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Record W4410628886 · doi:10.7554/elife.106720

Progressive mural cell deficiencies across the lifespan in a foxf2 model of cerebral small vessel disease

2025· preprint· en· W4410628886 on OpenAlexafffund
Merry Faye E Graff, Emma EM Heeg

Bibliographic record

VenueeLife · 2025
Typepreprint
Languageen
FieldMedicine
TopicCerebrovascular and genetic disorders
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
FundersCanadian Institutes of Health Research
KeywordsDiseaseMuralMedicineMural cellCardiologyAnatomyPathologyInternal medicineHistorySmooth muscle

Abstract

fetched live from OpenAlex

Cerebral small vessel disease (SVD) is a leading cause of stroke and dementia and yet is often an incidental finding in aged patients due to the inaccessibility of brain vasculature to imaging. Animal models are important for modelling the development and progression of SVD across the lifespan. In humans, reduced FOXF2 is associated with an increased stroke risk and SVD prevalence in humans. In the zebrafish, foxf2 is expressed in pericytes and vascular smooth muscle cells and is involved in vascular stability. We use partial foxf2 loss of function ( foxf2a -/- ) to model the lifespan effect of reduced Foxf2 on small vessel biology. We find that the initial pool of pericytes in developing foxf2a mutants is strongly reduced. The few brain pericytes present in mutants have strikingly longer processes and enlarged soma. foxf2a mutant pericytes can partially repopulate the brain after ablation, suggesting some recovery is possible. Despite this capacity, adult foxf2a mutant brains show regional heterogeneity, with some areas of normality and others with severe pericyte depletion. Taken together, foxf2a mutants fail to generate a sufficient initial population of pericytes. The pericytes that remain have abnormal cell morphology. Over the lifespan, initial pericyte deficits are not repaired and lead to severely abnormal cerebrovasculature in adults. This work opens new avenues for modeling progressive genetic forms of human cerebral small vessel disease.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

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 science0.0000.001
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 head0.025
GPT teacher head0.293
Teacher spread0.268 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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