Overexpression of miR-365a-3p relieves sepsis-induced acute myocardial injury by targeting MyD88/NF-κB pathway
Bibliographic record
Abstract
Sepsis often leads to systemic multiple organ dysfunction, with the majority of deaths attributable to acute myocardial injury (AMI). In this study, we aimed to explore the functional role of miR-365a-3p in sepsis-induced AMI. The sepsis myocardial injury model was constructed using lipopolysaccharide (LPS) both in vitro and in vivo with selective regulation of miR-365a-3p expression. Real-time PCR or Western blot was employed to detect the expressions of miR-365a-3p, inflammatory cytokines (tumor necrosis factor α (TNF-α), interleukin-1β (IL-1β), and IL-6), and inflammation-related proteins (nuclear factor-κB (NF-κB), I-κB, myeloid differentiation factor 88 (MyD88)) in myocardial tissues and cells. Also, cell counting kit-8 (CCK8) and flow cytometry assays were used to measure cardiomyocyte proliferation and apoptosis, respectively. Furthermore, the targeting relationship between miR-365a-3p and MyD88 was verified with the dual luciferase activity assay. miR-365a-3p was downregulated in LPS-induced myocardial injury model. miR-365a-3p overexpression attenuated cardiomyocyte apoptosis and suppressed the expressions of inflammatory cytokines and proteins. Inhibiting miR-365a-3p, however, produced the opposite effects. Mechanistically, miR-365a-3p targeted the 3'-untranslated region of MyD88, thereby inactivating MyD88-mediated NF-κB pathway. miR-365a-3p overexpression mitigated sepsis-mediated myocardial injury by inhibiting MyD88-mediated NF-κB activation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".