MicroRNA Let-7i Is a Promising Serum Biomarker for Post-stroke Cognitive Impairment and Alleviated OGD-Induced Cell Damage in vitro by Regulating Bcl-2
Bibliographic record
Abstract
Background The mechanism of post-stroke cognitive impairment (PSCI) has not been explained. We aimed to investigate whether miR-let-7i participates in the PSCI and illuminates its underlying role in oxygen–glucose deprivation (OGD)-induced cell apoptosis. Methods Blood samples from 36 subjects with PSCI and 38 with post-stroke cognitive normality (Non-PSCI) were collected to evaluate the differential expression of miR-let-7 family members, using qRT-PCT analysis. Spearman correlation was performed to estimate the correlation between the miR-1et-7i level and Montreal Cognitive Assessment (MoCA) score. Treatment of SH-SY5Y cells with OGD was used to induce cell apoptosis in vitro . Effects of miR-let-7i on OGD-induced cell apoptosis was estimated after transfection. The target gene of miR-let-7i was analyzed by dual luciferase reporter gene assay. Results The expression of miR-let-7i was up-regulated in PSCI patients compared with Non-PSCI ( p < 0.001) and negatively correlated with MoCA score ( r = −0.643, p < 0.001). When exposed to OGD, SH-SY5Y cells showed significant apoptosis accompanied by miR-let-7i up-regulation. In OGD-treated cells, miR-let-7i up-regulation was accompanied by cell apoptosis, while down-regulation showed the opposite effect. Luciferase reporter assay showed that Bcl-2 was a target gene of miR-let-7i. Western blot showed that miR-let-7i up-regulation promoted Bcl-2 expression, while qRT-PCR showed that miR-let-7i had no effect on Bcl-2 expression. Conclusion miR-let-7i was overexpressed in PSCI patients and it could be used as a diagnostic biomarker for PSCI. We illuminated the potential mechanism that miR-let-7i alleviated OGD-induced cell damage by targeting Bcl-2 at the post-transcriptional level.
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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".