Dynamic changes in the aggregation-depolymerization behavior of Ovomucin-Complex and its binding to urease during in vitro simulated gastric digestion.

Ovomucin-Complex extracted from egg white is expected to have a barrier function similar to gastric mucin. In this study, the dynamic changes in structure, rheological properties and binding ability of Ovomucin-Complex during in vitro simulated gastric digestion were investigated. The results from HPLC and CLSM showed that extremely acidic pH (pH = 2.0) promoted Ovomucin-Complex to form aggregation. Acid-induced aggregation may hinder its binding to pepsin, thus rendering Ovomucin-Complex resistant to pepsin. Consequently, most of the polymer structure and weak gel properties of Ovomucin-Complex retained after simulated gastric digestion as verified by HPLC, CLSM and rheological measurement, although there was a small breakdown of the glycosidic bond as confirmed by the increased content of reducing sugar. The significantly reduced hydrophobic interactions of Ovomucin-Complex were observed under extremely acidic conditions and simulated gastric digestion compared with the native. Noticeably, the undigested Ovomucin-Complex after simulated gastric digestion showed a higher affinity (KD = 5.0 ± 3.2 nm) for urease - the key surface antigen of Helicobacter pylori. The interaction mechanism between Ovomucin-Complex and urease during gastric digestion deserves further studies. This finding provides a new insight to develop an artificial physical mucus barrier to reduce Helicobacter pylori infection.

Machine learning based peri-surgical risk calculator for abdominal related emergency general surgery: a multicenter retrospective study.

BACKGROUND: Currently, there is a lack of ideal risk prediction tools in the field of emergency general surgery (EGS). The American Association for the Surgery of Trauma recommends developing risk assessment tools specifically for EGS-related diseases. In this study, we sought to utilize machine learning (ML) algorithms to explore and develop a web-based calculator for predicting five perioperative risk events of eight common operations in EGS. METHOD: This study focused on patients with EGS and utilized electronic medical record systems to obtain data retrospectively from five centers in China. Five ML algorithms, including Random Forest (RF), Support Vector Machine, Naive Bayes, XGBoost, and Logistic Regression, were employed to construct predictive models for postoperative mortality, pneumonia, surgical site infection, thrombosis, and mechanical ventilation >48 h. The optimal models for each outcome event were determined based on metrics, including the value of the Area Under the Curve, F1 score, and sensitivity. A comparative analysis was conducted between the optimal models and Emergency Surgery Score (ESS), Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and American Society of Anesthesiologists (ASA) classification. A web-based calculator was developed to determine corresponding risk probabilities. RESULT: Based on 10 993 patients with EGS, we determined the optimal RF model. The RF model also exhibited strong predictive performance compared with the ESS, APACHE II score, and ASA classification. Using this optimal model, the authors developed an online calculator with a questionnaire-guided interactive interface, catering to both the preoperative and postoperative application scenarios. CONCLUSIONS: The authors successfully developed an ML-based calculator for predicting the risk of postoperative adverse events in patients with EGS. This calculator accurately predicted the occurrence risk of five outcome events, providing quantified risk probabilities for clinical diagnosis and treatment.