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Record W2093457385 · doi:10.1074/jbc.m111.294967

Pharmacologic Uncoupling of Angiogenesis and Inflammation during Initiation of Pathological Corneal Neovascularization

2011· article· en· W2093457385 on OpenAlexaff
Jeremy M. Sivak, Allison C. Ostriker, Amber Woolfenden, John Demirs, Rosemarie Cepeda, Debby Long, Karen Anderson, Bruce Jaffee

Bibliographic record

VenueJournal of Biological Chemistry · 2011
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsNeovascularizationAngiogenesisInflammationCorneal neovascularizationPathologicalCorneal inflammationMedicineChemistryCancer researchPathologyImmunology

Abstract

fetched live from OpenAlex

Pathological neovascularization occurs when a balance of pro- and anti-angiogenic factors is disrupted, accompanied by an amplifying inflammatory cascade. However, the interdependence of these responses and the mechanism triggering the initial angiogenic switch have remained unclear. We present data from an epithelial debridement model of corneal neovascularization describing an initial 3-day period when a substantial component of neovascular growth occurs. Administration of selective inhibitors shows that this initial growth requires signaling through VEGFR-2 (vascular endothelial growth factor receptor-2), independent of the accompanying inflammatory response. Instead, increased VEGF production is found prominently in repair epithelial cells and is increased prior to recruitment of neutrophil/granulocytes and macrophage/monocytes. Consequently, early granulocyte and monocyte depletion has little effect on corneal neovascularization outgrowth. These data indicate that it is possible to pharmacologically uncouple these mechanisms during early injury-driven neovascularization in the cornea and suggest that initial tissue responses are coordinated by repair epithelial cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.213

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.053
GPT teacher head0.262
Teacher spread0.209 · 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 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

Citations22
Published2011
Admission routes1
Has abstractyes

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