Inhibition of microRNA-297 alleviates THLE-2 cell injury induced by hypoxia/reoxygenation by inhibiting NLRP3 inflammasome activation via sirtuin 3
Bibliographic record
Abstract
It has been acknowledged that microRNAs (miRNAs/miRs) assume a critical role in hypoxia/reoxygenation (H/R) - induced hepatocyte injury. Therefore, cell experiments were performed in this study to investigate the mechanism of miR-297 in H/R-induced hepatocyte injury with the involvement of sirtuin 3 (SIRT3) and NOD-like receptor pyrin domain containing 3 (NLRP3). Initially, transformed human liver epithelial-2 (THLE-2) cells were utilized for H/R challenge. After miR-297 antagomir and NLRP3 adenovirus vector delivery, THLE-2 cell proliferation and apoptosis were measured by MTT, EdU, and TUNEL assays, respectively. Enzyme-linked immunosorbent assay was conducted to evaluate the levels of apoptosis-related indicators (Bax and Bcl-2) and inflammation-related indicators (interleukin 6 (IL-6) and IL-10), Western blot analysis to detect NLRP3, and cleaved caspase-1 expression. The binding relation between miR-297 and SIRT3 was examined using dual-luciferase assay. The results showed that miR-297 antagomir repressed the apoptosis and inflammation induced by H/R treatment in THLE-2 cells. Mechanistically, miR-297 antagomir diminished the extent of IκBα and nuclear factor-kappa B (NF-κB) phosphorylation and NLRP3 activation in H/R-induced THLE-2 cells by targeting SIRT3. Furthermore, NLRP3 overexpression normalized the promoting effects of miR-297 antagomir on proliferation and its inhibitory effects on apoptosis and inflammation in H/R-induced THLE-2 cells. In summary, our results elucidated that miR-297 antagomir repressed H/R-induced THLE-2 cell injury via SIRT3 promotion and NLRP3 inactivation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".