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Record W4411671469 · doi:10.1093/pnasnexus/pgaf168

Gene therapy for ocular hypertension using hfCas13d-mediated mRNA targeting

2025· article· en· W4411671469 on OpenAlexfundno aff
Siyu Chen, Zhiquan Liu, Chien‐Hui Lo, Qing Wang, Ke Ning, Qí Zhāng, Jingyu Zhao, Ying‐Chun Shen, Yang Sun

Bibliographic record

VenuePNAS Nexus · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsnot available
FundersNational Institutes of HealthResearch to Prevent BlindnessChildren's Health Research InstituteInternational Retinal Research Foundation
KeywordsGlaucomaGene knockdownIntraocular pressureOphthalmologyMedicineOcular hypertensionGenetic enhancementRetinal ganglion cellCiliary bodyRetinalGeneBiologyGenetics

Abstract

fetched live from OpenAlex

Abstract Glaucoma is a major global cause of irreversible vision loss. It is marked by elevated intraocular pressure (IOP) and the loss of retinal ganglion cells (RGC). While there are medical and surgical therapies for glaucoma aiming to reduce aqueous humor production or enhance its drainage, these treatments are often inadequate for effectively managing the disease. In this study, we developed a targeted therapy for glaucoma by knocking down two genes associated with aqueous humor production (aquaporin 1 [AQP1] and carbonic anhydrase type 2 [CA2]) using Cas13 RNA editing systems. We demonstrate that hfCas13d-mediated knockdown of AQP1 and CA2 significantly lowers IOP in wild-type mice and in a corticosteroid-induced glaucoma mouse model. We show that the lowered IOP results from decreasing aqueous production without affecting the outflow facility; this treatment also significantly promotes RGC survival as compared with untreated control groups. Therefore, CRISPR–Cas-based gene editing may be an effective treatment to lower IOP for glaucomatous optic neuropathy.

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 score0.192
Threshold uncertainty score0.484

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 head0.016
GPT teacher head0.262
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.

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

Citations7
Published2025
Admission routes1
Has abstractyes

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