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Record W2005059170 · doi:10.1038/jcbfm.2015.8

A Mouse Model of Small-Vessel Disease that Produces Brain-Wide-Identified Microocclusions and Regionally Selective Neuronal Injury

2015· article· en· W2005059170 on OpenAlexafffund
Gergely Silasi, Jennifer She, Jamie D. Boyd, Songchao Xue, Timothy H. Murphy

Bibliographic record

VenueJournal of Cerebral Blood Flow & Metabolism · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsNeocortexStriatumGenetically modified mouseThalamusHippocampusWhite matterTransgeneAnatomyPathologyBiologyAtrophyNeuroscienceMedicineMagnetic resonance imagingGene

Abstract

fetched live from OpenAlex

We developed a mouse model of small-vessel disease where occlusions are produced through endovascular injection of fluorescent microspheres that target ~12 μm diameter penetrating arterioles and can be localized in histology. Using Thy1-GFP transgenic mice, we visualized the impact of microocclusions on neuronal structure. Microocclusions in the hippocampus produce cell loss or neuronal atrophy (~7% of lodged microspheres led to microinfarcts), while axons within white matter tracts, as well as the striatum and thalamus became blebbed or disrupted. Although the neocortex contained more occlusions than other structures, labeled layer 5 neurons were relatively resistant to structural damage, with <2% of the lodged microspheres producing obvious neuronal damage.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.258
Teacher spread0.207 · 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.

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

Citations64
Published2015
Admission routes2
Has abstractyes

Explore more

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