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.
Exchange bias between ferromagnetic and antiferromagnetic layers has been widely utilized in spintronic devices. Controlling the exchange bias in magnetic multilayers by an electric field (E-field) has been proposed as a low-power solution for manipulating the macroscopic properties such as exchange bias fields and magnetization values, while how the magnetic domains respond to the E-fields has rarely been reported in an exchange-biased system. Here, we realize the vector imaging of reversible electrical modulation of magnetization reversal in exchange-biased CoFeB/IrMn/PMN-PT (011) multiferroic heterostructures, utilizing in-situ quantitative magneto-optical Kerr effect (MOKE) microscopy. Under the electrical control, magnetic domains at −80 Oe rotate reversibly between around 160°and 80°-120°, whose transverse components reverse from 225°to 45°correspondingly. Moreover, pixel-by-pixel comparisons are conducted to further imply the reversible magnetization reversal by E-fields. Efield-induced reversible magnetization reversal is also demonstrated without applying external magnetic fields. Vector imaging of electrical manipulation of exchange bias is of great significance in understanding the magnetoelectric effect and the development of next-generation spintronic devices.
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