

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 score0.095
Threshold uncertainty score0.424

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 communication0.0000.001
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 head0.130
GPT teacher head0.307
Teacher spread0.177 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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