{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:39:02Z","timestamp":1792291142703,"version":"4.4.0"},"reference-count":80,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T00:00:00Z","timestamp":1754438400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p>Acute pancreatitis (AP) is pancreatic inflammation caused by abnormal activation of trypsinogen, and moderately severe or severe acute pancreatitis (SAP) can lead to systemic inflammatory response syndrome (SIRS) and organ failure, associated with high mortality. Therefore, early prediction of the severity of acute pancreatitis is particularly important to improve patient survival rate and reduce complications. Currently, many scoring systems (e.g., Ranson scoring, etc) and classical biomarkers are available in the clinical practice, but there are still many limitations, such as low predictive value and time delay. Potential biomarkers for the prediction of SAP are still a hot topic in current research. In this review, we aim to summarize newly discovered biomarkers for the prediction of the severity of acute pancreatitis in the recent years, and provide an overview of serum markers, intestinal flora, and genetic markers. At the same time, the development of emerging detection technologies [e.g., Robust AP Identification and Diagnosis (RAPIDx) and droplet digital PCR (ddPCR)] also provides new possibilities for early prediction of SAP, allowing these biomarkers to be applied clinically.<\/jats:p>","DOI":"10.3389\/fmed.2025.1619087","type":"journal-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T05:30:38Z","timestamp":1754458238000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Biomarkers to predict the severity of acute 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