ABSTRACT
Acute pancreatitis (AP) is a common disease faced by clinicians. Severe acute pancreatitis (SAP) has a high mortality rate, so early identification of patients who may develop into SAP is of great significance for guiding treatment. Machine learning is a multi-layer representational learning algorithm that analyzes and obtains laws from existing data and uses these laws to make predictions on unknown data. This study established an SAP prediction scoring system based on machine learning, which can predict the SAP risk of patients within 24 hours. The prediction accuracy rate is as high as 87.36% and AUC 94.11%. The model can better assist clinical decision-making and treatment, and guide doctors to make relevant interventions earlier.
ABSTRACT
Objective: A high-performance liquid chromatographic (HPLC) procedure with a fluorescence detector was developed to rapidly separate ?,?,?,?- tocopherol isomers in human serum Methods: The HPLC system consisted of Inertsil silica column (100-A, 3?m,4.6mm?250mm) and 7% (v/v) methyl-tert- butyl ether in n-hexane as mobile phase . Prior to HPLC, the serum sample wa deproteined by ethanol (BHT 0.0625%) and the tocopherol isomers were efficiently extracted in thei original isomeric conformations using n-hexane-ethyl acetate (5:1) in the presence of 2,6-bi-buty p-methylphenol (BHT). Result: The quantification limits, defined as the lowest quantitatively measurable concentration of the different compounds (ng/ml) are calculated according to the experiment:?-tocophero 1.0,?-tocopherol 1.0,?-tocopherol 0.5,?-tocopherol 0.5. The recovery rates are between 95%~105% Correlation coefficients are over 0.999 when the concentration is between 5 ng/ml~5 ?g/ml. Conclusion This technique is suitable for assay of tocopherol isomers in human serum at all ages.