Objective To analyze the factors affecting the prognosis of patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) undergoing immunotherapy and construct an individualized prognostic nomogram prediction model. Methods A retrospective analysis was conducted on the clinical data of 385 patients with driver gene-negative, locally advanced or metastatic NSCLC who received first-line immune checkpoint inhibitors. Univariate and multivariate Cox regression analyses were used to identify prognostic risk factors, and a prognostic nomogram model was established. The predictive performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC), and calibration curves. The cutoff value of the nomogram was calculated to stratify patients by risk. Survival curves were calculated by Kaplan-Meier analysis. Results Age (HR=1.775, 95%CI: 1.265-2.490), degree of differentiation (HR=0.365, 95%CI: 0.257-0.519), low PD-L1 expression (HR=0.661, 95%CI: 0.455-0.960), high PD-L1 expression (HR=0.423, 95%CI: 0.297-0.603), SCC-Ag (HR=1.549, 95%CI: 1.109-2.163), and CONUT score (HR=2.527, 95%CI: 1.797-3.554) were independent risk factors affecting overall survival (OS) of patients with NSCLC undergoing immunotherapy. The nomogram prediction model constructed on the basis of these factors had a C-index of 0.767. Time-dependent ROC curves for survival showed that the AUCs for 1-, 2-, and 3-year OS were 0.830, 0.853, and 0.886, respectively. Calibration curves indicated that the nomogram-predicted survival rates were in good agreement with the actual outcomes. The cutoff value for the study’s nomogram prediction model was 136.60 points, and survival curves showed statistically significant differences between different risk groups (P<0.05). Conclusion The nomogram model established in this study can effectively predict the prognosis of patients with driver gene-negative locally advanced or metastatic NSCLC treated with first-line immunosuppressive therapy. It provides a new tool for assessing prognosis and aids clinicians in formulating individualized treatment plans.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective To evaluate the application value of artificial intelligence (AI) in the quality control of mammographic images and explore its feasibility for improving image quality. Methods A retrospective analysis was conducted on 500 mammographic images from 125 female patients. These images were acquired in December 2024 at the Department of Radiology, Obstetrics and Gynecology Hospital of Tongji University by two junior technologists with qualification certificate for junior radiologic technologists and ≤ 2 months of independent operation. The reference standard was the evaluation by a panel of senior experts comprising one associate chief radiologist and two intermediate radiologic technologists with over 10 years of experience in mammography. The evaluation was based on nine criteria within an AI quality control system. The correlation and consistency (Kappa test) of the assessment outcomes were compared among the junior technologist group, the AI quality control group, and the panel group. Additionally, the differences in the proportions of high-, medium-, and low-quality images rated by the three groups were analyzed. After one month of AI assistance, 200 images from 50 additional patients acquired by the same two junior technologists in January 2025 were collected. The area under the receiver operating characteristic curve and 95% confidence interval (95%CI) were calculated for the assessment performance of the junior technologist group before and after AI assistance. Differences were compared using the DeLong test. Changes in the disqualification rates for the nine criteria were analyzed. Results In consistency analysis, the AI quality control group showed high consistency with the panel group across multiple key criteria, with Kappa values ranging from 0.41 to 1.00. In contrast, the consistency between the junior technologist group and the panel group was generally low, with Kappa values ranging from 0.13 to 0.49. In comparison of image quality classification, no significant differences were observed in image quality classification between the AI group and the panel group (P>0.05). However, the proportion of images rated as high quality by the junior technologist group was significantly higher, while the proportions rated as medium and low quality were significantly lower, compared to those rated by the panel group (P<0.05). After AI assistance, the area under the receiver operating characteristic curve for the assessment performance of junior technologist group increased significantly from 0.56 (95%CI: 0.51-0.61) to 0.91 (95%CI: 0.87-0.94) (P<0.001). The disqualification rates for skin folds, incomplete inclusion of the pectoralis major muscle, and nipple not in profile decreased significantly. Conclusion AI demonstrates assessment capability comparable to the panel group in mammographic quality control. AI effectively enhances the quality of images acquired by junior technologists through real-time, objective feedback.

Objective: To develop and compare multiple machine learning models for identifying high-risk college students exhibiting phubbing behavior and to determine key predictive factors, so as to provide evidence for precise screening and early intervention strategies. Methods: In December 2025, 1 828 undergraduate students, selected from three universities in Wuhan by using convenience sampling method, were surveyed via online qustionnaire for sociodemographic characteristics, phubbing behavior, family cohesion index, mature happiness, fear of negative evaluation, multidimensional state boredom, and psychological vulnerability. Four machine learning algorithms, including multilayer perceptron (MLP), extreme gradient booting (XGBoost), K-nearest neighbors (KNN), and gradient boosting decision tree (GBDT), were applied. The dataset was randomly split into a training set and a test set at a ratio of 7∶3. Model performance was evaluated using accuracy, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The best performing model was further interpreted using shapley additive explanation (SHAP) analysis to assess feature importance. Results: In the training set,955 participants were classified as having low level phubbing behavior, and 325 participants were classified as having high level phubbing behavior. Significant differences were observed in psychological vulnerability, multidimensional state boredom, family cohesion index, and mature happiness between groups ( Z =-10.29, -11.72, -8.17, -7.83, all P <0.05). Significant differences were also found in the detection rate of high level phubbing behavior according to family residence, family status, left behind experience,interpersonal relationships,exercise frequency,physical flexibility,and sleep status ( χ 2=12.22, 38.93, 16.90, 44.64, 58.17, 82.14, 37.89, all P <0.05). Among the four machine learning models (MLP, KNN, GBDT, XGBoost),all showed good discrimination ability, with XGBoost performing best (accuracy 79%, recall 71%, F1-score=0.70, AUC=0.78), while GBDT achieved the highest precision (78%). Feature importance analysis showed that multidimensional state boredom was the most important predictor of phubbing behavior, followed by psychological vulnerability, family cohesion, mature happiness, and fear of negative evaluation. SHAP analysis indicated that multidimensional state boredom, psychological vulnerability, and fear of negative evaluation had positive risk effects, while family cohesion and hedonic well being had protective effects for high risk of phubbing behavior. Conclusions Multidimensional state boredom is the core predictor of phubbing behavior among college students. The XGBoost model demonstrates good predictive performance and can provide a reference for identifying high risk individuals and implementing targeted interventions.

Objective To compare the efficacy of five Blastocystis staining method for staining cells with different morphologies and in different developmental stages to select appropriate staining techniques for specific scenarios and experimental purposes. Methods Blastocystis was stained using Wright-Giemsa, periodic acid-Schiff (PAS), Oil Red O, Nile Red, and Lugol's iodine solution respectively. The staining effects of the five method on Blastocystis were compared. Results Wright-Giemsa staining colored the cytoplasm and nucleus of Blastocystis bluish-purple, with other parts lightly stained; PAS staining rendered Blastocystis cells red; Oil Red O staining showed different structures within Blastocystis in varying intensities of orange-red; Nile Red enabled Blastocystis to fluoresce orange-yellow under a fluorescence microscope; Lugol's iodine solution stained the cytoplasm and nucleus a dark brownish-yellow. Conclusions Wright-Giemsa staining is recommended for clinical diagnosis. For metabolic studies, a combination of periodic acid-Schiff (PAS) staining and Oil Red O staining is suggested; PAS + Oil Red O staining can provide comprehensive morphological evidence for cell metabolism research. Nile Red staining is recommended for dynamic observation of live Blastocystis. Lugol's iodine staining is suitable for preliminary screening of large-scale samples.

The recombinant human thyroid-stimulating hormone (rhTSH) can be used for differentiated thyroid cancer (DTC) preparation before ¹³¹I therapy, and follow-up. Since its approval by the American Food and Drug Administration (FDA) in 1998, rhTSH has been clinically used in European countries, the United States, and other countries for nearly 30 years. In April 2024, China’s first independently developed rhTSH agent was approved. Based on clinical experience and evidence from domestic and international studies, an expert consensus on the application of rhTSH has been developed following thorough consultations among experts. The consensus offers evidence-based recommendations to guide the appropriate use of rhTSH in clinical practice.

ObjectiveTo explore the effect of Yishen Tongluo prescription on sperm DNA fragmentation index （DFI） and sperm mitochondrial membrane potential （MMP） in patients with asymptomatic idiopathic asthenospermia infertility. MethodsA total of 128 patients with asymptomatic idiopathic asthenospermia were randomly assigned to an experimental group （64 cases） and a control group （64 cases）. The experimental group received Yishen Tongluo prescription， while the control group was treated with Wuzi Yanzongwan combined with L-carnitine oral solution. One treatment course lasted 12 weeks. Spouse pregnancy rate， sperm progressive motility （PR）， total sperm motility （PR+NP）， sperm function （sperm tail hypotonic swelling rate， sperm acrosin activity）， sperm DFI， and sperm MMP were compared between the two groups before and after treatment. Adverse reactions were observed and recorded during the study， and clinical efficacy and safety were systematically evaluated. ResultsA total of 121 patients completed the study， including 61 in the experimental group and 60 in the control group. The spouse pregnancy rate in the experimental group was 14.75% （9/61）， higher than that in the control group at 6.67% （4/60）， though the difference was not statistically significant. Clinical efficacy in the experimental group was superior to that in the control group （P<0.05）. Compared with the results before treatment， sperm PR， PR + NP， sperm tail hypotonic swelling rate， sperm acrosin activity， sperm DFI， and sperm MMP were significantly improved in both groups after treatment （P<0.05）， with greater improvements in the experimental group （P<0.05）. However， there was no significant change in sperm concentration in either group after treatment. During the study， no abnormal safety indicators or significant adverse reactions occurred in either group. ConclusionThe kidney-tonifying and collateral-dredging method shows good clinical efficacy in the treatment of asymptomatic idiopathic asthenospermia infertility. Yishen Tongluo prescription can improve sperm motility， increase spouse pregnancy rate， enhance sperm function， and demonstrates good safety. Its mechanism may be related to reducing sperm DFI and increasing sperm MMP.

Objective: To investigate the detection of depressive symptoms and its influencing factors among middle school students in Huzhou City, so as to provide insights for improving the mental health levels among middle school students. Methods: From September to November 2024, a total of 4 729 middle school students from five counties (districts) in Huzhou City were selected through the stratified cluster random sampling method. Demographic information, lifestyle, and school bullying were collected through questionnaire surveys. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D). Factors affecting depressive symptoms among middle school students were analyzed using a multivariable logistic regression model. Results: A total of 4 729 middle school students were surveyed, including 2 200 boys (46.52%) and 2 529 girls (53.48%). Depressive symptoms were detected in 1 026 students, with a detection rate of 21.70%. Multivariable logistic regression analysis showed that girl (OR=1.960, 95%CI: 1.659-2.317), high school (ordinary high school, OR=1.789, 95%CI: 1.465-2.186; vocational high school, OR=1.581, 95%CI: 1.105-2.263), consumption of sugar-sweetened beverages >0 time/day (<1 time/day, OR=1.363, 95%CI: 1.009-1.841; ≥1 time/day, OR=1.568, 95%CI: 1.098-2.239), fried food intake ≥1 time/day (OR=1.890, 95%CI: 1.291-2.769), skipping breakfast daily (OR=2.178, 95%CI: 1.825-2.599), TV viewing time ≥2 hours/day (OR=1.457, 95%CI: 1.154-1.838), insufficient sleep duration (OR=1.761, 95%CI: 1.422-2.181), smoking (OR=2.798, 95%CI: 1.834-4.269), alcohol consumption (OR=2.282, 95%CI: 1.861-2.798), experiencing school bullying (OR=5.440, 95%CI: 3.148-9.402) and parental physical/verbal abuse (OR=3.954, 95%CI: 3.189-4.902) were associated with a higher risk of depressive symptoms among middle school students. Conversely, the middle school students who engaged in moderate-to-vigorous physical activity ≥3 times/week (OR=0.784, 95%CI: 0.668-0.921) and attended physical education classes ≥3 sessions/week (OR=0.736, 95%CI: 0.613-0.884) were associated with a lower risk of depressive symptoms. Conclusion The prevalence of depressive symptoms among middle school students in Huzhou City was lower than national average, and was influenced by dietary habits, physical exercise, sleep duration, smoking, alcohol consumption, and experiencing school bullying.

INTRODUCTION: Paediatric sexual assault (SA) victims should be assessed for post-exposure prophylaxis (PEP) to mitigate the risk of sexually transmitted infections (STIs). We describe the clinical characteristics of children and young persons (CYPs) presenting with SA at KK Women's and Children's Hospital in Singapore, viral PEP (human immunodeficiency virus [HIV] and hepatitis B virus [HBV]) prescribing practices, and STI evaluation at follow-up. METHOD: Medical records of CYPs ≤16 years who presented with SA between January 2022 and August 2023 were reviewed, including assault and assailant characteristics, baseline and follow-up STI screening, PEP prescription, adherence and follow-up attendance. CYPs with SA in the preceding 72 hours by HIV-positive or HIV-status unknown assailants with high-risk characteris-tics were eligible for HIV PEP. RESULTS: We analysed 278 CYPs who made 292 SA visits. There were 40 (13.7%) CYPs eligible for HIV PEP, of whom 29 (82.9%) received it. Among those tested at baseline, 9% and 34.9% of CYPs tested positive for Chlamydia trachomatis and Gardnerella vaginalis, respectively. None tested positive for Neisseria gonorrhoeae, Trichomonas vaginalis, HIV, HBV or hepatitis C. Majority of CYPs tested were HBV non-immune (n=167, 67.6%); only 77 (46.1%) received the vaccine. Out of 27 CYPs eligible for HBV PEP with immunoglobulin, only 21 (77.7%) received immunoglobulin. A total of 37 CYPs received HIV PEP, including 8 who were retrospectively deemed ineligible. Only 10 (27%) completed the course. Overall, 153 (57.7%) CYPs attended follow-up, and none seroconverted for HIV or HBV. CONCLUSION We report suboptimal rates of HBV post-exposure vaccination, and low compliance to HIV PEP and follow-up among paediatric SA victims. Factors contri-buting to poor compliance should be examined to optimise care for this vulnerable population.
Humans ; Post-Exposure Prophylaxis/methods* ; Female ; Child ; Adolescent ; Singapore/epidemiology* ; HIV Infections/prevention & control* ; Male ; Sexually Transmitted Diseases/epidemiology* ; Retrospective Studies ; Hepatitis B/prevention & control* ; Follow-Up Studies ; Child, Preschool ; Sex Offenses/statistics & numerical data* ; Child Abuse, Sexual