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Record W3212437635 · doi:10.1038/s41598-021-01077-w

Neuroprotection of retinal ganglion cells by the sigma-1 receptor agonist pridopidine in models of experimental glaucoma

2021· article· en· W3212437635 on OpenAlexaff
Michal Geva, Noga Gershoni‐Emek, Luana Naia, Philip T. T. Ly, Sandra I. Mota, A. Cristina Rego, Michael R. Hayden, Leonard A. Levin

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPharmacological Receptor Mechanisms and Effects
Canadian institutionsMontreal Neurological Institute and HospitalBC Children's HospitalMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsNeuroprotectionRetinaSigma-1 receptorGlaucomaRetinal ganglion cellNeuroscienceRetinalAgonistRetinal degenerationPharmacologyBiologyCell biologyReceptorMedicineOphthalmologyBiochemistry

Abstract

fetched live from OpenAlex

Optic neuropathies such as glaucoma are characterized by retinal ganglion cell (RGC) degeneration and death. The sigma-1 receptor (S1R) is an attractive target for treating optic neuropathies as it is highly expressed in RGCs, and its absence causes retinal degeneration. Activation of the S1R exerts neuroprotective effects in models of retinal degeneration. Pridopidine is a highly selective and potent S1R agonist in clinical development. We show that pridopidine exerts neuroprotection of retinal ganglion cells in two different rat models of glaucoma. Pridopidine strongly binds melanin, which is highly expressed in the retina. This feature of pridopidine has implications to its ocular distribution, bioavailability, and effective dose. Mitochondria dysfunction is a key contributor to retinal ganglion cell degeneration. Pridopidine rescues mitochondrial function via activation of the S1R, providing support for the potential mechanism driving its neuroprotective effect in retinal ganglion cells.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.286

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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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