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Record W3165616893 · doi:10.1152/ajpendo.00008.2021

Exercise and estrogen: common pathways in Alzheimer’s disease pathology

2021· review· en· W3165616893 on OpenAlexafffund
Ahmed Bagit, Grant C. Hayward, Rebecca E. K. MacPherson

Bibliographic record

VenueAmerican Journal of Physiology-Endocrinology and Metabolism · 2021
Typereview
Languageen
FieldMedicine
TopicMenopause: Health Impacts and Treatments
Canadian institutionsUniversity of OttawaBrock University
FundersScottish Rite Charitable Foundation of Canada
KeywordsEstrogenDiseasePathologicalNeurotrophic factorsBrain-derived neurotrophic factorMedicineRisk factorAlzheimer's diseasePsychologyEpidemiologyNeuroscienceBioinformaticsInternal medicineEndocrinologyBiology

Abstract

fetched live from OpenAlex

Alzheimer's disease (AD) is a neurodegenerative disease that is characterized by progressive declines in cognitive function. Current epidemiological data indicate significant sex-linked disparities, where females have a higher risk of developing AD compared with male counterparts. This disparity necessitates further investigations to uncover the pathological and molecular factors influencing these sex differences. Although the underlying pathways behind this observed disparity remain elusive, recent research points to menopausal estrogen loss as a potential factor. Estrogen holds a significant role in amyloid precursor protein (APP) processing and overall neuronal health through the regulation of brain-derived neurotrophic factor (BDNF), a factor that is also reduced in postmenopausal women. BDNF is a known contributor to neuronal health and its reduced expression is typically linked to AD disorders. Exercise is known to increase BDNF and may provide an accessible activity for postmenopausal women to reduce their risk of AD. This review aims to discuss the relationship between estrogen, exercise, and BDNF in AD pathology.

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)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.055
GPT teacher head0.359
Teacher spread0.304 · 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

Citations54
Published2021
Admission routes2
Has abstractyes

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