Accurate prediction of acute pancreatitis severity with integrative blood molecular measurements.

BACKGROUND: Early diagnosis of severe acute pancreatitis (SAP) is essential to minimize its mortality and improve prognosis. We aimed to develop an accurate and applicable machine learning predictive model based on routine clinical testing results for stratifying acute pancreatitis (AP) severity. RESULTS: We identified 11 markers predictive of AP severity and trained an AP stratification model called APSAVE, which classified AP cases within 24 hours at an average area under the curve (AUC) of 0.74 +/- 0.04. It was further validated in 568 validation cases, achieving an AUC of 0.73, which is similar to that of Ranson's criteria (AUC = 0.74) and higher than APACHE II and BISAP (AUC = 0.69 and 0.66, respectively). CONCLUSIONS: We developed and validated a venous blood marker-based AP severity stratification model with higher accuracy and broader applicability, which holds promises for reducing SAP mortality and improving its clinical outcomes. MATERIALS AND METHODS: Nine hundred and forty-five AP patients were enrolled into this study. Clinical venous blood tests covering 65 biomarkers were performed on AP patients within 24 hours of admission. An SAP prediction model was built with statistical learning to select biomarkers that are most predictive for AP severity.

[Microbial ATP in rat muscle tissue and its application for the estimation of postmortem interval].

OBJECTIVE: To study the relationship between the changes of microbial ATP in rat muscle tissue and the postmortem interval (PMI). METHODS: Healthy SD rats were sacrificed and their muscles were sampled at different postmortem intervals. The concentration of microbial ATP was detected using bioluminescent assay and the data was statistically analyzed. RESULTS: The content of microbial ATP in rat muscle tissue increased along with PMI extension and peaked on postmortem day 7, thereafter decreased gradually, but increased slightly on postmortem day 10 once again. The PMI correlated best with the content of microbial ATP in rat muscle tissue within 9 days. If the PMI was the independent variable, the cubic polynomial regression equation was y = 0.020x(3) - 0.166x(2) - 0.666x + 13.412 (r2 = 0.989, P < 0.01). CONCLUSION: Changes of microbial ATP content in rat muscle tissue may be used for estimation of PMI. Since this assay requires only small amount of tissue with much less influence by self-decomposition, it may broaden the time range of PMI estimation.