Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.

Objective:To investigate the detection status of pulmonary tuberculosis (PTB) among patients aged ≥65 years in China and provide evidence for improving PTB prevention and control in this population.Methods:The data were collected from the tuberculosis subsystem of Chinese Disease Control and Prevention Information System, and the case information of elderly PTB patients aged ≥65 years old who were registered in designated tuberculosis medical institutions nationwide from January 1, 2015 to December 31, 2023.Descriptive epidemiological methods were used to analyze trends in detection status, regional differences, and demographic characteristics.Results:From 2015 to 2023, 1 567 047 elderly PTB detection were identified, accounting for 25.1% of all PTB patients (1 567 047/6 243 215). The average registration rate for elderly PTB patients was 96.9 per 100 000, approximately twice that of the general population. The registration rate declined over the years ( Z=-2.61, P=0.009) but increased in 2018 and 2023. The proportion of elderly PTB patients rose annually, from 21.3% in 2015 to 32.4% in 2023 ( Z=2.30, P=0.022). Active case-finding accounted for only 3.0% (47 049/1 567 047) of patients on average during the study period, peaking at 7.3% (14 123/194 615) in 2018 before declining. The registration rates of elderly tuberculosis patients are relatively higher in central and western regions. In the central region, the average registration rate was 113.8 per 100 000, with a proportion of active case detection of 0.4% (2 532/570 059). In the western region, the average registration rate was 130.0 per 100 000, and the proportion of active case-finding was 7.6% (41 973/549 998). Subgroups with notably lower active detection proportions included males (2.5%, 27 443/1 101 091), those aged 80-84 years (2.2%, 2 978/133 855), and migrant populations (0.5%, 1 635/307 673). Conclusions:The burden of PTB among the elderly aged ≥65 years in China remains high, with a low proportion of active case-finding from 2015-2023. There is an urgent need to strengthen health education and active screening to improve the early diagnosis and prevention of tuberculosis in the elderly.

Objective:To investigate the detection status of pulmonary tuberculosis (PTB) among patients aged ≥65 years in China and provide evidence for improving PTB prevention and control in this population.Methods:The data were collected from the tuberculosis subsystem of Chinese Disease Control and Prevention Information System, and the case information of elderly PTB patients aged ≥65 years old who were registered in designated tuberculosis medical institutions nationwide from January 1, 2015 to December 31, 2023.Descriptive epidemiological methods were used to analyze trends in detection status, regional differences, and demographic characteristics.Results:From 2015 to 2023, 1 567 047 elderly PTB detection were identified, accounting for 25.1% of all PTB patients (1 567 047/6 243 215). The average registration rate for elderly PTB patients was 96.9 per 100 000, approximately twice that of the general population. The registration rate declined over the years ( Z=-2.61, P=0.009) but increased in 2018 and 2023. The proportion of elderly PTB patients rose annually, from 21.3% in 2015 to 32.4% in 2023 ( Z=2.30, P=0.022). Active case-finding accounted for only 3.0% (47 049/1 567 047) of patients on average during the study period, peaking at 7.3% (14 123/194 615) in 2018 before declining. The registration rates of elderly tuberculosis patients are relatively higher in central and western regions. In the central region, the average registration rate was 113.8 per 100 000, with a proportion of active case detection of 0.4% (2 532/570 059). In the western region, the average registration rate was 130.0 per 100 000, and the proportion of active case-finding was 7.6% (41 973/549 998). Subgroups with notably lower active detection proportions included males (2.5%, 27 443/1 101 091), those aged 80-84 years (2.2%, 2 978/133 855), and migrant populations (0.5%, 1 635/307 673). Conclusions:The burden of PTB among the elderly aged ≥65 years in China remains high, with a low proportion of active case-finding from 2015-2023. There is an urgent need to strengthen health education and active screening to improve the early diagnosis and prevention of tuberculosis in the elderly.

Objective To construct a machine learning algorithm using biomarkers to predict the risk of sepsis-induced respiratory tract infection in order to assist clinicians in making decisions.Methods Based on the diagnostic criteria of the research subjects,and the basic clinical data of the participants were collected.The data set was randomly split into a training set(80％)and a validation set(20％).Use feature filtering algorithms to select the best subset of variables from the training set,and use this subset to construct random forest(RF),extreme gradient boosting(XGBoost),adaptive boosting(AdaBoost),Logistic regression(LR),ridge regression(Ridge),and support vector machine(SVM)classifiers.Then,evaluate the model's generalization ability using a validation dataset.Evaluate the performance of the model comprehensively through accuracy,precision,recall,and area under the curve.Results A total of 377 patients with sepsis-induced respiratory tract infection(case group)and 564 patients with respiratory tract infection(control group)were included,and 17 variables were found to be suitable for the initial model construction.Using feature screening algorithm,we found that the predictive performance of tree models(RF,XGboost,and AdaBoost)was better than that of linear models(LR,SVM,and Ridge).The AdaBoost model included 14 biomarkers,and its prediction accuracy was better than RF,XGBoost,LR,SVM,Ridge models,its precision,recall,accuracy and area under the curve were 0.90,0.84,91.75％and 0.950,respectively.The Ridge model had the worst prediction performance,with an accuracy of 82.97％,its precision,recall and area under the curve were 0.90,0.72 and 0.835 respectively.Conclusion In this study,six predictive models of sepsis-induced respiratory tract infection were developed,among which AdaBoost model could more accurately predict the risk of sepsis-induced respiratory tract infection and help to assist clinical decision-making.

Chemogenetic technology is a receptor-ligand system that regulates cell viability and function by changing receptor specificity and affinity, and it achieves precise neuronal regulation by specifically regulating neurons and neural circuits. At present, this technique is widely used in the study of neural circuits. This article briefly describes the application and progress of chemogenetic technology in the study of depression neural circuits, reviews the application of chemogenetic technology in several brain regions closely related to depression, such as ventral tegmental area, nucleus accumbens, prefrontal cortex, hippocampus and lateral habenula, and discusses the potential and challenges of chemogenetic technology as a technology for precise regulation of neural activity in future research, in order to provide reliable ideas and directions for chemogenetic technology in the study of depression neural circuits.

Objective To investigate the dynamic change of fatty liver (FL) in patients with fatty liver-related acute pancreatitis (FLAP) and related influencing factors. Methods A total of 136 FLAP patients who were admitted to The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, from December 2017 to December 2021 were enrolled as subjects. With the first CT examination after admission as the starting point and the last CT examination before discharge as the ending point, the subjects were divided into FL aggravation group (FLAG group), FL mild mitigation group (FLMMG group), and FL significant mitigation group (FLSMG group) according to the degree of change in FL. General information and clinical data were compared between groups. General information included age, sex, length of hospital stay, etiology of acute pancreatitis (AP), and changes in body weight and temperature, and clinical data included treatment modality and the changes in laboratory markers and AP severity. The chi-square test or the Fisher's exact test was used for comparison of categorical data between groups. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups, and the least significant difference t -test was used for further comparison between two groups; the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups, and the Mann-Whitney U test with Bonferroni correction was used for further comparison between two groups. Dynamic changes of data were expressed as the difference from the starting point to the ending point, and a covariance analysis was used for comparison of data with dynamic changes. The Spearman correlation analysis was used to investigate the correlation of indices with significant changes with the degree of change in FL. Results Among the 136 FLAP patients, 61 achieved mild mitigation of FL, 59 achieved significant mitigation of FL, and 16 experienced aggravation of FL at the ending point of the study. There were significant differences between the three groups in the length of hospital stay ( χ 2 =16.215, P < 0.001) and the change in body weight ( F =3.908, P < 0.05), and the FLSMG group had a greater reduction in body weight and a longer length of hospital stay. There were also significant differences between the three groups in the number of fasting days ( χ 2 =11.020, P =0.004) and the degree of changes in C-reactive protein (CRP) ( F =8.589, P < 0.001), white blood cell count (WBC) ( F =5.448, P =0.005), and CT severity index (CTSI) ( F =7.544, P =0.001), and the FLSMG group had greater reductions in CRP, WBC, and CTSI and a longer duration of fasting. Length of hospital stay, number of fasting days, and changes in CRP and CTSI were significantly correlated with the change in FL ( r =0.352, 0.372, -0.365, and -0.350, all P < 0.001). Conclusion Most FLAP patients tend to have mitigation of FL, and its dynamic changes are closely associated with the changes in CRP and CTSI.

Objective Using CT three-dimensional image technique to observe the pulmonary vein stenosis of circumferential pulmonary vein ablation (CPVA) for atrial ifbrillation (AF) on the structure of pulmonary vein before and after radiofrequency ablation. Methods 28 patients with AF who underwent CPVA were followed-up for a mean (6.5±3.9) months.The results of Pulmonary vein morphology study was compared with analysis of preablation, after following up radiofrequency catheter alation (6.5±3.9) months. Pulmonary vein diameters, cross-sectional area and left atrial volume were measured before and after CPVA using 64-slice multidector computed tomography (CT). Results Mild stenosis of pulmonary vein maximum diameter and pulmonary minimum diameter were 61.6%and 56.3%after CPVA. Moderate stenosis of pulmonary vein maximum diameter and pulmonary minimum diameter were 3.6%and 5.4%. All patients does not present symptoms of pulmonary vein stenosis at rest on during excercise during follow up. Conclusions Mild and moderate asymptomatic pulmonary vein stenosis may present in some patients after CPVA.