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Therapeutic Margins in a Novel Preclinical Model of Retinitis Pigmentosa

2013· article· en· W2046854874 on OpenAlexaff
Richard J. Davis, Chun-Wei Hsu, Yi‐Ting Tsai, Katherine J. Wert, Javier Sancho-Pellúz, Chyuan‐Sheng Lin, Stephen H. Tsang

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsColumbia College
FundersNational Cancer InstituteWellcome TrustNational Eye InstituteResearch to Prevent BlindnessFoundation Fighting BlindnessU.S. Department of Defense
KeywordsRetinitis pigmentosaRetinal degenerationGene therapy of the human retinaAlleleLoss functionDegeneration (medical)MutantBiologyPhenotypeMedicineNeuroscienceGeneRetinaPathologyGenetics

Abstract

fetched live from OpenAlex

The third-most common cause of autosomal recessive retinitis pigmentosa (RP) is due to defective cGMP phosphodiesterase-6 (PDE6). Previous work using viral gene therapy on PDE6-mutant mouse models demonstrated photoreceptors can be rescued if administered before degeneration. However, whether visual function can be rescued after degeneration onset has not been addressed. This is a clinically important question, as newly diagnosed patients exhibit considerable loss of rods and cones in their peripheral retinas. We have generated and characterized a tamoxifen inducible Cre-loxP rescue allele, Pde6b(Stop), which allows us to temporally correct PDE6-deficiency. Whereas untreated mutants exhibit degeneration, activation of Cre-loxP recombination in early embryogenesis produced stable long-term rescue. Reversal at later time-points showed partial long-term or short-lived rescue. Our results suggest stable restoration of retinal function by gene therapy can be achieved if a sufficient number of rods are treated. Because patients are generally diagnosed after extensive loss of rods, the success of clinical trials may depend on identifying patients as early as possible to maximize the number of treatable rods.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.875
Threshold uncertainty score0.208

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 head0.048
GPT teacher head0.299
Teacher spread0.251 · 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

Citations39
Published2013
Admission routes1
Has abstractyes

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