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Record W4205501172 · doi:10.1002/alz.12543

Increased RyR2 open probability induces neuronal hyperactivity and memory loss with or without Alzheimer's disease–causing gene mutations

2022· article· en· W4205501172 on OpenAlexafffund
Jinjing Yao, Ya‐Jing Liu, Bo Sun, Xiaoqin Zhan, John Paul Estillore, Ray W. Turner, S.R. Wayne Chen

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsLibin Cardiovascular Institute of AlbertaUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsRyanodine receptor 2MutationPremovement neuronal activityNeuroscienceMutantRyanodine receptorBiologyGeneGeneticsReceptor

Abstract

fetched live from OpenAlex

INTRODUCTION: Neuronal hyperactivity is an early neuronal defect commonly observed in familial and sporadic Alzheimer's disease (AD), but the underlying mechanisms are unclear. METHODS: mutation that markedly increases the channel's open probability (Po) to determine the impact of increased RyR2 activity in neuronal function without AD gene mutations. RESULTS: Genetically increasing RyR2 Po induced neuronal hyperactivity in vivo in anesthetized and awake mice. Increased RyR2 Po induced hyperactive behaviors, impaired learning and memory, defective dendritic spines, and neuronal cell death. Increased RyR2 Po exacerbated the onset of neuronal hyperexcitability and learning and memory impairments in 5xFAD mice. DISCUSSION: Increased RyR2 Po exacerbates the onset of familial AD-associated neuronal dysfunction, and induces AD-like defects in the absence of AD-causing gene mutations, suggesting that RyR2-associated neuronal hyperactivity represents a common target for combating AD with or without AD gene mutations.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0020.000

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 head0.064
GPT teacher head0.332
Teacher spread0.269 · 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

Citations35
Published2022
Admission routes2
Has abstractyes

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