Psychotropic medication plays a crucial role in the field of mental illness,and the issues of drug efficacy and safety due to individual differences cannot be ignored.Genetic factors,especially the genetic poly-morphisms related to drug-metabolizing enzymes,drug action targets,and risk,have a significant impact on drug responses.Pharmacogenomics,by detecting genetic polymorphisms,can reveal a patient's inherited tend-encies towards drug efficacy,pharmacokinetic characteristics,and potential toxicity,thereby predicting the therapeutic effects and adverse reactions of drug treatment,and providing guidance for personalized therapy.Therefore,individualized medication based on pharmacogenomics helps to improve cure rates,reduce relapse rates,and decrease medical costs,which is of great significance to clinical medication in mental illness.

This study collected data on 9 cases of fatal head injuries caused by adjustable wrenches and analyzed 16 types of injury characteristics of head injuries caused by adjustable wrenches.The distinctive patterns of head injuries caused by adjustable wrenches were summarized.A four level scoring model and scoring criteria were constructed for head injuries caused by adjustable wrenches.From a morphological perspective,it provided a reference for the inference of injury-causing instruments in forensic practice.

This study collected data on 9 cases of fatal head injuries caused by adjustable wrenches and analyzed 16 types of injury characteristics of head injuries caused by adjustable wrenches.The distinctive patterns of head injuries caused by adjustable wrenches were summarized.A four level scoring model and scoring criteria were constructed for head injuries caused by adjustable wrenches.From a morphological perspective,it provided a reference for the inference of injury-causing instruments in forensic practice.

Objective To investigate the predictive factors for death within 30 days after admission in patients with decompensated liver cirrhosis and acute kidney injury(AKI),and to establish and validate a nomogram prediction model.Methods The Joint Medical Record Management System of The First Affiliated Hospital of Nanchang University was used to obtain the patients with decompensated liver cirrhosis who were hospitalized in Department of Gastroenterology and Department of Infectious Diseases from January 2015 to December 2020,among whom 330 patients who met the 2015 International Club of Ascites diagnostic criteria for AKI were enrolled and divided into training group with 193 patients and validation group with 137 patients.A Cox regression analysis was used to investigate the predictive factors for death,and then a nomogram prediction model for the risk of death within 30 days after admission was established and validated.The independent-samples t-test was used for comparison of normally distributed continuous data between two groups,and a one-way analysis of variance was used for comparison between multiple groups,while the least significant difference t-test was used for further comparison between two groups;The Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups,while the Kruskal-Wallis H test was used for comparison between multiple groups.The chi-square test or the Fisher's exact test was used for comparison of categorical data between groups.Results The prevalence rate of AKI was 16.5%in patients with decompensated liver cirrhosis.The 330 patients included in the study had a mean age of 53.6±12.4 years,and male patients accounted for 79.1%.The mortality rate was 50.0%within 30 days after admission,with a mortality rate of 46.6%in the training group and 54.7%in the validation group.The presence of acute-on-chronic liver failure(ACLF)on admission was an independent risk factor for the progression of AKI into stage 1(odds ratio=2.571,95%confidence interval:1.143-5.780,P=0.022).The nomogram based on white blood cell count,international normalized ratio,presence or absence of hepatic encephalopathy,and AKI stage on admission could well predict the risk of death with 30 days after admission,with a C-index of 0.680 in the training group and 0.683 in the validation group,and it was not inferior to CTP score and MELD score.Conclusion ACLF is an independent risk factor for the progression of AKI into stage 1.The nomogram prediction model established in this study can effectively predict the risk of death within 30 days after admission and thus has important guiding significance for the early identification and management of patients with decompensated liver cirrhosis and AKI.

Objective To observe the temporary loss(re-falling)of knee range of motion(ROM)during rehabilitation after arthroly-sis for post-traumatic knee stiffness,and analyze the factors related to it. Methods From July,2016 to September,2019,64 patients(68 knees)in Beijing Bo'ai Hospital accepted minimally inva-sive arthrolysis for post-traumatic knee stiffness,and were followed up for twelve months.ROM of flexion and extension of knee was measured before operation,and one,six and twelve weeks,and six and twelve months af-ter operation;while they were asssessed with Hospital for Special Surgery Knee Score(HSS).Multivariate Logis-tic regression was performed on re-falling. Results The ROM and HSS score improved as a whole after operation;however,HSS score improved constantly,but ROM decreased six weeks after operation compared with that one weeks after operation,involving 46 knees of 44 cases.Arthrolysis longer than 12 months from primary injuries,multiple complicated fracture and history of infection were the risk factors for re-falling(OR＞8.058,P＜0.05). Conclusion Minimally invasive arthrolysis is effective on knee function after arthrolysis for post-traumatic knee stiff-ness.However,re-falling of ROM may happen during rehabilitation.Delay of operation,multiple complicated fracture and history of infection may increase the risk of re-falling.

[Objective] To explore the characteristics of lifestyle behaviors and mental health status among blood donors in Guangzhou, and to investigate the correlation between donation frequency and these factors. [Methods] A cross-sectional study was conducted among 13 042 whole blood donors from 17 street blood donation sites of Guangzhou Blood Center from May to August 2020. Descriptive analysis was used to describe the characteristics of lifestyle behaviors and mental health status among blood donors in Guangzhou. Ordinal multinomial logistic regression model was used to analyze the correlation between donation frequency and these factors. [Results] It was found that some of 13 042 blood donors had unhealthy habits, such as 6.8% (698/10 214，2 828 missing values) had severe tobacco dependence, 30.6% (3 997/13 042) had low exercise levels, 38.8%（5 056/13 042）had poor sleep quality, and 2.2% (271/12 159，883 missing values) had alcohol dependence. In addition, 2.8% (364/13 042) and 1.3% (172/13 042) of the donors may have moderate to severe depression and anxiety symptoms, respectively. The results of the ordinal multinomial logistic regression model showed that exercise level was significantly negatively correlated with the degree of depression and anxiety among blood donors. With the decrease in exercise level, the possibility of depression and anxiety among donors increased significantly. BMI, household income, education level, marital status, donation frequency, alcohol consumption and smoking had no significant correlation with the mental health status of donors. [Conclusion] Improving the exercise habits of blood donors may help enhance their mental health level. It is recommended that blood station staff strengthen the content of exercise when providing health education to blood donors to maintain a healthy lifestyle. It also suggests that there may be a certain degree of under-diagnosis of mental health problems in the process of health consultation before blood donation, and conducting more comprehensive and effective mental health assessments for blood donors is recommended.

Objective To verify the genetic characteristics associated with gastric cancer,and to propose a hybrid feature selection method for identifying target genes,further analyzing their significance and establishing a new diagnostic prediction model.Methods Analysis of variance in bioinformatics was performed on the original gastric cancer data,and then machine learning methods such as random forest,recursive feature elimination of support vector machine,and LASSO algorithm were used to screen gastric cancer associated genes,and the intersection of results was taken as the key gene set.The key genes were identified and verified through enrichment analysis.The diagnosis and prediction models based on 8 kinds of machine learning classification algorithms such as multi-layer perceptron,logistic regression and decision tree,were constructed using the key genes.Results The key genes selected by the hybrid feature selection method were closely related to the tumorigenesis and development.Eight key genes(TXNDC5,BMP8A,ONECUT2,COL10A1,JCHAIN,INHBA,LCTL and TRIM59)were identified as potential markers of good diagnostic efficacy in gastric cancer.The ROC curve and accuracy results demonstrated that among the 8 classification models,MLP is the best gastric cancer prediction model,with an accuracy of 97.77%,which was 3.83%higher than that of Xgboost gastric cancer prediction model.Conclusion The study identifies 8 key genes for the diagnosis and prevention of gastric cancer,and establishes the optimal prognosis model.

Tumors are serious diseases threatening human health,and the early diagnosis is essential to improve treatment success and patient survival.The study of tumor gene expression data has become a major tool for revealing tumor disease mechanisms,in which artificial intelligence plays an important role.The potential advantages of supervised learning,unsupervised learning and deep learning in tumor prediction and classification are explored from the perspective of machine learning methods.Special attention is paid to the impact of feature selection algorithms on gene screening and their importance in high-dimensional gene expression data.By providing a comprehensive overview of the application and development of artificial intelligence in the analysis of tumor gene expression data,the study aims to provide an outlook for future research directions and promote further development.

Objective To determine the time point when porcine pancreatic elastase(PPE)induced abdominal aortic aneurysm(AAA)reaches the regression phase in mice and observe the histological characteristics of AAA in regression phase.Methods AAAs were induced by transient intraluminal infusion of PPE in C57BL/6J mice.The diameters of the mouse abdominal aortas were measured before PPE infusion and sacrifice time,day 14 for AAA progression phase or day 56 for regression phase after PPE infusion,respectively.The histological characteristics of the aneurysm lesion site on day 14 and day 56 after surgery were compared and analyzed.Results The diameters of the abdominal aortas were significantly increased in both day 14 and day 56 after PPE infusion groups(diameter growth rate 147％and 155％,respectively)as compared to the baseline diameters.In the day 14 group,the infused aortas showed typical AAA characteristics,such as elastin break/degradation,medial smooth muscle cells depletion,and inflammatory cell diffused infiltration.In the day 56 group after PPE infusion,although the artery diameter did not change significantly as compared to the day 14 group,histology showed that elastin was partially repaired,new smooth muscle cells were added to the damaged aorta media,the infiltrated inflammatory cells were significantly subsided,and the adventitia neovascularization was reduced,showing a significant feature of the disease regression phase.Conclusion In the PPE-induced mouse AAA model,day 56 after surgery is an appropriate time point for observing aneurysm regression,and the histological characteristics of the regression are obvious.

Objective:To explore the application value of Time-Driven Activity-Based-Costing (TD-ABC) under Diagnosis Related Groups (DRG) payment mode in department cost management of public hospitals. Methods:The medical project cost of obstetrics department in Baise Maternal and Child Health Hospital in Guangxi was selected as the research object between January and December 2023. The medical service quality and department cost management were studied to explore the applicability,effectiveness and potential problems of TD-ABC under DRG payment mode so as to provide practical experiences and theoretical support for cost management and improvement of medical service quality in public hospitals. The cost management in obstetrics department of Baise Maternal and Child Health Hospital in Guangxi after implementation of TD-ABC under DRG payment mode was evaluated,and reform results of medical insurance payment mode were presented to provide data support for improving medical service quality and reducing department cost. Results:In Obstetrics Department of Baise Maternal and Child Health Hospital in Guangxi between March 2023 and March 2024,the total cost was 12.6826 million yuan,and labor cost was the highest. In all the 16 main medical service items,there were 10 profitable items and 6 defective items. The item with the worst yield rate was puncture,followed by obstetric examination,medical registration,consultation of difficult cases,discharge nursing and preoperative preparation. Conclusion:As a new cost management method,TD-ABC has broad application prospect and practical value in public hospitals. Application of TD-ABC under DRG payment mode in cost management can control cost more effectively,optimize allocation of resources and improve quality of medical service.