Objective: To explore the effects of personality and sleep quality with psychological distress of junior and senior high school stduents, so as to provide a reference basis for precise interventions of junior and senior high school students mental health. Methods: In October 2023, a convenience sampling method was used to select 9 034 students aged 12-17 from Shiyan City as the study subjects. The Pittsburgh Sleep Quality Index (PSQI) and Kessler Psychological Distress Scale (K10) were used to collect information on sleep quality and psychological distress of junior and senior high school stduents. Between group comparison was conducted by using t-test and Chi-square test. Generalized linear models were employed to analyze the interaction and joint effects of personality and sleep quality on psychological distress. Results: The generalized linear model analysis showed that the interaction between personality and sleep quality on psychological distress was statistically significant of junior and senior high school students(effect size=0.80, P <0.01). The general linear model analysis indicated that, after adjusting for variables such as age, gender, screen time, and daily sitting time with the extroverted and good sleep quality group as the reference, the introverted and poor sleep quality group had the largest mean difference in psychological distress scores (difference=0.51, P <0.05). When stratified by sleep quality, psychological distress scores were higher in the introverted and neutral personality groups with both poor and good sleep quality compared to the extroverted group (poor sleep quality: introverted difference=3.71, neutral difference=1.14; good sleep quality: introverted difference=2.23, neutral difference=0.57, all P < 0.05). When stratified by personality, psychological distress scores were higher in the poor sleep quality groups for introverted, neutral, and extroverted individuals compared to their good sleep quality counterparts (differences=8.66, 7.83, 7.34, all P < 0.05 ). Conclusions Personality and sleep quality have interactive and joint effects on psychological distress of junior and senior high school stduents. Personalized psychological interventions should be developed based on personality and sleep quality.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

BACKGROUND: The contraction behaviors of cardiomyocytes (CMs), especially contraction synchrony, are crucial factors reflecting their maturity and response to drugs. A wider field of view helps to observe more pronounced synchrony differences, but the accompanied greater computational load, requiring more computing power or longer computational time. METHODS: We proposed a method that directly correlates variations in optical field brightness with cardiac tissue contraction status (CVB method), based on principles from physics and photometry, for rapid video analysis in wide field of view to obtain contraction parameters, such as period and contraction propagation direction and speed. RESULTS: Through video analysis of human induced pluripotent stem cell (hiPSC)-derived CMs labeled with green fluorescent protein (GFP) cultured on aligned and random nanofiber scaffolds, the CVB method was demonstrated to obtain contraction parameters and quantify the direction and speed of contraction within regions of interest (ROIs) in wide field of view. The CVB method required less computation time compared to one of the contour tracking methods, the LucasKanade (LK) optical flow method, and provided better stability and accuracy in the results. CONCLUSION This method has a smaller computational load, is less affected by motion blur and out-of-focus conditions, and provides a potential tool for accurate and rapid analysis of cardiac tissue contraction synchrony in wide field of view without the need for more powerful hardware.

Objective To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

OBJECTIVE To investigate the potential mechanism of Sanqi danshen tablets in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Core targets of Sanqi danshen tablets in the treatment of NAFLD were explored by network pharmacological methods. Gene ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were also performed. Based on the results obtained from network pharmacological studies， using SD rats as subjects， the NAFLD model was induced by feeding them high-fat diet. The effects of Sanqi danshen tablets on pathological changes such as lipid droplet vacuoles and lipid accumulation in the liver tissue of NAFLD rats， as well as its impact on relative indicators of lipid metabolism， inflammatory responses and oxidative stress， were investigated. RESULTS A total of 20 core targets for the treatment of NAFLD with Sanqi danshen tablets were screened， primarily involved in GO functions such as biological regulation， cellular membrane and binding， and enriched in signaling pathways related to inflammatory responses， oxidative stress and lipid metabolism. Compared with the model group， lipid droplet vacuoles were reduced significantly in low-dose， medium-dose， high-dose groups of Sanqi danshen tablets and positive control （simvastatin） group， the number of lipid droplets decreased significantly and the color became lighter. The contents of total cholesterol， triglyceride （except for medium- dose group of Sanqi danshen tablets）， aspartate transaminase， alanine transaminase， tumor necrosis factor-α （except for low-dose group of Sanqi danshen tablets）， interleukin-17 （except for Sanqi danshen tablets groups） and malondialdehyde （except for low- dose group of Sanqi danshen tablets） in liver tissue were significantly decreased， while the content of superoxide dismutase was significantly increased （P＜0.01 or P＜0.05）. CONCLUSIONS Sanqi danshen tablets exert anti-inflammatory， antioxidant and lipid metabolism regulating effects by influencing the levels of inflammation， oxidative stress and lipids metabolism-related indicators， thereby improving NAFLD in rats.

To develop a novel polyamino acid-based nanohydrogel drug delivery system for dexamethasone to enhance its delivery efficiency to the inner ear.

Objective: To analyze the incidence of statutory and keymonitored infectious diseases among school students in Beijing from 2016 to 2020, so as to provide a reference for developing the prevention and control of infectious diseases in schools. Methods: A descriptive statistical analysis was conducted on student cases aged 6-22 years in Beijing from 2016 to 2020 selected from the China Disease Surveillance Information Reporting Management System. Rate comparisons were performed using the 2 test and trend 2 test. Results: From 2016 to 2020, the overall incidence of statutory and keymonitored infectious diseases among students in Beijing showed an upward trend (χ2trend=582.42), the incidence rates of Category B and other infectious diseases exhibited a downward trend (χ2trend=82.71, 18.34), while Category C infectious diseases demonstrated a significant upward trend (χ2trend=911.75) (P<0.01). Among Category B infectious diseases, scarlet fever, bacillary dysentery, tuberculosis, and HIV/AIDS were predominant, with annual average incidence rates of 61.33/100 000, 35.38/100 000, 13.88/100 000, and 3.78/100 000, respectively. Except for HIV/AIDS, the reported incidence rates of other infectious diseases showed a declining trend. Among Category C infectious diseases, influenza, other infectious diarrhea, hand-foot-mouth disease, and mumps were predominant, with annual average incidence rates of 956.13/100 000, 114.39/100 000, 111.37/100 000, and 28.24/100 000, respectively. Influenza showed a significant upward trend (χ2trend=1 508.30), while the other infectious diarrhea, hand-foot-mouth disease, and mumps exhibited a downward trend (χ2trend=13.84, 25.78, 6.13) (P<0.05). Among other infectious diseases, varicella was predominant (χ2trend=17.47, P<0.05). Scarlet fever, influenza, hand-foot-mouth disease, and mumps had higher incidence rates among primary and middle school students; other infectious diarrhea and varicella were more prevalent among high school students; tuberculosis and bacillary dysentery were more common among high school and college students; and HIV/AIDS had higher incidence rates among college and high school students. Conclusion From 2016 to 2020, the incidence of Category B infectious diseases among students in Beijing showed a declining trend, while influenza, a Category C infectious disease, exhibited a significant upward trend.

[摘 要] 目的：探究钾钙激活通道亚家族N成员4（KCNN4）在胰腺癌组织中的表达及其对胰腺癌进展的影响，解析KCNN4在胰腺癌临床诊断及预后判断中的作用。方法：利用GEPIA2数据分析平台，结合TCGA和GTEx数据库的数据分析KCNN4在胰腺癌组织中的表达水平及其与患者预后的关系。收集24例海军军医大学长海医院手术切除的胰腺癌患者的癌及癌旁组织标本，通过qPCR、WB法和免疫组化染色技术验证KCNN4在胰腺癌组织中的表达水平。利用shRNA敲低人胰腺癌细胞中BXPC3和PANC-1中KCNN4的表达，通过CCK-8和克隆形成实验检测细胞增殖与生长情况。利用小鼠胰腺癌KPC细胞构建胰腺癌原位成瘤模型，观察敲低KCNN4对胰腺原位成瘤的影响，统计小鼠生存期（OS）。结果：整合TCGA和GTEx数据库数据分析结果发现，KCNN4在胰腺癌组织中高表达（P < 0.05），且与患者OS和DFS缩短相关（均P < 0.05）。胰腺癌组织中KCNN4 mRNA和蛋白表达量均显著高于癌旁组织（均P < 0.01）。KCNN4敲低后，胰腺癌细胞生长速率显著减慢、克隆形成数量显著减少（均P < 0.01）。小鼠胰腺原位荷瘤实验结果表明，KCNN4敲低可抑制肿瘤细胞在胰腺原位的生长并延长小鼠OS。结论：KCNN4在胰腺癌组织中高表达，其能促进胰腺癌细胞增殖和胰腺癌进展，与患者预后密切相关，有望作为胰腺癌临床诊断及预后评估的靶点。