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Record W2471290523 · doi:10.4103/1673-5374.184448

The intricacies of neurotrophic factor therapy for retinal ganglion cell rescue in glaucoma: a case for gene therapy

2016· review· en· W2471290523 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeural Regeneration Research · 2016
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRetinal Development and Disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsNeurotrophic factorsNeuroscienceRetinal ganglion cellNeurotrophinGenetic enhancementRegeneration (biology)MedicineGlaucomaRetinaBioinformaticsBiologyGeneCell biologyInternal medicineGeneticsReceptor

Abstract

fetched live from OpenAlex

Regeneration of damaged retinal ganglion cells (RGC) and their axons is an important aspect of reversing vision loss in glaucoma patients. While current therapies can effectively lower intraocular pressure, they do not provide extrinsic support to RGCs to actively aid in their protection and regeneration. The unmet need could be addressed by neurotrophic factor gene therapy, where plasmid DNA, encoding neurotrophic factors, is delivered to retinal cells to maintain sufficient levels of neurotrophins in the retina. In this review, we aim to describe the intricacies in the design of the therapy including: the choice of neurotrophic factor, the site and route of administration and target cell populations for gene delivery. Furthermore, we also discuss the challenges currently being faced in RGC-related therapy development with special considerations to the existence of multiple RGC subtypes and the lack of efficient and representative in vitro models for rapid and reliable screening in the drug development process.

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.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.103
GPT teacher head0.399
Teacher spread0.297 · 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