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Record W2905443246 · doi:10.1002/jcb.28227

The effects of microRNAs in activating neovascularization pathways in diabetic retinopathy

2018· review· en· W2905443246 on OpenAlexaff
Mahbobeh Satari, Esmat Aghadavod, Naghmeh Mirhosseini, Zatollah Asemi

Bibliographic record

VenueJournal of Cellular Biochemistry · 2018
Typereview
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsUniversity of SaskatchewanSaskatchewan Health Authority
Fundersnot available
KeywordsDiabetic retinopathyNeovascularizationPathogenesisMedicinemicroRNAChoroidal neovascularizationDiabetes mellitusSignal transductionBlood–retinal barrierRetinopathyPathologicalPathophysiologyAngiogenesisRetinalImmunologyBioinformaticsPathologyCancer researchBiologyOphthalmologyCell biologyEndocrinologyGene

Abstract

fetched live from OpenAlex

Diabetic retinopathy (DR) is one of the major complications of diabetes mellitus that causes diabetic macular edema and visual loss. DR is categorized, based on the presence of vascular lesions and neovascularization, into non-proliferative and proliferative DR. Vascular changes in DR correlate with the cellular damage and pathological changes in the capillaries of blood-retinal barrier. Several cytokines have been involved in inducing neovascularization. These cytokines activate different signaling pathways which are mainly responsible for the complications of DR. Recently; microRNAs (miRNAs) have been introduced as the key factors in the regulation of the cytokine expression which plays a critical role in neovascularization of retinal cells. Some studies have demonstrated that changing levels of miRNAs have essential role in the pathophysiology of vascular changes in patients with DR. The aim of this study is to identify the effects of miRNAs in the pathogenesis of DR via activating neovascularization pathways.

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)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.644
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.269
Teacher spread0.258 · 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
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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