The role of mitochondrial damage-associated molecular patterns in acute pancreatitis.

Acute pancreatitis (AP) is one of the most common gastrointestinal tract diseases with significant morbidity and mortality. Current treatments remain unspecific and supportive due to the severity and clinical course of AP, which can fluctuate rapidly and unpredictably. Mitochondria, cellular power plant to produce energy, are involved in a variety of physiological or pathological activities in human body. There is a growing evidence indicating that mitochondria damage-associated molecular patterns (mtDAMPs) play an important role in pathogenesis and progression of AP. With the pro-inflammatory properties, released mtDAMPs may damage pancreatic cells by binding with receptors, activating downstream molecules and releasing inflammatory factors. This review focuses on the possible interaction between AP and mtDAMPs, which include cytochrome c (Cyt c), mitochondrial transcription factor A (TFAM), mitochondrial DNA (mtDNA), cardiolipin (CL), adenosine triphosphate (ATP) and succinate, with focus on experimental research and potential therapeutic targets in clinical practice. Preventing or diminishing the release of mtDAMPs or targeting the mtDAMPs receptors might have a role in AP progression.

Comparison of an interpretable extreme gradient boosting model and an artificial neural network model for prediction of severe acute pancreatitis.

INTRODUCTION: Acute pancreatitis (AP) that progresses to persistent organ failure is referred to as severe acute pancreatitis (SAP). It is a condition associated with a relatively high mortality. A prediction model that would facilitate early recognition of patients at risk for SAP is crucial for improvement of patient prognosis. OBJECTIVES: The aim of this study was to evaluate the accuracy of extreme gradient boosting (XGBoost) and artificial neural network (ANN) models for predicting SAP. PATIENTS AND METHODS: A total of 648 patients with AP were enrolled. XGBoost and ANN models were developed and validated in the training (519 patients) and test sets (129 patients). The accuracy and predictive performance of the XGBoost and ANN models were evaluated using both the area under the receiver operating characteristic curves (AUCs) and the area under the precision­recall curves (AUC­PRs). RESULTS: A total of 15 variables were selected for model construction through a univariable analysis. The AUCs of the XGBoost and ANN models in 5­fold cross­validation of the training set were 0.92 (95% CI, 0.87-0.97) and 0.86 (95% CI, 0.78-0.92), respectively, whereas the AUCs for the test set were 0.93 (95% CI, 0.85-1) and 0.87 (95% CI, 0.79-0.96), respectively. The XGBoost model outperformed the ANN model in terms of both diagnostic accuracy and AUC­PR. Individual predictions of the XGBoost model were explained using a local interpretable model­agnostic explanation plot. CONCLUSIONS: An interpretable XGBoost model showed better discriminatory efficiency for predicting SAP than the ANN model, and could be used in clinical practice to identify patients at risk for SAP.

Comprehensive review on the pathogenesis of hypertriglyceridaemia-associated acute pancreatitis.

It is well known, that the inflammatory process that characterizes acute pancreatitis (AP) can lead to both pancreatic damage and systemic inflammatory response syndrome (SIRS). During the last 20 years, there has been a growing incidence of episodes of acute pancreatitis associated with hypertriglyceridaemia (HTAP). This review provides an overview of triglyceride metabolism and the potential mechanisms that may contribute to developing or exacerbating HTAP. The article comprehensively discusses the various pathological roles of free fatty acid, inflammatory response mechanisms, the involvement of microcirculation, serum calcium overload, oxidative stress and the endoplasmic reticulum, genetic polymorphism, and gut microbiota, which are known to trigger or escalate this condition. Future perspectives on HTAP appear promising, with ongoing research focused on developing more specific and effective treatment strategies.

Relationship between Cholesterol-Related Lipids and Severe Acute Pancreatitis: From Bench to Bedside.

It is well known that hypercholesterolemia in the body has pro-inflammatory effects through the formation of inflammasomes and augmentation of TLR (Toll-like receptor) signaling, which gives rise to cardiovascular disease and neurodegenerative diseases. However, the interaction between cholesterol-related lipids and acute pancreatitis (AP) has not yet been summarized before. This hinders the consensus on the existence and clinical importance of cholesterol-associated AP. This review focuses on the possible interaction between AP and cholesterol-related lipids, which include total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein (Apo) A1, from the bench to the bedside. With a higher serum level of total cholesterol, LDL-C is associated with the severity of AP, while the persistent inflammation of AP is allied with a decrease in serum levels of cholesterol-related lipids. Therefore, an interaction between cholesterol-related lipids and AP is postulated. Cholesterol-related lipids should be recommended as risk factors and early predictors for measuring the severity of AP. Cholesterol-lowering drugs may play a role in the treatment and prevention of AP with hypercholesterolemia.

Fabp5 is a common gene between a high-cholesterol diet and acute pancreatitis.

Background and aims: Hypercholesterolemia has been identified as risk factor for severe acute pancreatitis (AP). We aimed to identify the common differentially expressed genes (DEGs) between a high-cholesterol diet and AP. Methods: We retrived gene expression profiles from the GEO database. DEGs were assessed using GEO2R. For AP hub genes, we conducted functional enrichment analysis and protein-protein interaction (PPI) analysis. GeneMANIA and correlation analysis were employed to predict potential DEG mechanisms. Validation was done across various healthy human tissues, pancreatic adenocarcinoma, peripheral blood in AP patients, and Sprague-Dawley rats with AP. Results: The gene "Fabp5" emerged as the sole common DEG shared by a high-cholesterol diet and AP. Using the 12 topological analysis methods in PPI network analysis, Rela, Actb, Cdh1, and Vcl were identified as hub DEGs. GeneMANIA revealed 77.6% physical interactions among Fabp5, TLR4, and Rela, while genetic correlation analysis indicated moderate associations among them. Peripheral blood analysis yielded area under the ROC curve (AUC) values of 0.71, 0.63, 0.74, 0.64, and 0.91 for Fabp5, TLR4, Actb, Cdh1 genes, and artificial neural network (ANN) model respectively, in predicting severe AP. In vivo immunohistochemical analysis demonstrated higher Fabp5 expression in the hyperlipidemia-associated AP group compared to the AP and control groups. Conclusion: Fabp5 emerged as the common DEG connecting a high-cholesterol diet and AP. Rela was highlighted as a crucial hub gene in AP. Genetic interactions were observed among Fabp5, TLR4, and Rela. An ANN model consisting of Fabp5, TLR4, Actb, and Cdh1 was helpful in predicting severe AP.

Non-linear correlation between amylase day 2 to day 1 ratio and incidence of severe acute pancreatitis.

Background: This study aimed to assess whether the amylase day 2/amylase day 1 ratio was associated with severe acute pancreatitis (SAP). Methods: We retrospectively enrolled 464 patients with acute pancreatitis. Serum amylase was measured on admission (day 1) and 24 h later (day 2). Univariable logistic regression with restricted cubic spline analysis, multivariable logistic analysis, and receiver operating characteristic curve analysis was used to evaluate the relationship between the amylase day 2/amylase day 1 ratio and SAP. Results: A non-linear association between the amylase day 2/amylase day 1 ratio and SAP was observed. The multivariable logistic analysis confirmed that a high amylase day 2/amylase day 1 ratio (≥0.3) was independently associated with the development of SAP (OR: 6.62). The area under the receiver operating characteristic curve (AUC) of the amylase day 2/amylase day 1 ratio, as a predictive factor for SAP, was 0.65. When amylase ratio ≥0.3 was counted as 1 point and added to the BISAP score to build a new model named the BISAPA (BISAP plus Amylase ratio) score (AUC = 0.86), it improved the diagnostic power of the original BISAP score (AUC = 0.83) for SAP. With a cut-off value of 3, the BISAPA score achieved a sensitivity of 66.0%, a specificity of 86.7%, and diagnostic accuracy of 84.48%. Conclusions: There is a non-linear correlation between the amylase day 2/amylase day 1 ratio and the incidence of SAP. BISAPA score might also be a useful tool for the same purpose.