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Effect of VEGF blockade on corneal graft neovascularization and rejection in rats

2008· article· en· W2020546153 on OpenAlexaff
N. Rocher, Francine Béhar‐Cohen, G Renard, Jean‐Louis Bourges

Bibliographic record

VenueActa Ophthalmologica · 2008
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsCorneal neovascularizationMedicineNeovascularizationOphthalmologyCorneaSalineVascular endothelial growth factorBlockadeImmunostainingVEGF receptorsSurgeryUrologyInternal medicineAngiogenesisImmunohistochemistry

Abstract

fetched live from OpenAlex

Abstract Purpose To evaluate the effect of anti‐vascular endothelial growth factor antibodies directed at VEGF 164, 120, 121 and 165, administered by subconjunctival injections (SC), on neovascularization and rejection after penetrating keratoplasty (PK) in rats Methods Twelve Lewis rats were grafted with corneal buttons from Brown Norway rats and were divided in 2 treatment groups (G) just after surgery (day 0). G1 received saline SC injections (0.02ml/inj, n=6) every 3 days from D0 to D21 and G2 received SC injections of anti‐VEGF (0.02ml/inj, 10µg/ml), with the same regimen. Rejection clinical scores were based on corneal oedema (0 to 3) and transparency (0 to 4). Surface and extension of neovascularization were scored clinically (0 to 4) and then quantified using lectin immunostaining on flat‐mounted buttons Results At D21, the mean rejection scores were significantly higher in G1 compared to G2. Rejection rates were 83% in G1 vs. 50% in G2 (p<0.05). Neovessels scores were 4±0 in G1 vs. 2.5±0.54 in G2 (p<0.001). On flat‐mounted corneas the mean ratio of vessels area/clear cornea was 58% in G1 vs. 36% in G2 (p=0,003). Conclusion The sub conjunctival administration of anti‐VEGF antibodies not only reduces neovessels growth but also prevent rejection after PK.

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 score0.022
Threshold uncertainty score0.355

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 head0.024
GPT teacher head0.279
Teacher spread0.255 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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