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Record W2808973343 · doi:10.1371/journal.pone.0199079

The route of administration influences the therapeutic index of an anti-proNGF neutralizing mAb for experimental treatment of Diabetic Retinopathy

2018· article· en· W2808973343 on OpenAlexafffund
Pablo F. Barcelona, Alba Galán, Hinyu Nedev, Yifan Jian, Marinko V. Šarunic, H. Uri Saragovi

Bibliographic record

VenuePLoS ONE · 2018
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsSimon Fraser UniversityMcGill UniversityJewish General Hospital
FundersCanadian Institutes of Health ResearchFoundation Fighting BlindnessMcGill University
KeywordsMedicineDiabetic retinopathyPharmacologyOphthalmologyDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

Many neurodegenerative retinal diseases are treated with monoclonal antibodies (mAb) delivered by invasive intravitreal injection (IVT). In Diabetic Retinopathy there is a scarcity of effective agents that can be delivered using non-invasive methods, and there are significant challenges in the validation of novel therapeutic targets. ProNGF represents a potential novel target, and IVT administration of a function-blocking anti-proNGF mAb is therapeutic in a mouse model of DR. We therefore compared invasive IVT to less invasive systemic intravenous (IV) and local subconjunctival (SCJ) administration, for therapy of Diabetic Retinopathy. The IV and SCJ routes are safe, afford sustained pharmacokinetics and tissue penetration of anti-proNGF mAb, and result in long-term therapeutic efficacy that blocks retinal inflammation, edema, and neuronal death. SCJ may be a more convenient and less-invasive approach for ophthalmic use and may enable reduced frequency of intervention for the treatment of retinal pathologies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.331
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2018
Admission routes2
Has abstractyes

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