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Record W4408995533 · doi:10.3390/cells14070505

Preclinical Models for Studying Fuchs Endothelial Corneal Dystrophy

2025· review· en· W4408995533 on OpenAlexafffund
Lexie W. Q. Xi, Wesley Luu, Myagmartsend Enkhbat, Dawn Neo, Jodhbir S. Mehta, Gary S. L. Peh, Evelyn K. F. Yim

Bibliographic record

VenueCells · 2025
Typereview
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsUniversity of Waterloo
FundersNational Medical Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsRegenerative medicineExtracellular matrixBiologyDiseaseTransplantationIn vivoEndothelial stem cellMedicineBioinformaticsPathologyNeuroscienceStem cellCell biologyGeneticsIn vitroSurgery

Abstract

fetched live from OpenAlex

Fuchs Endothelial Corneal Dystrophy (FECD) is a corneal endothelial disease that causes microenvironment alterations and endothelial cell loss, which leads to vision impairment. It has a high global prevalence, especially in elderly populations. FECD is also one of the leading indications of corneal transplantation globally. Currently, there is no clearly defined canonical pathway for this disease, and it has been proposed that the combinatorial effects of genetic mutations and exogenous factors cause FECD. Clinical studies and observations have provided valuable knowledge and understanding of FECD, while preclinical studies are essential for gaining insights into disease progression and mechanisms for the development and testing of regenerative medicine therapies. In this review, we first introduce the proposed genetic and molecular pathologies of FECD. Notably, we discuss the impact of abnormal extracellular matrix deposition (guttata), endothelial-to-mesenchymal transition, cell senescence, and oxidative stress on the pathology and etiology of FECD. We review and summarize the in vitro cell models, ex vivo tissues, and in vivo animal models used to study FECD. The benefits and challenges of each model are also discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.143
GPT teacher head0.389
Teacher spread0.246 · 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.

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

Citations10
Published2025
Admission routes2
Has abstractyes

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