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Lymphatic vessels identified in failed corneal transplants with neovascularisation

2018· article· en· W2896449081 on OpenAlexafffund
Michael Adam Diamond, Sze Wah Samuel Chan, Xun Zhou, Yelena Glinka, E Girard, Yeni H. Yücel, Neeru Gupta

Bibliographic record

VenueBritish Journal of Ophthalmology · 2018
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsPodoplaninCD31Lymphatic systemPathologyLymphangiogenesisImmunofluorescenceMedicineLymphatic vesselImmunohistochemistryLymphatic EndotheliumCorneaAntibodyOphthalmologyImmunologyMetastasisCancer

Abstract

fetched live from OpenAlex

Background Corneal transplant failure with neovascularisation is a leading indication for full-thickness grafts in patients. Lymphangiogenesis is implicated in the pathology of graft failure, and here we systematically evaluate failed human corneal transplants with neovascularisation for the presence of lymphatic vessels. Methods Nine failed grafts with neovascularisation, based on H VEGFR-3 and LYVE-1 . All immunofluorescence and FISH samples were compared with positive and negative controls and visualised by confocal microscopy. Results Corneal neovascularisation was established in all cases by H VEGFR-3 and LYVE-1 mRNAs. Conclusions The consistent finding of lymphatic vessels in failed grafts with neovascularisation implicates them in the pathogenesis of corneal transplant failure, and points to the lymphatics as a potential new therapeutic target.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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 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

Citations18
Published2018
Admission routes2
Has abstractyes

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