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Record W4404862059 · doi:10.1186/s40478-024-01892-y

Retinal cytoarchitecture is preserved in an organotypic perfused human and porcine eye model

2024· article· en· W4404862059 on OpenAlexafffund
Darren Chan, J. Zhang, Gah-Jone Won, Jeremy M. Sivak

Bibliographic record

VenueActa Neuropathologica Communications · 2024
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersNational Eye InstituteCanadian Institutes of Health ResearchUniversity Health Network FoundationNational Institutes of HealthGlaucoma Research Foundation
KeywordsCytoarchitectureRetinalRetinaEx vivoBiologyNeuroscienceOphthalmologyAnatomyPathologyMedicineIn vivo

Abstract

fetched live from OpenAlex

Pathobiology of the intact human retina has been challenging to study due to its relative inaccessibility and limited sample availability. Thus, there is a great need for new translational models that can maintain human retinal integrity and cytoarchitecture. The role of physiologic intraocular pressure (IOP) and fluid flow on retinal tissue has not been well studied. Here, we present an ex vivo organotypic model to assess the impact of physiological intraocular perfusion on retinal cytoarchitecture and cell survival. We demonstrate that retinal cytoarchitecture is remarkably well preserved following re-establishment of physiological IOP and aqueous humor dynamics for up to 24 h in ex vivo whole globe porcine and human eyes, comparable to freshly preserved control eyes. Accordingly, cell death was minimized in the perfused retinas, which also displayed normal markers of cellular metabolism and astrogliosis. These results are in marked contrast to contralateral control eyes without active perfusion, which displayed excessive cell death and disrupted cytoarchitecture at the same time point. These experiments demonstrate the critical impact that physiological pressure and fluid flow have on retinal tissue, and introduce a new pre-clinical model to study human and porcine retinal health and degeneration in a relevant biomechanical setting.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.567

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 integrity0.0000.001
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 head0.046
GPT teacher head0.337
Teacher spread0.291 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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