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Parvalbumin as a sex-specific target in Alzheimer’s disease research – A mini-review

2023· review· en· W4386074883 on OpenAlexafffund
Dylan J. Terstege, Jonathan R. Epp

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2023
Typereview
Languageen
FieldNeuroscience
TopicStress Responses and Cortisol
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaFondation Brain Canada
KeywordsParvalbuminNeurosciencePsychologyDiseaseAlzheimer's diseaseDegenerative diseaseMedicineCentral nervous system diseasePathology

Abstract

fetched live from OpenAlex

Alzheimer’s disease (AD) is the most common form of dementia, and both the incidence of this disease and its associated cognitive decline disproportionally effect women. While the etiology of AD is unknown, recent work has demonstrated that the balance of excitatory and inhibitory activity across the brain may serve as a strong predictor of cognitive impairments in AD. Across the cortex, the most prominent source of inhibitory signalling is from a class of parvalbumin-expressing interneurons (PV+). In this mini-review, the impacts of sex- and age-related factors on the function of PV+ neurons are examined within the context of vulnerability to AD pathology. These primary factors of influence include changes in brain metabolism, circulating sex hormone levels, and inflammatory response. In addition to positing the increased vulnerability of PV+ neurons to dysfunction in AD, this mini-review highlights the critical importance of presenting sex stratified data in the study of AD.

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)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.599
GPT teacher head0.531
Teacher spread0.067 · 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

Citations14
Published2023
Admission routes2
Has abstractno

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