Serum miR-206 and miR-132 as Potential Circulating Biomarkers for Mild Cognitive Impairment
Bibliographic record
Abstract
MicroRNAs (miRNAs), a class of small, non-coding RNA molecules with gene regulatory functions, have emerged to play a critical role in the pathogenesis of a variety of diseases. Recently, circulating miRNAs have been reported as potential biomarkers for various pathologic conditions. The present study was performed to investigate the potential role of circulating miRNAs as diagnostic biomarkers for mild cognitive impairment (MCI). We collected 66 patients with MCI and 76 normal controls from our previous cross-sectional cohort study. Seven miRNAs (miR-206, miR-132, miR-193b, miR-130b, miR-20a, miR-296, and miR-329) related to Alzheimer's disease (AD) were detected in serum using a quantitative real-time PCR (qRT-PCR) method. Each miRNA's diagnostic performance was evaluated by receiver operating characteristic curves and the areas under curves (AUC) analysis. The levels of miR-206 and miR-132 in MCI patients' serum were significantly elevated compared to normal controls. Combining detection of miR-206 and miR-132 achieved the highest AUC of 0.981, followed by test of miR-206 (AUC = 0.880) and miR-132 (AUC = 0.912) separately. Importantly, miR-206 and miR-132 were respectively correlated with the Montreal Cognitive Assessment score in MCI patients. These results preliminarily indicated that circulating miR-206 and miR-132 as novel miRNAs upregulated in MCI patient were potential biomarkers for diagnosis of MCI.
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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 | 0.000 | 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) | 0.000 | 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".