Activating SIRT3 in peritoneal mesothelial cells alleviates postsurgical peritoneal adhesion formation by decreasing oxidative stress and inhibiting the NLRP3 inflammasome
Bibliographic record
Abstract
Abstract Peritoneal adhesions (PAs) are a serious complication of abdominal surgery and negatively affect the quality of life of millions of people worldwide. However, a clear molecular mechanism and a standard therapeutic strategy for PAs have not been established. Here, we developed a standardized method to mimic the pathological changes in PAs and found that sirtuin 3 (SIRT3) expression was severely decreased in adhesion tissues, which was consistent with our bioinformatics analysis and patient adhesion tissue analysis. Thus, we hypothesized that activating SIRT3 could alleviate postsurgical PAs. Sirt3-deficient ( Sirt3 −/− ) mice exhibited many more PAs after standardized abdominal surgery. Furthermore, compared with wild-type ( Sirt3 +/+ ) mice, Sirt3-deficient ( Sirt3 −/− ) mice showed more prominent reactive oxygen species (ROS) accumulation, increased levels of inflammatory factors, and exacerbated mitochondrial damage and fragmentation. In addition, we observed NLRP3 inflammasome activation in the adhesion tissues of Sirt3 −/− but, not Sirt3 +/+ mice. Furthermore, mesothelial cells sorted from Sirt3 −/− mice exhibited impaired mitochondrial bioenergetics and redox homeostasis. Honokiol (HKL), a natural compound found in several species of the genus Magnolia , could activate SIRT3 in vitro. Then, we demonstrated that treatment with HKL could reduce oxidative stress and the levels of inflammatory factors and suppress NLRP3 activation in vivo, reducing the occurrence of postsurgical PAs. In vitro treatment with HKL also restored mitochondrial bioenergetics and promoted mesothelial cell viability under oxidative stress conditions. Taken together, our findings show that the rescue of SIRT3 by HKL may be a new therapeutic strategy to alleviate and block postsurgical PA formation.
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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) | 0.002 | 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".