Clinical value of high‐frequency ultrasound and serum <scp>miR</scp>‐92a‐3p in diabetic peripheral neuropathy
Bibliographic record
Abstract
AIMS/INTRODUCTION: Cross-sectional area (CSA) by high-frequency ultrasound (HF-US) detects morphologic changes in neuropathy. microRNA (miR)-92a-3p is linked to diabetes and nerve damage. Our study evaluated the diagnostic use of CSAs along with miR-92a-3p in diabetic peripheral neuropathy (DPN) and its relationship to the condition. MATERIALS AND METHODS: A total of 172 patients were enrolled, comprising 89 with DPN and 83 T2DM cases without DPN. Real-time quantitative polymerase chain reaction (RT-qPCR) was utilized to quantify serum miR-92a-3p levels. HF-US detected CSA in the median, ulnar, and tibial nerves. Logistic regression analyzed potential risk factors for DPN occurrence and severity. Receiver operating characteristic curves assessed the diagnostic significance of miR-92a-3p and CSAs for DPN. Pearson coefficients evaluated the correlation between the Toronto Clinical Scoring System (TCSS) score and miR-92a-3p or CSAs. RESULTS: DPN patients had significantly higher serum miR-92a-3p levels and CSAs of the median, tibial, and ulnar nerves than T2DM patients. Moreover, miR-92a-3p and CSAs were risk factors for DPN. When combined, they yielded an AUC of 0.891, with 80.90% sensitivity and 91.57% specificity, accurately identifying DPN patients. Furthermore, miR-92a-3p and CSAs correlated with TCSS score and were higher in moderate-to-severe patients with DPN than in mild patients with DPN. Finally, miR-92a-3p combined with CSAs predicted moderate-to-severe DPN with 91.67% sensitivity and 85.37% specificity. CONCLUSIONS: Serum miR-92a-3p and CSAs of median, ulnar, and tibial nerves are elevated in DPN patients. Their combination has high diagnostic significance in identifying DPN in T2DM patients and assessing its severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".