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Record W4294308056 · doi:10.3390/metabo12090820

Urinary Levels of miR-491-5p and miR-592 as Potential Diagnostic Biomarkers in Female Aging Patients with OAB: A Pilot Study

2022· article· en· W4294308056 on OpenAlexafffund
Philippe Cammisotto, Abubakr H. Mossa, Samer Shamout, Lysanne Campeau

Bibliographic record

VenueMetabolites · 2022
Typearticle
Languageen
FieldMedicine
TopicUrinary Bladder and Prostate Research
Canadian institutionsJewish General HospitalMcGill University
FundersRéseau québécois de recherche sur le vieillissement
KeywordsOveractive bladderUrineNerve growth factorInternal medicineUrinary systemMedicineBiomarkerEndocrinologymicroRNAReceptorUrologyBiologyPathology

Abstract

fetched live from OpenAlex

Women with overactive bladder syndrome (OAB) have a lower urinary ratio of nerve growth factor (NGF) to its precursor (proNGF) compared to healthy controls. MicroRNAs related to NGF and proNGF metabolism and to their receptors may be present in urine and may possess diagnostic value. Urine and blood samples from 20 control and 20 OAB women (50–80 years) were obtained, together with validated questionnaires and other clinical parameters. The relative expression of urinary microRNAs was measured with RT-qPCR. MiR-491-5p, which negatively controls the translation of the matrix metalloproteinase-9 (MMP-9), the main enzyme degrading NGF, was significantly decreased in OAB. Similarly, miR-592, which represses p75NTR receptor synthesis, was down-regulated in OAB. Age, renal function and insulin resistance did not affect these results. ROC curves confirmed the high sensitivity of miR-491-5p and miR-592 for diagnosis. On the other hand, miRNAs involved in the expression of proNGF, of survival receptor TrkA and of markers of nerve integrity were similar between groups. The detection of miR-491-5p and miR-592 in urine could be a useful and non-invasive tool for the diagnosis of OAB syndrome in aging women.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.298
Teacher spread0.269 · 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 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

Citations2
Published2022
Admission routes2
Has abstractyes

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