Objective:To summarize the clinical characteristics of patients with acute pancreatitis (AP) in plateau areas, and screen potential biomarkers for the pathogenesis of AP at high altitudes by metabolomics.Methods:A total of 42 patients with AP admitted to the Department of Gastroenterology in Lhasa People's Hospital from December 2023 to May 2024 were prospectively enrolled (AP group), and 33 healthy controls (Control group) were included during the same period. Demographic and clinical data were collected, and serum non-targeted metabolomics was performed based on ultra-performance liquid chromatography-mass spectrometry techniques. The serum metabolites map was evaluated by using principal component analysis, and a regression model with orthogonal partial least squares-discriminant analysis (OPLS-DA) was constructed to evaluate the explanatory power ( R2) and predictive power ( Q2) of these metabolites. The OPLS-DA-based dimensionality reduction was applied to compute variable importance in projection (VIP), while fold change (FC) values were used to assess the difference of metabolites between groups. A bidirectional clustering heat map was generated for samples and differential metabolites to evaluate sample similarity within groups. Additionally, a volcano plot was created to visualize differential metabolites, and the top five up-regulated and down-regulated metabolites distinguishing AP from healthy controls were selected. The receiver operating characteristic curve (ROC) was drawn, and the area under the curve (AUC), sensitivity and specificity based on ROC analysis were calculated to evaluate the differential power of differential metabolites. Results:The majority of participants were Tibetans in both the AP group (37 cases, 88.1%) and the control group (27 cases, 81.8%). The average age of AP patients was (50.45±17.85) years old, and the male to female ratio was 1.1∶1.0. The leading etiology was biliary disease (33 cases, 78.6%), and most patients encountered moderately severe disease (26 cases, 61.9%). The incidence of local complications was 83.3%, mainly thoracoabdominal effusion and peripancreatic effusion (30 cases, 71.4%). The incidence of systemic complications was 59.5%, mainly systemic inflammatory response syndrome (22 cases, 52.4%) and respiratory failure (15 cases, 35.7%). Principal component analysis showed significant differences in serum metabolites between groups. OPLS-DA showed that these metabolites effectively distinguished AP patients from healthy controls: R2=0.992 and Q2=0.913 in the positive ion mode, R2=0.983 and Q2=0.914 in the negative ion mode. There were 450 up-regulated and 366 down-regulated differential metabolites in AP group, respectively. Among them, gamma-glutamylleucine, cortisone, 4(15)-Copaen-11-ol, mytiloxanthin, and indole-3-glycol aldehyde were the top five up-regulated metabolites, while N-Acetyltryptophan, kynurenic acid, deoxyuridine monophosphate, pseudouridine, and farnesyl acetate were the top five down-regulated metabolites. ROC analysis of these markers showed that all AUC values were >0.8, with all P values <0.001, with both sensitivity and specificity exceeding 80%. Among them, N-Acetyltryptophan and farnesyl acetate possessed the best differential performance. Conclusions:Biliary causes are most frequent among AP patients in plateau area. The disease severity is mainly moderately severe, accompanied by high incidences of local and systemic complications. Some amino acids and prenol lipids could significantly distinguish AP patients from healthy controls, and might be involved in the pathogenesis of AP at high altitudes.

Objective:To summarize the clinical characteristics of patients with acute pancreatitis (AP) in plateau areas, and screen potential biomarkers for the pathogenesis of AP at high altitudes by metabolomics.Methods:A total of 42 patients with AP admitted to the Department of Gastroenterology in Lhasa People's Hospital from December 2023 to May 2024 were prospectively enrolled (AP group), and 33 healthy controls (Control group) were included during the same period. Demographic and clinical data were collected, and serum non-targeted metabolomics was performed based on ultra-performance liquid chromatography-mass spectrometry techniques. The serum metabolites map was evaluated by using principal component analysis, and a regression model with orthogonal partial least squares-discriminant analysis (OPLS-DA) was constructed to evaluate the explanatory power ( R2) and predictive power ( Q2) of these metabolites. The OPLS-DA-based dimensionality reduction was applied to compute variable importance in projection (VIP), while fold change (FC) values were used to assess the difference of metabolites between groups. A bidirectional clustering heat map was generated for samples and differential metabolites to evaluate sample similarity within groups. Additionally, a volcano plot was created to visualize differential metabolites, and the top five up-regulated and down-regulated metabolites distinguishing AP from healthy controls were selected. The receiver operating characteristic curve (ROC) was drawn, and the area under the curve (AUC), sensitivity and specificity based on ROC analysis were calculated to evaluate the differential power of differential metabolites. Results:The majority of participants were Tibetans in both the AP group (37 cases, 88.1%) and the control group (27 cases, 81.8%). The average age of AP patients was (50.45±17.85) years old, and the male to female ratio was 1.1∶1.0. The leading etiology was biliary disease (33 cases, 78.6%), and most patients encountered moderately severe disease (26 cases, 61.9%). The incidence of local complications was 83.3%, mainly thoracoabdominal effusion and peripancreatic effusion (30 cases, 71.4%). The incidence of systemic complications was 59.5%, mainly systemic inflammatory response syndrome (22 cases, 52.4%) and respiratory failure (15 cases, 35.7%). Principal component analysis showed significant differences in serum metabolites between groups. OPLS-DA showed that these metabolites effectively distinguished AP patients from healthy controls: R2=0.992 and Q2=0.913 in the positive ion mode, R2=0.983 and Q2=0.914 in the negative ion mode. There were 450 up-regulated and 366 down-regulated differential metabolites in AP group, respectively. Among them, gamma-glutamylleucine, cortisone, 4(15)-Copaen-11-ol, mytiloxanthin, and indole-3-glycol aldehyde were the top five up-regulated metabolites, while N-Acetyltryptophan, kynurenic acid, deoxyuridine monophosphate, pseudouridine, and farnesyl acetate were the top five down-regulated metabolites. ROC analysis of these markers showed that all AUC values were >0.8, with all P values <0.001, with both sensitivity and specificity exceeding 80%. Among them, N-Acetyltryptophan and farnesyl acetate possessed the best differential performance. Conclusions:Biliary causes are most frequent among AP patients in plateau area. The disease severity is mainly moderately severe, accompanied by high incidences of local and systemic complications. Some amino acids and prenol lipids could significantly distinguish AP patients from healthy controls, and might be involved in the pathogenesis of AP at high altitudes.

Objective:To explore the application value of machine learning algorithms in constructing a predictive model for cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy.Methods:This study was a retrospective cohort study. The female patients with breast cancer who received anthracyclines in the Affiliated Cancer Hospital of Xinjiang Medical University from January 2020 to December 2023 were enrolled. The endpoint event was abnormal electrocardiogram (ECG). According to whether the patients had ECG abnormalities during chemotherapy, they were divided into the ECG abnormal group and the ECG normal group. The dataset was divided into the training set and the test set at a ratio of 8∶2, and logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) were used to construct a risk prediction model for cardiovascular toxicity in breast cancer patients, and the receiver operating characteristic curve, calibration curve and clinical decision curve were used to evaluate the model.Results:A total of 731 female patients with breast cancer, aged (51.6±9.4) years, were enrolled. The follow-up time was (130.3±37.1) days. There were 333 cases in the ECG abnormal group and 398 cases in the ECG normal group. Seven factors influencing cardiovascular toxicity were identified, including age, menstrual history, diabetes, combination therapy with trastuzumab, combination therapy with dexrazoxane, creatine kinase isoenzymes, and α-hydroxybutyrate dehydrogenase. In the training set, the area under the curve ( AUC) for the logistic regression, random forest, XGBoost, SVM, and MLP models was 0.712, 0.863, 0.774, 0.813, and 0.733, respectively. In the test set, the AUC was 0.671, 0.778, 0.746, 0.771, and 0.705, respectively. Calibration curves and clinical decision curves showed that the random forest model performed the best. Conclusion:Models constructed with machine learning algorithms show promise in predicting cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy, with the random forest prediction model performing the best.

Objective:To explore the application value of machine learning algorithms in constructing a predictive model for cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy.Methods:This study was a retrospective cohort study. The female patients with breast cancer who received anthracyclines in the Affiliated Cancer Hospital of Xinjiang Medical University from January 2020 to December 2023 were enrolled. The endpoint event was abnormal electrocardiogram (ECG). According to whether the patients had ECG abnormalities during chemotherapy, they were divided into the ECG abnormal group and the ECG normal group. The dataset was divided into the training set and the test set at a ratio of 8∶2, and logistic regression, random forest, extreme gradient boosting (XGBoost), support vector machine (SVM) and multilayer perceptron (MLP) were used to construct a risk prediction model for cardiovascular toxicity in breast cancer patients, and the receiver operating characteristic curve, calibration curve and clinical decision curve were used to evaluate the model.Results:A total of 731 female patients with breast cancer, aged (51.6±9.4) years, were enrolled. The follow-up time was (130.3±37.1) days. There were 333 cases in the ECG abnormal group and 398 cases in the ECG normal group. Seven factors influencing cardiovascular toxicity were identified, including age, menstrual history, diabetes, combination therapy with trastuzumab, combination therapy with dexrazoxane, creatine kinase isoenzymes, and α-hydroxybutyrate dehydrogenase. In the training set, the area under the curve ( AUC) for the logistic regression, random forest, XGBoost, SVM, and MLP models was 0.712, 0.863, 0.774, 0.813, and 0.733, respectively. In the test set, the AUC was 0.671, 0.778, 0.746, 0.771, and 0.705, respectively. Calibration curves and clinical decision curves showed that the random forest model performed the best. Conclusion:Models constructed with machine learning algorithms show promise in predicting cardiovascular toxicity in breast cancer patients receiving anthracycline-based chemotherapy, with the random forest prediction model performing the best.

Objective Current data are insufficient for comparisons of effectiveness between percutaneous coronary intervention(PCI)and coronary artery bypass grafting(CABG)among patients with coronary artery disease(CAD)and left ventricular dysfunction.Methods A total of 905 CAD patients with reduced left ventricular ejection fraction(LVEF≤35％)in single center of China who underwent either PCI or CABG were enrolled in a real-world cohort study.Clinical outcomes included short-and long-term all-cause mortality,rates of heart failure(HF)hospitalization and repeat revascularization.Propensity score matching was used to balance the 2 cohorts.Results PCI was associated with lower 30-day mortality rate(HR 0.29,95％CI 0.09-0.88,P=0.029).At a mean follow-up of 4.5 years,PCI and CABG had similar all-cause death(HR 1.00,95％CI 0.67-1.50,P=0.990)and HF hospitalization(HR 0.81,95％CI 0.40-1.64,P=0.561),but PCI had higher risk of repeat revascularization(HR 14.46,95％CI 3.43-60.98,P＜0.001).PCI was associated with more significant LVEF improvement than CABG(P=0.031 for interaction).Conclusions CAD patients with reduced LVEF who underwent PCI had lower short-term mortality rate and more LVEF improvement but higher risk of repeat revascularization during follow-up than patients who underwent CABG.PCI showed comparable long-term survival and HF hospitalization risk.

Cell Cycle Proteins ; Microtubule-Associated Proteins

COVID-19/epidemiology* ; Humans ; Mental Health ; Pandemics ; Surveys and Questionnaires

At present，there is no triage and treatment system in China for the surviving crew members after a submarine accident. The North Atlantic Treaty Organization（NATO）provides a Triage and Treatment System for the management of diseases for the patients after a submarine accident. This system classifies diseases into four types，i.e.，T1，T2，T3，and T4，according to the severity of the specific disease. Furthermore，the diseases are classified into type C1 and C2 depending on whether the hyperbaric therapy is needed. The treatment zone is divided into 5 areas：triage area，grade 1 treatment area，grade 2 treatment area，grade 3 treatment area，and T4 treatment area. Based on our previous experience in teaching submarine rescue，we put forward some suggestions for improvement on the system，so as to provide a point of reference for the development of Chinese triage and treatment system after a submarine accident in future.

With the progress of globalization, the public health emergencies represented by major infectious diseases have become a major challenge for the public health management in China. The article briefly describes the emergency response capability assessment tools in China, and introduces two emergency response assessment tools with complete content structure and wide application in the world. Then the advantages and disadvantages of the tools are compared and discussed in order to provide reference for improvement of the assessment tools for public health emergency response capability in China.
China ; Disaster Planning ; Humans ; Public Health ; Public Health Administration

At present，there is no triage and treatment system in China for the surviving crew members after a submarine accident. The North Atlantic Treaty Organization（NATO）provides a Triage and Treatment System for the management of diseases for the patients after a submarine accident. This system classifies diseases into four types，i.e.，T1，T2，T3，and T4，according to the severity of the specific disease. Furthermore，the diseases are classified into type C1 and C2 depending on whether the hyperbaric therapy is needed. The treatment zone is divided into 5 areas：triage area，grade 1 treatment area，grade 2 treatment area，grade 3 treatment area，and T4 treatment area. Based on our previous experience in teaching submarine rescue，we put forward some suggestions for improvement on the system，so as to provide a point of reference for the development of Chinese triage and treatment system after a submarine accident in future.