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Pericytes in the optic nerve head

2025· review· en· W4410844217 on OpenAlexaff
Susannah Waxman, Deborah Villafranca‐Baughman, Julie A. Phillippi, Tatjana Jakobs, Luis Alarcón-Martínez, Adriana Di Polo, Ian A. Sigal

Bibliographic record

VenueProgress in Retinal and Eye Research · 2025
Typereview
Languageen
FieldMedicine
TopicCerebral Venous Sinus Thrombosis
Canadian institutionsUniversité de Montréal
FundersNational Eye InstituteNational Institutes of HealthResearch to Prevent BlindnessEye and Ear Foundation of Pittsburgh
KeywordsOptic nerveHead (geology)AnatomyNeuroscienceMedicineBiology

Abstract

fetched live from OpenAlex

Pericytes are a unique population of contractile mural cells and are an essential part of the microvasculature. In the retina and brain, pericytes play crucial roles in regulating blood flow, maintaining the blood-brain barrier, signaling with neighboring cells, and depositing extracellular matrix. Pericyte dysfunction is an early process in a variety of neurodegenerative conditions. However, remarkably little is known about pericytes at an early site of neurodegeneration in glaucoma, the optic nerve head (ONH). This work summarizes the current understanding of pericyte contributions to ONH physiology, identifies potential roles in glaucomatous pathophysiology, and uncovers open questions at the intersection of these areas. We surveyed the literature to identify the roles of ONH pericytes in the context of health and glaucoma. Additionally, we probed for the presence of pericytes along microvasculature in mouse, nonhuman primate, and human donor ONH tissues. We identified an association between factors influencing ONH dysfunction in glaucoma and factors influencing pericyte dysfunction in other neurodegenerative conditions. Pericytes exist in the mouse, nonhuman primate, and human ONH, implicating their capacity for local function. ONH pericytes represent a promising but underexplored target for treating microvascular impairment in glaucoma. Investigating the contribution of pericytes in both healthy and disease states can help inform mechanisms of dysfunction in glaucomatous pathology, paving the way for the development of novel therapeutic strategies.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.162
GPT teacher head0.500
Teacher spread0.339 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations3
Published2025
Admission routes1
Has abstractno

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