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Record W4412942460 · doi:10.1111/jnc.70185

Shared Mechanisms in Dementia and Depression: The Modulatory Role of Physical Exercise

2025· review· en· W4412942460 on OpenAlexaff
Pedro Borges de Souza, Ana Lúcia S. Rodrigues, Fernanda G. De Felice

Bibliographic record

VenueJournal of Neurochemistry · 2025
Typereview
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsQueen's University
FundersFundação de Amparo à Pesquisa e Inovação do Estado de Santa CatarinaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsDementiaDepression (economics)NeuroscienceDiseasePsychologyPrefrontal cortexMedicineNeuroinflammationHippocampusCognitionInternal medicine

Abstract

fetched live from OpenAlex

Dementia and depression are two prevalent disorders that warrant significant attention due to their high prevalence and substantial contribution to the global burden of disease. Depression has a high incidence in the elderly and is considered a risk factor for the development of dementia, being a promising target for dementia prevention strategies. Additionally, dementia and depression share common mechanisms through which they manifest pathologically. This review addresses the potential shared mechanisms between dementia and depression, including hypothalamic-pituitary-adrenal (HPA) axis dysfunction, brain atrophy (mainly hippocampus and prefrontal cortex), cognitive decline, and neuroinflammation. It also explores the therapeutic potential of physical exercise in modulating these shared pathways, highlighting its role as a non-pharmacological intervention for the treatment and prevention of both disorders. The review also explores the muscle-brain crosstalk and the intracellular pathways through which physical exercise exerts its effects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.300
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations7
Published2025
Admission routes1
Has abstractyes

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