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Record W3191576165 · doi:10.3791/62980

Optimization of the Retinal Vein Occlusion Mouse Model to Limit Variability

2021· article· en· W3191576165 on OpenAlexaff
Crystal Colón Ortiz, Anna M. Potenski, Jacqueline Lawson, Jade Smart, Carol M. Troy

Bibliographic record

VenueJournal of Visualized Experiments · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsColumbia College
FundersNational Institute of Neurological Disorders and StrokeNational Eye InstituteNational Institute on Aging
KeywordsOcclusionRetinal VeinRetinalRetinaMedicineOphthalmologyBranch retinal vein occlusionVascular occlusionMacular edemaNeuroscienceCardiologyBiology

Abstract

fetched live from OpenAlex

Mouse models of retinal vein occlusion (RVO) are often used in ophthalmology to study hypoxic-ischemic injury in the neural retina. In this report, a detailed method pointing out critical steps is provided with recommendations for optimization to achieve consistently successful occlusion rates across different genetically modified mouse strains. The RVO mouse model consists primarily of the intravenous administration of a photosensitizer dye followed by laser photocoagulation using a retinal imaging microscope attached to an ophthalmic guided laser. Three variables were identified as determinants of occlusion consistency. By adjusting the wait time after rose bengal administration and balancing the baseline and experimental laser output, the variability across experiments can be limited and a higher success rate of occlusions achieved. This method can be used to study retinal diseases that are characterized by retinal edema and hypoxic-ischemic injury. Additionally, as this model induces vascular injury, it can also be applied to study the neurovasculature, neuronal death, and inflammation.

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 score0.059
Threshold uncertainty score0.315

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 head0.034
GPT teacher head0.417
Teacher spread0.382 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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