microRNAs in sudden hearing loss
Bibliographic record
Abstract
OBJECTIVES: To compare the circulating microRNA (miRNA) expression profiles between sudden sensory neural hearing loss (SSNHL) patients and age-matched normal hearing controls. STUDY DESIGN: Prospective cohort multi-center study. METHODS: Patients presenting within 28 days of onset of SSNHL were prospectively recruited along with contemporaneous age-matched controls. Pooled sera of four patient (n = 09, mean age = 53.0 years; 07, 55.0; 10, 52.9; 10, 51.6) and two control (09, 51.2 and 03, 50.0) groups were assessed using a TaqMan Low Density Array. The patients' sera were also divided into two pools, untreated (04, 57.7) and treated (32, 52.6) for additional analysis. miRNA expression level was derived from cycle threshold (Ct) values normalized to a global mean. Inter-group mean Ct differences with fold changes ≥2.0 and ≤0.5 at P < .05 were considered significant. Bioinformatic databases were used to identify putative target mRNAs or validated genes and their functional annotations. RESULTS: Thirty-six SSNHL patients (mean age 53.0 ± standard deviation (SD) 15.2 years) and 12 controls (50.9 ± 11.9) were studied. Eight miRNAs hsa-miR-590-5p/ -186-5p/ -195-5p/ -140-3p/ -128-3p/ -132-3p/ -375-3p, and -30a-3p were identified as significantly differentially expressed in SSNHL patients. Most of these miRNAs were abundantly identified in the nervous system and the putative target messenger RNAs (mRNAs) were enriched in signaling pathways such as phosphatidyl inositol 3 kinase/protein Kinase B (PI3K/Akt), Ras and mitogen-activated protein kinase (MAPK). CONCLUSION: These findings suggest the possible cellular signaling pathways that underlie the disruption of auditory signal transmission in SSNHL. LEVEL OF EVIDENCE: 2 Laryngoscope, 130:E416-E422, 2020.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".