The anti-inflammatory mechanism of SAHA in acute pancreatitis through HDAC5/SLIT2/Akt/β-catenin axis
Bibliographic record
Abstract
Acute pancreatitis (AP) is widely recognized to be an inflammation-related disease, in which HDAC was upregulated. The anti-inflammatory role of suberoylanilide hydroxamic acid (SAHA), a HDAC inhibitor, has been documented. In this context, this research was implemented to figure out whether SAHA manipulated inflammation in AP. Subsequent to induction of AP mouse model, HDAC5 expression was detected. The binding of HDAC5 and SLIT2 was detected by Co-Immunoprecipitation and Chromatin immunoprecipitation assays. SAHA treatment and gain- and loss-of-function approaches were used in AP mice and lipopolysaccharide (LPS)-induced pancreatic acinar cells. In mice, biochemical methods were implemented to measure activities of pancreatic lipase, trypsin, myeloperoxidase (MPO) and pancreatic edema, TUNEL staining to determine pancreatic cell apoptosis, and flow cytometry to assess the total number of leukocytes and neutrophils in pancreas. In pancreatic acinar cells, CCK-8 was performed to evaluate cell viability. HDAC5 exhibited overexpression in AP mice. Mechanical analysis showed that HDAC5 facilitated SLIT2 deacetylation to downregulate SLIT2, thus activating Akt/β-catenin pathway in pancreatic acinar cells. SAHA treatment, HDAC5 silencing or SLIT2 overexpression diminished inflammation in AP in vivo and in vitro. SAHA treatment, HDAC5 silencing or SLIT2 overexpression reduced activities of pancreatic lipase, trypsin, MPO, pancreatic edema and cell apoptosis in AP mice as well as elevated viability of LPS-induced pancreatic acinar cells. SAHA might exert anti-inflammatory effects in AP mice via HDAC5/SLIT2/Akt/β-catenin axis.
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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) | 0.000 | 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".