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Record W4390904141 · doi:10.1038/s42003-023-05662-9

Susceptibility to acute cognitive dysfunction in aged mice is underpinned by reduced white matter integrity and microgliosis

2024· article· en· W4390904141 on OpenAlexaff
Dáire Healy, Carol Murray, C. McAdams, R.Wood Power, Pierre-Louis Hollier, Lucas Silva Tortorelli, Ana Belén López-Rodríguez, Colm Cunningham

Bibliographic record

VenueCommunications Biology · 2024
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsTrinity College
FundersNational Institute on AgingNational Institutes of HealthU.S. Department of Health and Human Services
KeywordsNeuroinflammationWhite matterDeliriumInflammationNeuroscienceCognitive declineSystemic inflammationMedicineMyelinPsychologyImmunologyInternal medicineCentral nervous systemDementiaPsychiatryDisease

Abstract

fetched live from OpenAlex

Age is a significant but heterogeneous risk factor for acute neuropsychiatric disturbances such as delirium. Neuroinflammation increases with aging but the determinants of underlying risk for acute dysfunction upon systemic inflammation are not clear. We hypothesised that, with advancing age, mice would become progressively more vulnerable to acute cognitive dysfunction and that neuroinflammation and neuronal integrity might predict heterogeneity in such vulnerability. Here we show region-dependent differential expression of microglial transcripts, but a ubiquitously observed primed signature: chronic Clec7a expression and exaggerated Il1b responses to systemic bacterial LPS. Cognitive frailty (vulnerability to acute disruption under acute stressors LPS and double stranded RNA; poly I:C) was increased in aged animals but showed heterogeneity and was significantly correlated with reduced myelin density, synaptic loss and severity of white matter microgliosis. The data indicate that white matter disruption and neuroinflammation may be key substrates of the progressive but heterogeneous risk for delirium in aged individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.508
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.341
Teacher spread0.313 · 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 teacher head, not a consensus.

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

Citations22
Published2024
Admission routes1
Has abstractyes

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