Differentially Expressed miR-511-3p in Stroke Patients Predicts the Presence of Post-Stroke Cognitive Impairment
Bibliographic record
Abstract
INTRODUCTION: Stroke is common cerebrovascular disease in the elderly, which is characterized by neurological defects caused by cerebral vessels. Multiple studies have shown that miRNAs play important roles in stroke. In addition, a large number of evidence suggest that stroke increases the risk and severity of cognitive impairments. METHODS: miR-511-3p expression levels were detected by real-time PCR. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic value of miR-511-3p in distinguishing stroke patients from healthy controls and to assess risk of post-stroke cognitive impairment (PSCI) in stroke patients. Pearson correlation coefficient was used to determine the relationship between miR-511-3p expression level and Montreal Cognitive Assessment Scale (MoCA) scores. RESULTS: Serum miR-511-3p expression levels were decreased in stroke patients, and the decrease was more significant in PSCI patients. ROC curve results showed that miR-511-3p had high diagnostic accuracy in distinguishing healthy controls from stroke patients. Moreover, the expression level of miR-511-3p can be used as an independent predictor for the occurrence of PSCI and is positively correlated with MoCA scores of PSCI patients. CONCLUSION: miR-511-3p may be involved in the occurrence and development of stroke. In addition, miR-511-3p may be a novel biomarker for predicting PSCI occurred in stroke patients. These results may help improve the quality of prognosis of stroke.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".