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Lipid mediators in glaucoma: Unraveling their diverse roles and untapped therapeutic potential

2024· article· en· W4391257095 on OpenAlexafffund
DJ Mathew, JM Sivak

Bibliographic record

VenueProstaglandins & Other Lipid Mediators · 2024
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsNeuroinflammationNeurodegenerationLipid signalingGlaucomaNeuroscienceInflammationNeuroprotectionEndocannabinoid systemMedicineBiologyDiseaseImmunologyPathologyReceptorInternal medicine

Abstract

fetched live from OpenAlex

Glaucoma is a complex neurodegenerative disease characterized by optic nerve damage and visual field loss, and remains a leading cause of irreversible blindness. Elevated intraocular pressure (IOP) is a critical risk factor that requires effective management. Emerging research underscores dual roles of bioactive lipid mediators in both IOP regulation, and the modulation of neurodegeneration and neuroinflammation in glaucoma. Bioactive lipids, encompassing eicosanoids, specialized pro-resolving mediators (SPMs), sphingolipids, and endocannabinoids, have emerged as crucial players in these processes, orchestrating inflammation and diverse effects on aqueous humor dynamics and tissue remodeling. Perturbations in these lipid mediators contribute to retinal ganglion cell loss, vascular dysfunction, oxidative stress, and neuroinflammation. Glaucoma management primarily targets IOP reduction via pharmacological agents and surgical interventions, with prostaglandin analogues at the forefront. Intriguingly, additional lipid mediators offer promise in attenuating inflammation and providing neuroprotection. Here we explore these pathways to shed light on their intricate roles, and to unveil novel therapeutic avenues for glaucoma management.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.246
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.

Study designObservational
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

Citations13
Published2024
Admission routes2
Has abstractyes

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