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Record W4323036310 · doi:10.1113/jp283824

RyR2‐dependent modulation of neuronal hyperactivity: A potential therapeutic target for treating Alzheimer's disease

2023· article· en· W4323036310 on OpenAlexafffund
Jinjing Yao, S.R. Wayne Chen

Bibliographic record

VenueThe Journal of Physiology · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health ResearchKrembil FoundationHeart and Stroke Foundation of Canada
KeywordsNeuroscienceDiseaseModulation (music)Alzheimer's diseaseMedicinePsychologyInternal medicinePhysics

Abstract

fetched live from OpenAlex

Increasing evidence suggests that simply reducing β-amyloid (Aβ) plaques may not significantly affect the progression of Alzheimer's disease (AD). There is also increasing evidence indicating that AD progression is driven by a vicious cycle of soluble Aβ-induced neuronal hyperactivity. In support of this, it has recently been shown that genetically and pharmacologically limiting ryanodine receptor 2 (RyR2) open time prevents neuronal hyperactivity, memory impairment, dendritic spine loss and neuronal cell death in AD mouse models. By contrast, increased RyR2 open probability (Po) exacerbates the onset of familial AD-associated neuronal dysfunction and induces AD-like defects in the absence of AD-causing gene mutations. Thus, RyR2-dependent modulation of neuronal hyperactivity represents a promising new target for combating 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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.001

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 head0.045
GPT teacher head0.339
Teacher spread0.295 · 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 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

Citations9
Published2023
Admission routes2
Has abstractyes

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