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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".