Objective:To assess the mental health status of medical students.Method:Approximately 456 students were tested with scl-90.Result: The medical students had more problems than the national norm on obsessive compulsive,eccentric,psychosis.female were inferior to males in somatic,interpersonal sensitivity,depression,anxiety,phobic anxiety,eccentric factor.The mental health level of high grade is betfer than low grade.Conclusion: Mental health education should be developed according to mental characteristics of medical students.

Objective:To evaluate the therapeutic effect and the prognosis of the laryngeal function preserving surgery (LFPS) and total laryngectomy in hypophngeal carcinoma.Method:Ninety-three cases of laryngopharynx carcinoma were retrospectively analyzed from September 1974 to September 2006.Among which 57 cases were primary pyriform sinus cancer,20 cases were postero-cricoid cancer and 16 cases were postero-pharyngeal wall cancer.53 cases were treated by LFPS,and 40 cases were treated by total laryngectomy.The treatment effectiveness,complication,survival rate and repair materials were analysed.Result:Adopting Kaplan-Meier survival analysis,the 3-year survival rates were 69.9%and 5-year survival rates were 43.0%.The 3-year survival rates of LFPS and Non-LFPS were 73.6% and 67.5%, and the 5-yenr survival rates of LFPS and Non-LFPS were 49.1% and 32.5%. There was no significant difference between the two groups(χ~2=2.566,P>0.05). Single element analysis indicated survival rates concerned with T-stage(χ~2=9.764,P<0.05), neck lymphatic transfer(χ~2=10.472,P<0.01) and the degree of pathological differentiation(χ~2=25.894,P<0.01).Cox regression analysis suggested that T-stage,the degree of pathological dfferentiaton and wheather going through the surgical operation were the independent elemant of the patient's prognosis.There was no significant difference between LFPS and No-LFPS in the neoplasms location, complication,neoplasms residuum and neoplasms recurrence.Conclusion:LFPS did not affect the survival rates and LFPS was feasible.LFPS can increase the living quality of laryngopharynx careinoma patients.

OBJECTIVE: To evaluate the therapeutic effect and the prognosis of the laryngeal function preserving surgery (LFPS) and total laryngectomy in hypopharyngeal carcinoma. METHOD: Ninety-three cases of laryngopharynx carcinoma were retrospectively analyzed from September 1974 to September 2006. Among which 57 cases were primary pyriform sinus cancer, 20 cases were postero-cricoid cancer and 16 cases were postero-pharyngeal wall cancer. 53 cases were treated by LFPS, and 40 cases were treated by total laryngectomy. The treatment effectiveness, complication, survival rate and repair materials were analysed. RESULT: Adopting Kaplan-Meier survival analysis, the 3-year survival rates were 69.9% and 5-year survival rates were 43.0%. The 3-year survival rates of LFPS and Non-LFPS were 73.6% and 67.5%, and the 5-year survival rates of LFPS and Non-LFPS were 49.1% and 32. 5%. There was no significant difference between the two groups (chi2 = 2.566, P > 0.05). Single element analysis indicated survival rates concerned with T-stage (chi2 = 9.764, P < 0.05), neck lymphatic transfer (chi2 = 10.472, P < 0.01) and the degree of pathological differentiation (chi2 = 25.894, P < 0.01). Cox regression analysis suggested that T-stage, the degree of pathological differentiation and whether going through the surgical operation were the independent element of the patient's prognosis. There was no significant difference between LFPS and No-LFPS in the neoplasms location, complication, neoplasms residuum and neoplasms recurrence. CONCLUSION LFPS did not affect the survival rates and LFPS was feasible. LFPS can increase the living quality of laryngopharynx carcinoma patients.
Adult ; Aged ; Aged, 80 and over ; Carcinoma, Squamous Cell ; surgery ; Female ; Humans ; Hypopharyngeal Neoplasms ; surgery ; Laryngeal Neoplasms ; surgery ; Laryngectomy ; methods ; Larynx ; physiology ; surgery ; Male ; Middle Aged ; Pharyngectomy ; methods ; Retrospective Studies

Acute pancreatitis （AP） is a gastrointestinal disease that requires early intervention， and when it progresses to moderate-severe AP （MSAP） or severe AP （SAP）， there will be a significant increase in the mortality rate of patients. Machine learning （ML） has achieved great success in the early prediction of AP using clinical data with the help of its powerful computational and learning capabilities. This article reviews the research advances in ML in predicting the severity， complications， and death of AP， so as to provide a theoretical basis and new insights for clinical diagnosis and treatment of AP through artificial intelligence.

Marine microorganisms, especially marine fungi, have historically proven their value as a prolific source for structurally novel and pharmacologically active secondary metabolites (Deshmukh et al., 2018; Carroll et al., 2022). The corals constitute a dominant part of reefs with the highest biodiversity, and harbor highly diverse and abundant microbial symbionts in their tissue, skeleton, and mucus layer, with species-specific core members that are spatially partitioned across coral microhabitats (Wang WQ et al., 2022). The coral-associated fungi were very recently found to be vital producers of structurally diverse compounds, terpenes, alkaloids, peptides, aromatics, lactones, and steroids. They demonstrate a wide range of bioactivity such as anticancer, antimicrobial, and antifouling activity (Chen et al., 2022). The genetically powerful genus Emericella (Ascomycota), which has marine and terrestrial sources, includes over 30 species and is distributed worldwide. It is considered a rich source of diverse secondary metabolites with antimicrobial activity or cytotoxicity (Alburae et al., 2020). Notably, Emericella nidulans, the sexual state of a classic biosynthetic strain Aspergillus nidulans, was recently reported as an important source of highly methylated polyketides (Li et al., 2019) and isoindolone-containing meroterpenoids (Zhou et al., 2016) with unusual skeletons.
Animals ; Aspergillus nidulans ; Polyketides/chemistry* ; Anthozoa/microbiology* ; Anti-Infective Agents/pharmacology* ; Alkaloids

OBJECTIVE: To establish a machine learning model based on extreme gradient boosting (XGBoost) algorithm for early prediction of severe acute pancreatitis (SAP), and explore its predictive efficiency. METHODS: A retrospective cohort study was conducted. The patients with acute pancreatitis (AP) who admitted to the First Affiliated Hospital of Soochow University, the Second Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University from January 1, 2020 to December 31, 2021 were enrolled. Demography information, etiology, past history, and clinical indicators and imaging data within 48 hours of admission were collected according to the medical record system and image system, and the modified CT severity index (MCTSI), Ranson score, bedside index for severity in acute pancreatitis (BISAP) and acute pancreatitis risk score (SABP) were calculated. The data sets of the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University were randomly divided into training set and validation set according to 8 : 2. Based on XGBoost algorithm, the SAP prediction model was constructed on the basis of hyperparameter adjustment by 5-fold cross validation and loss function. The data set of the Second Affiliated Hospital of Soochow University was served as independent test set. The predictive efficacy of the XGBoost model was evaluated by drawing the receiver operator characteristic curve (ROC curve), and compared it with the traditional AP related severity score; variable importance ranking diagram and Shapley additive explanation (SHAP) diagram were drawn to visually explain the model. RESULTS: A total of 1 183 AP patients were enrolled finally, of which 129 (10.9%) developed SAP. Among the patients from the First Affiliated Hospital of Soochow University and Changshu Hospital Affiliated to Soochow University, there were 786 patients in the training set and 197 in the validation set; 200 patients from the Second Affiliated Hospital of Soochow University were used as the test set. Analysis of all three datasets showed that patients who advanced to SAP exhibited pathological manifestation such as abnormal respiratory function, coagulation function, liver and kidney function, and lipid metabolism. Based on the XGBoost algorithm, an SAP prediction model was constructed, and ROC curve analysis showed that the accuracy for prediction of SAP reached 0.830, the area under the ROC curve (AUC) was 0.927, which was significantly improved compared with the traditional scoring systems including MCTSI, Ranson, BISAP and SABP, the accuracy was 0.610, 0.690, 0.763, 0.625, and the AUC was 0.689, 0.631, 0.875, and 0.770, respectively. The feature importance analysis based on the XGBoost model showed that the top ten items ranked by the importance of model features were admission pleural effusion (0.119), albumin (Alb, 0.049), triglycerides (TG, 0.036), Ca²⁺ (0.034), prothrombin time (PT, 0.031), systemic inflammatory response syndrome (SIRS, 0.031), C-reactive protein (CRP, 0.031), platelet count (PLT, 0.030), lactate dehydrogenase (LDH, 0.029), and alkaline phosphatase (ALP, 0.028). The above indicators were of great significance for the XGBoost model to predict SAP. The SHAP contribution analysis based on the XGBoost model showed that the risk of SAP increased significantly when patients had pleural effusion and decreased Alb. CONCLUSIONS A SAP prediction scoring system was established based on the machine automatic learning XGBoost algorithm, which can predict the SAP risk of patients within 48 hours of admission with good accuracy.
Humans ; Pancreatitis ; Acute Disease ; Retrospective Studies ; Hospitalization ; Algorithms