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Record W3047761849 · doi:10.3390/jcm9082592

(Neuro) Peptides, Physical Activity, and Cognition

2020· review· en· W3047761849 on OpenAlexaff
J. Autio, Ville Stenbäck, Dominique D. Gagnon, Juhani Leppäluoto, Karl‐Heinz Herzig

Bibliographic record

VenueJournal of Clinical Medicine · 2020
Typereview
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsLaurentian University
Fundersnot available
KeywordsMedicineCognitionCognitive declineGalaninNeuropeptideNeuroscienceDementiaGhrelinSomatostatinCentral nervous systemNeurotensinEndocrinologyInternal medicineReceptorDiseasePsychology

Abstract

fetched live from OpenAlex

Regular physical activity (PA) improves cognitive functions, prevents brain atrophy, and delays the onset of cognitive decline, dementia, and Alzheimer's disease. Presently, there are no specific recommendations for PA producing positive effects on brain health and little is known on its mediators. PA affects production and release of several peptides secreted from peripheral and central tissues, targeting receptors located in the central nervous system (CNS). This review will provide a summary of the current knowledge on the association between PA and cognition with a focus on the role of (neuro)peptides. For the review we define peptides as molecules with less than 100 amino acids and exclude myokines. Tachykinins, somatostatin, and opioid peptides were excluded from this review since they were not affected by PA. There is evidence suggesting that PA increases peripheral insulin growth factor 1 (IGF-1) levels and elevated serum IGF-1 levels are associated with improved cognitive performance. It is therefore likely that IGF-1 plays a role in PA induced improvement of cognition. Other neuropeptides such as neuropeptide Y (NPY), ghrelin, galanin, and vasoactive intestinal peptide (VIP) could mediate the beneficial effects of PA on cognition, but the current literature regarding these (neuro)peptides is limited.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.249
GPT teacher head0.494
Teacher spread0.245 · 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 designOther design
Domainnot available
GenreReview

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

Citations25
Published2020
Admission routes1
Has abstractyes

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