Objective: To construct a nonsuicidal selfinjury (NSSI) risk prediction model for middle school students using different machine learning algorithms and evaluate the models effectiveness, so as to provide guidance for the prevention and control of NSSI in campus. Methods: In March 2023, a total of 3 372 middle and high school students from schools in Nanchang, Fuzhou and Shangrao cities in Jiangxi Province were selected by combining stratified random cluster sampling and convenient sampling methods. Questionnaire surveys were conducted using various instruments including general information questionnaire, Selfesteem Scale, Ottawa Selfinjury Scale, Social Support Assessment Scale, Chinese Version of the Olweus Bullying Questionnaire, Event Attribution Style Scale, Adolescent Resilience Scale, and Adolescent Life Events Scale. Data were divided into training set (n=2 361) and test set (n=1 011) at a ratio of 7∶3, and variables were selected based on univariate and LASSO regression results. Four machine learning algorithms including namely random forest, support vector machine, Logistic regression and XGBoost, were used to construct NSSI risk prediction models, and the models performance was evaluated and compared using metrics including area under curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score. Results: The detection rate of NSSI among middle school students was 34.4%. Univariate analysis showed that there were statistically significant differences in NSSI detection rates among middle school students of different grades, genders, registered residence locations, whether they were class cadres and four types of bullying (physical, verbal, relational bullying and cyberbullying) (χ2=27.17, 15.81, 11.54, 4.63；68.22, 140.63, 77.81, 13.95, P<0.05). NSSI was included as the dependent variable in the LASSO regression model for variable screening, and the results regression identified 10 predictive variables including grade level, selfesteem, subjective support, support utilization, verbal bullying, emotional control, interpersonal relationships, punishment, loss of relatives and property, and health and adaptation issues. The AUC values of random forest, support vector machine, Logistic regression, and XGBoost algorithms were 0.76, 0.76, 0.76 and 0.77, respectively, with no statistically significant differences between pairwise comparisons (Z=-0.59-0.82, P>0.05). Sensitivity values were 0.62, 0.61, 0.62 and 0.61, respectively. Specificity values were 0.74, 0.78, 0.78 and 0.78, respectively. Positive predictive values were 0.56, 0.59, 0.60 and 0.59, respectively. Negative predictive values were 0.79, 0.79, 0.80 and 0.79, respectively. F1 scores were 0.59, 0.60, 0.61 and 0.60, respectively. Conclusions All four nonsuicidal selfinjury risk prediction models perform well, with the Logistic regression model slightly outperforming the others. Schools and parents should pay attention to the predictive factors corresponding to NSSI, so as to reduce the occurrence of NSSI among middle school students.

The incidence and recurrence rates of urinary stone diseases have remained high recently, and stone analysis is of great significance for further understanding of the pathophysiological processes of urinary stones and to develop effective prevention strategies and precise treatment. Imaging evaluation is the main method of preoperative stone analysis, and dual-energy CT has shown its potential in identifying common main components of stones. The emergence of photon counting spectral CT is expected to achieve accurate analysis of stone components at the pixel level. The intraoperative stone analysis mainly relies on the automatic recognition of endoscopic images, and using machine learning algorithms can more reliably predict common stone composition. It is of great significance for stone analysis and assessment of metabolic causes by introducing morpho-constitutional classification (MCC)and observing and describing the papillary renal lesions during operation. This article reviews the progress of preoperative and intraoperative stone analysis, in order to improve clinicians' understanding of the importance of stone analysis, and provide a direction for further clinical research.

Objective:To investigate the diagnostic accuracy of serological indicators and evaluate the diagnostic value of a new established combined serological model on identifying the minimal hepatic encephalopathy (MHE) in patients with compensated cirrhosis.Methods:This prospective multicenter study enrolled 263 compensated cirrhotic patients from 23 hospitals in 15 provinces, autonomous regions and municipalities of China between October 2021 and August 2022. Clinical data and laboratory test results were collected, and the model for end-stage liver disease (MELD) score was calculated. Ammonia level was corrected to the upper limit of normal (AMM-ULN) by the baseline blood ammonia measurements/upper limit of the normal reference value. MHE was diagnosed by combined abnormal number connection test-A and abnormal digit symbol test as suggested by Guidelines on the management of hepatic encephalopathy in cirrhosis. The patients were randomly divided (7∶3) into training set ( n=185) and validation set ( n=78) based on caret package of R language. Logistic regression was used to establish a combined model of MHE diagnosis. The diagnostic performance was evaluated by the area under the curve (AUC) of receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve. The internal verification was carried out by the Bootstrap method ( n=200). AUC comparisons were achieved using the Delong test. Results:In the training set, prevalence of MHE was 37.8% (70/185). There were statistically significant differences in AMM-ULN, albumin, platelet, alkaline phosphatase, international normalized ratio, MELD score and education between non-MHE group and MHE group (all P<0.05). Multivariate Logistic regression analysis showed that AMM-ULN [odds ratio ( OR)=1.78, 95% confidence interval ( CI) 1.05-3.14, P=0.038] and MELD score ( OR=1.11, 95% CI 1.04-1.20, P=0.002) were independent risk factors for MHE, and the AUC for predicting MHE were 0.663, 0.625, respectively. Compared with the use of blood AMM-ULN and MELD score alone, the AUC of the combined model of AMM-ULN, MELD score and education exhibited better predictive performance in determining the presence of MHE was 0.755, the specificity and sensitivity was 85.2% and 55.7%, respectively. Hosmer-Lemeshow test and calibration curve showed that the model had good calibration ( P=0.733). The AUC for internal validation of the combined model for diagnosing MHE was 0.752. In the validation set, the AUC of the combined model for diagnosing MHE was 0.794, and Hosmer-Lemeshow test showed good calibration ( P=0.841). Conclusion:Use of the combined model including AMM-ULN, MELD score and education could improve the predictive efficiency of MHE among patients with compensated cirrhosis.

Objective:To compare the differences in the prevalence of mild micro-hepatic encephalopathy (MHE) among patients with cirrhosis by using the psychometric hepatic encephalopathy score (PHES) and the Stroop smartphone application (Encephal App) test.Methods:This prospective, multi-center, real-world study was initiated by the National Clinical Medical Research Center for Infectious Diseases and the Portal Hypertension Alliance and registered with International ClinicalTrials.gov (NCT05140837). 354 cases of cirrhosis were enrolled in 19 hospitals across the country. PHES (including digital connection tests A and B, digital symbol tests, trajectory drawing tests, and serial management tests) and the Stroop test were conducted in all of them. PHES was differentiated using standard diagnostic criteria established by the two studies in China and South Korea. The Stroop test was evaluated based on the criteria of the research and development team. The impact of different diagnostic standards or methods on the incidence of MHE in patients with cirrhosis was analyzed. Data between groups were differentiated using the t-test, Mann-Whitney U test, and χ2 test. A kappa test was used to compare the consistency between groups. Results:After PHES, the prevalence of MHE among 354 cases of cirrhosis was 78.53% and 15.25%, respectively, based on Chinese research standards and Korean research normal value standards. However, the prevalence of MHE was 56.78% based on the Stroop test, and the differences in pairwise comparisons among the three groups were statistically significant (kappa = -0.064, P < 0.001). Stratified analysis revealed that the MHE prevalence in three groups of patients with Child-Pugh classes A, B, and C was 74.14%, 83.33%, and 88.24%, respectively, according to the normal value standards of Chinese researchers, while the MHE prevalence rates in three groups of patients with Child-Pugh classes A, B, and C were 8.29%, 23.53%, and 38.24%, respectively, according to the normal value standards of Korean researchers. Furthermore, the prevalence rates of MHE in the three groups of patients with Child-Pugh grades A, B, and C were 52.68%, 58.82%, and 73.53%, respectively, according to the Stroop test standard. However, among the results of each diagnostic standard, the prevalence of MHE showed an increasing trend with an increasing Child-Pugh grade. Further comparison demonstrated that the scores obtained by the number connection test A and the number symbol test were consistent according to the normal value standards of the two studies in China and South Korea ( Z = -0.982, -1.702; P = 0.326, 0.089), while the other three sub-tests had significant differences ( P < 0.001). Conclusion:The prevalence rate of MHE in the cirrhotic population is high, but the prevalence of MHE obtained by using different diagnostic criteria or methods varies greatly. Therefore, in line with the current changes in demographics and disease spectrum, it is necessary to enroll a larger sample size of a healthy population as a control. Moreover, the establishment of more reliable diagnostic scoring criteria will serve as a basis for obtaining accurate MHE incidence and formulating diagnosis and treatment strategies in cirrhotic populations.

Background: The benefits and risks of eradicating Helicobacter pylori (Hp) should be balanced in aged people. Aims: To investigate the efficacy, safety and antibiotic resistance status of Hp eradication treatment in the elderly. Methods: Four cohorts of subjects who received Hp eradication treatment in randomized controlled trials conducted in Chongqing Daping Hospital were reviewed; the demographic and clinical data were extracted and recorded. Propensity score matching was performed to select comparable elderly group (≥60 years old) and non-elderly group (<60 years old). The eradication rate, compliance, adverse events, antibiotic resistance, and the influencing factors of eradication were analyzed in these two groups. Results: A total of 994 subjects receiving Hp eradication treatment were enrolled initially, among them, 224 were categorized as elderly group and non-elderly group by propensity score matching, with 122 subjects in each group. The Hp eradication rates were higher than 90% in both elderly and non-elderly groups, and no significant differences were observed in compliance, drug-related adverse events, and antibiotic resistance rates between these two groups (all P>0.05). Furthermore, there were no significant differences in the Hp eradication rates in terms of gender, cigarette smoking, alcohol consumption, comorbidities (hypertension, diabetes, surgical history, and inflammation), compliance, etc. in the elderly group (all P> 0.05). But in retreated elderly subjects, the resistance rate of clarithromycin was higher than that in naïve elderly subjects (P<0.05). Smoking and poor compliance were identified as independent risk factors for Hp eradication by multivariate Logistic regression analysis. Conclusions: Hp eradication in the elderly demonstrates comparable efficacy and safety with non-elderly subjects. Clarithromycin-containing regimens are not recommended for empirical retreatment in aged people.

Objective:To investigate the preference for Front-of-Pack Labeling (FOP) and its association with the understanding of the Nutrition Facts Panel among Chinese residents.Methods:A multi-stage sampling strategy was adopted to select 3 002 people aged between 18 and 70 years old from the eastern region of China (Beijing, Jiangsu Province, Guangdong Province), the northeast region (Heilongjiang Province), the central region (Henan Province) and the western region (Sichuan Province) from July 2020 to March 2021. Socio-demographic characteristics of participants and their understanding of the Nutrition Facts Panel and preference for FOP were collected. The χ2 test was conducted to compare the preference for FOP in different groups of population, and multivariate logistic regression was used to analyze the association between the preference for FOP and the understanding of the Nutrition Facts Panel. Results:The mean age of 3 002 participants was (42.3±13.4) years, of which 1 914 (63.8%) were females and 69.3% could not understand the Nutrition Facts Panel. About 2 458 respondents (81.9%) suggested that FOP could be promoted. The top three nutrients that should be labeled were sugar (68.4%), salt (68.2%) and total fat (62.4%). The number of participants who believed that the Multiple Traffic Lights (MTL) could be easier to help consumers to quickly choose healthy food, attract attention and provide the most needed information was 1 064 (35.4%), 1 026 (34.2%) and 1 140 (38.0%), respectively. The multivariate logistic regression analysis showed that, compared with the Guideline Daily Amount (GDA) system, participants who could not understand the Nutrition Facts Panel preferred (1) Nutri-Score, Warning labels, and Health logos: Smart Choice in terms of"Which format of FOP could quickly help you choose food more easily?"[ OR (95% CI): 2.21 (1.62-3.02), 1.64 (1.22-2.22), 1.79 (1.31-2.45), respectively]; (2) Nutri-Score, Warning labels, and Health logos: Smart Choice in terms of"Which format of FOP could attract your attention the most?"[ OR (95% CI): 2.62 (1.92-3.59), 1.96 (1.45-2.66), 2.25 (1.66-3.04), respectively]; and (3) Nutri-Score, Warning labels, and Health logos: Smart Choice in terms of"Which format of FOP could provide you with the most needed information?"[ OR (95% CI): 2.33 (1.70-3.21), 2.21 (1.66-2.95), 2.01 (1.50-2.71), respectively]. Conclusion:The residents from six provinces in China have a supportive attitude towards FOP. The interpretive FOP with color information, specific nutrient information and summary indicator can be launched. The nutrition information of sugar, salt and total fat could be prioritized to be labeled on the FOP.

Objective:To investigate the preference for Front-of-Pack Labeling (FOP) and its association with the understanding of the Nutrition Facts Panel among Chinese residents.Methods:A multi-stage sampling strategy was adopted to select 3 002 people aged between 18 and 70 years old from the eastern region of China (Beijing, Jiangsu Province, Guangdong Province), the northeast region (Heilongjiang Province), the central region (Henan Province) and the western region (Sichuan Province) from July 2020 to March 2021. Socio-demographic characteristics of participants and their understanding of the Nutrition Facts Panel and preference for FOP were collected. The χ2 test was conducted to compare the preference for FOP in different groups of population, and multivariate logistic regression was used to analyze the association between the preference for FOP and the understanding of the Nutrition Facts Panel. Results:The mean age of 3 002 participants was (42.3±13.4) years, of which 1 914 (63.8%) were females and 69.3% could not understand the Nutrition Facts Panel. About 2 458 respondents (81.9%) suggested that FOP could be promoted. The top three nutrients that should be labeled were sugar (68.4%), salt (68.2%) and total fat (62.4%). The number of participants who believed that the Multiple Traffic Lights (MTL) could be easier to help consumers to quickly choose healthy food, attract attention and provide the most needed information was 1 064 (35.4%), 1 026 (34.2%) and 1 140 (38.0%), respectively. The multivariate logistic regression analysis showed that, compared with the Guideline Daily Amount (GDA) system, participants who could not understand the Nutrition Facts Panel preferred (1) Nutri-Score, Warning labels, and Health logos: Smart Choice in terms of"Which format of FOP could quickly help you choose food more easily?"[ OR (95% CI): 2.21 (1.62-3.02), 1.64 (1.22-2.22), 1.79 (1.31-2.45), respectively]; (2) Nutri-Score, Warning labels, and Health logos: Smart Choice in terms of"Which format of FOP could attract your attention the most?"[ OR (95% CI): 2.62 (1.92-3.59), 1.96 (1.45-2.66), 2.25 (1.66-3.04), respectively]; and (3) Nutri-Score, Warning labels, and Health logos: Smart Choice in terms of"Which format of FOP could provide you with the most needed information?"[ OR (95% CI): 2.33 (1.70-3.21), 2.21 (1.66-2.95), 2.01 (1.50-2.71), respectively]. Conclusion:The residents from six provinces in China have a supportive attitude towards FOP. The interpretive FOP with color information, specific nutrient information and summary indicator can be launched. The nutrition information of sugar, salt and total fat could be prioritized to be labeled on the FOP.

Epithelial ovarian cancer (EOC) is one of the leading causes of death from gynecologic cancers and peritoneal dissemination is the major cause of death in patients with EOC. Although the loss of 4.1N is associated with increased risk of malignancy, its association with EOC remains unclear. To explore the underlying mechanism of the loss of 4.1N in constitutive activation of epithelial-mesenchymal transition (EMT) and matrix-detached cell death resistance, we investigated samples from 268 formalin-fixed EOC tissues and performed various in vitro and in vivo assays. We report that the loss of 4.1N correlated with progress in clinical stage, as well as poor survival in EOC patients. The loss of 4.1N induces EMT in adherent EOC cells and its expression inhibits anoikis resistance and EMT by directly binding and accelerating the degradation of 14-3-3 in suspension EOC cells. Furthermore, the loss of 4.1N could increase the rate of entosis, which aggravates cell death resistance in suspension EOC cells. Moreover, xenograft tumors in nude mice also show that the loss of 4.1N can aggravate peritoneal dissemination of EOC cells. Single-agent and combination therapy with a ROCK inhibitor and a 14-3-3 antagonist can reduce tumor spread to varying degrees. Our results not only define the vital role of 4.1N loss in inducing EMT, anoikis resistance, and entosis-induced cell death resistance in EOC, but also suggest that individual or combined application of 4.1N, 14-3-3 antagonists, and entosis inhibitors may be a promising therapeutic approach for the treatment of EOC.

Strain is the fundamental unit in microbial taxonomy. The functional diversity among strains has great influence on host phenotypes. With the development of microbiome research, knowing the composition and functional capacities of complex microbial communities at the strain level has become increasingly valuable in scientific research and clinical applications. This review introduces the principles of bioinformatics algorithms for strain analysis based on metagenomic data, the applications in microbiome research and directions of future development.
Algorithms ; Computational Biology ; Metagenome ; Metagenomics ; Microbiota/genetics*

Myasthenia gravis(MG)is a B cell-mediated,T cell-dependent,complements-involved autoimmune disease.Ocular myasthenia gravis(OMG)is a typical MG,with its symptoms limited to the extraocular muscles.The occurrence and development of a variety of autoimmune diseases including OMG are closely associated with the imbalanced expression of follicular regulatory T cells(Tfr cells).Therefore,Tfr cells may be a new research topic for OMG.
Complement System Proteins ; Humans ; Myasthenia Gravis ; Oculomotor Muscles ; T-Lymphocytes, Regulatory