Objective: To explore the influencing factors of anxiety symptoms and to construct a predictive model based on machine learning algorithms, so as to provide support for the prevention and management of anxiety symptoms among junior high school students. Methods: From April to May 2023, a stratified random cluster sampling method was adopted to select 8 176 junior high school students from Zhengzhou and Shangqiu citys. All participants completed the Adolescent Self rating Life Events Checklist, the 10item Connor-Davidson Resilience Scale, the School Connectedness Scale, the Parent-Child Cohesion Questionnaire, and the 7 item Generalized Anxiety Disorder Scale. Logistic regression analysis identified the associated factors of anxiety symptoms among junior high school students. Predictive models were constructed using Logistic regression, Random Forest, and eXtreme Gradient Boosting (XGBoost) algorithms, with SHapley Additive exPlanations analysis explaining the optimal model. Results: The detection rate of anxiety symptoms among junior high school students was 16.3%. Logistic regression analysis showed that junior high school students who were female ( OR =1.22), in the ninth grade ( OR =1.27), living in urban areas ( OR =1.37), having a father with a college education or above ( OR =1.26), having a mother with a senior high school education ( OR =1.26), and experiencing higher levels of negative life events ( OR =1.05) reported a higher risk of anxiety symptoms(all P <0.05). In contrast, those with moderate family economic status ( OR =0.71), moderate academic burden ( OR =0.59), low academic burden ( OR =0.54), moderate sleep quality ( OR =0.46), good sleep quality ( OR =0.26), excellent sleep quality ( OR =0.15), higher levels of psychological resilience ( OR =0.96), higher levels of school connectedness ( OR =0.96), and higher levels of parent-child cohesion ( OR =0.98) reported a lower risk of anxiety symptoms (all P <0.05). Three machine learning models demonstrated good predictive performance for anxiety symptoms among junior high school students (all AUC>0.8), with the XGBoost model achieving the best predictive performance. SHAP analysis revealed that negative life events, sleep quality, school connectedness, psychological resilience and parent-child cohesion were the top five relevant factors for predicting anxiety symptoms. Conclusions The detection rate of anxiety symptoms among junior high school students is relatively high. The XGBoost model is the optimal predictive model for anxiety symptoms in the population. Negative life events, sleep quality, school connectedness, psychological resilience, and parent-child cohesion are significant correlates of anxiety symptoms among junior high school students.

Objective:To investigate the application of time series models in forecasting pre-hospital emergency ambulance trips in Handan City and develop the LPro ensemble model for improved prediction accuracy to support emergency resource allocation.Methods:Pre-hospital emergency data from Handan Emergency Medical Command Center (2019-2023) were retrospectively analyzed. From 324 799 original records, 289 949 valid records were included after cleaning. The training set (2019-2022: 215 918 records) included 35 527 records in 2019, 52 015 in 2020, 61 836 in 2021, and 66 540 in 2022. The validation set (2023) contained 74 031 records. ARIMA, linear trend seasonal, exponential smoothing, and Prophet models were fitted to the training set. The LPro ensemble model was constructed using MAPE-based weighting (linear trend seasonal model: 0.38, Prophet: 0.62). Performance metrics included MAPE, RMSE, MAE, and R 2. Results:Data showed annual growth (compound annual growth rate 23.27%) and seasonal patterns (October peaks, February troughs). Ambulance dispatches increased annually with monthly cyclical patterns. For 2023 validation predictions: ARIMA (MAPE 8.76%, RMSE 619, MAE 491, R 2 0.4563), linear trend seasonal (MAPE 9.83%, RMSE 671, MAE 545, R 2 0.3608), Prophet (MAPE 8.43%, RMSE 562, MAE 503, R 2 0.5513), exponential smoothing (MAPE 8.08%, RMSE 643, MAE 410, R 2 0.4124). LPro model showed superior performance (MAPE 7.05%, RMSE 491, MAE 393, R 2 0.6570), with 16.37% lower MAPE, 12.63% lower RMSE, 21.87% lower MAE, and 19.17% higher R 2 versus Prophet. Conclusion:The LPro ensemble model substantially enhances prediction accuracy and reliability, offering scientific support for emergency resource optimization and dispatch scheduling in Handan City.

Objective To evaluate the transition rules of normal body mass index(BMI),overweight and metabolic syndrome(MetS)in patients with major depressive disorder(MDD).Methods Patients with MDD who had multiple admission records between Jan 2016 and Nov 2021 in Beijing Anding Hospital,Capital Medical University were included.Based on the overweight and metabolic syndrome status assessed at each admission,the patients were categorized into three states:normal BMI,overweight and metabolic syndrome.A multi-state Markov model was used to analyze the transition intensity and transition frequency between three states and the influence of covariates on transitions.Results A total of 892 records of 398 subjects were included,with a median age of 56 years old and 31.4% males.The median follow-up period was 40 months.The multi-state model showed that there were 494 transitions between the three states,of which 5.1% moved from normal BMI to overweight and 5.5% moved from overweight to MetS.The intensity of transition was the highest from overweight to MetS,9.52 times greater than overweight to normal BMI.After 48.53 months,MDD patients with normal BMI began to transition to MetS.For overweight MDD patients,the transition to MetS started after 8.77 months.MDD patients with normal BMI or overweight had 31.4% and 50.4% probabilities of developing Mets after 36 months.For MDD patients comorbid with MetS,the probability of staying at MetS was 51.2% after 36 months.Multivariate analysis showed that being unmarried was a risk factor against developing overweight in normal BMI MDD patients,while a higher level of education was a protective factor against developing MetS in overweight MDD patients.Conclusion MDD patients exhibited a higher intensity and risk of developing MetS,and it is not easy to reverse MetS,suggesting that BMI management and MetS intervention should be strengthened in MDD patients.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To investigate the diagnostic value of fetal cardiac ultrasound view visualization in the first trimester for congenital heart disease（CHD）.Methods:A retrospective analysis was performed on 13 323 singleton fetuses who underwent first-trimester（11-13 +6 weeks）ultrasound screening at the Ultrasound Medicine Center，the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to June 2024. Cardiac views including the four-chamber view（4CV），left ventricular outflow tract view（LVOT），and Results:The study group showed significantly higher rates of "poorly visualized" 4CV，LVOT，and 3VT than the control group（2.70% vs. 0.14%， P=0.005；36.49% vs. 4.76%， P<0.001；36.49% vs.2.46%， P<0.001）. The efficacies of combination 1（any view abnormal）and combination 2（any view "poorly visualized" or "abnormal"）were comparable，with AUCs of 0.86 and 0.85（ P=0.424）. The AUCs of combination 3（3VT "poorly visualized" or any view "abnormal"）and combination 4（4CV "poorly visualized" or any view "abnormal"）were 0.88 and 0.86（ P=0.424），all significantly higher than combination 5（LVOT "poorly visualized" or any view "abnormal"，AUC=0.84，all P<0.05）. Conclusions:"Poorly visualized" cardiac views in the first trimester demonstrate good diagnostic efficacy for CHD，particularly when 3VT or 4CV are affected，warranting heightened clinical vigilance for fetal cardiac anomalies.

Objective To evaluate the transition rules of normal body mass index(BMI),overweight and metabolic syndrome(MetS)in patients with major depressive disorder(MDD).Methods Patients with MDD who had multiple admission records between Jan 2016 and Nov 2021 in Beijing Anding Hospital,Capital Medical University were included.Based on the overweight and metabolic syndrome status assessed at each admission,the patients were categorized into three states:normal BMI,overweight and metabolic syndrome.A multi-state Markov model was used to analyze the transition intensity and transition frequency between three states and the influence of covariates on transitions.Results A total of 892 records of 398 subjects were included,with a median age of 56 years old and 31.4% males.The median follow-up period was 40 months.The multi-state model showed that there were 494 transitions between the three states,of which 5.1% moved from normal BMI to overweight and 5.5% moved from overweight to MetS.The intensity of transition was the highest from overweight to MetS,9.52 times greater than overweight to normal BMI.After 48.53 months,MDD patients with normal BMI began to transition to MetS.For overweight MDD patients,the transition to MetS started after 8.77 months.MDD patients with normal BMI or overweight had 31.4% and 50.4% probabilities of developing Mets after 36 months.For MDD patients comorbid with MetS,the probability of staying at MetS was 51.2% after 36 months.Multivariate analysis showed that being unmarried was a risk factor against developing overweight in normal BMI MDD patients,while a higher level of education was a protective factor against developing MetS in overweight MDD patients.Conclusion MDD patients exhibited a higher intensity and risk of developing MetS,and it is not easy to reverse MetS,suggesting that BMI management and MetS intervention should be strengthened in MDD patients.

Objective:To explore the clinical application value of artificial intelligence（AI）-assisted training in enhancing the accuracy of nuchal translucency（NT）identification，standardization of measurement，and diagnostic efficacy for abnormalities among ultrasound residents.Methods:A retrospective collection of 300 standard fetal NT ultrasound images was conducted at the Center for Medical Ultrasound，Suzhou Hospital Affiliated of Nanjing Medical University from January 2018 to June 2024. The AI model performed NT measurements and diagnoses once. Four sonographers of different seniority levels（including two resident physicians）independently conducted NT measurements and diagnoses twice. Prior to the experiment，the middle-age and resident sonographers had uniformly completed traditional theory training. Following the first independent measurements，the two resident sonographers received additional AI-assisted training，after which all 4 sonographers performed the second independent measurements. A fetal medicine expert evaluated blindly all the results and compared the differences in NT recognition accuracy，measurement standard rate and diagnosis accuracy between the middle-age sonographer（traditional training only）and two resident sonographers（traditional + AI-assisted training）.Results:For the middle-aged sonographer who only received traditional lecture-based training，the accuracy of NT recognition，standardization rate of measurement，or diagnostic accuracy were not significantly improved befroe and after the training，and the diffrence was not statistically significant（ χ2=0.189，1.887，0.326；all P>0.05）. In contrast，the second-year resident（Resident 2）and first-year resident（Resident 1），who received both traditional lecture-based training and AI training，demonstrated some improvements in the accuracy of NT measurement site recognition，though the differences were not statistically significant（ χ2=1.301，2.418；all P>0.05）. However，both residents did significant improvements in the standardization rate of NT measurement（ χ2=25.768，17.035；all P<0.05）. In terms of diagnostic accuracy，Resident 1 did significant improvement（ χ2=10.180， P<0.05），while Resident 2 also did some improvement，though the difference was not statistically significant（ χ2=2.573， P>0.05）. Conclusions:The AI-assisted training system enhances the ability of ultrasound resident sonographers to recognize，measure，and diagnose NT，providing a novel and efficient training model for standardized residency training in ultrasound specialties.

Objective:To investigate the diagnostic value of fetal cardiac ultrasound view visualization in the first trimester for congenital heart disease（CHD）.Methods:A retrospective analysis was performed on 13 323 singleton fetuses who underwent first-trimester（11-13 +6 weeks）ultrasound screening at the Ultrasound Medicine Center，the Affiliated Suzhou Hospital of Nanjing Medical University from January 2018 to June 2024. Cardiac views including the four-chamber view（4CV），left ventricular outflow tract view（LVOT），and Results:The study group showed significantly higher rates of "poorly visualized" 4CV，LVOT，and 3VT than the control group（2.70% vs. 0.14%， P=0.005；36.49% vs. 4.76%， P<0.001；36.49% vs.2.46%， P<0.001）. The efficacies of combination 1（any view abnormal）and combination 2（any view "poorly visualized" or "abnormal"）were comparable，with AUCs of 0.86 and 0.85（ P=0.424）. The AUCs of combination 3（3VT "poorly visualized" or any view "abnormal"）and combination 4（4CV "poorly visualized" or any view "abnormal"）were 0.88 and 0.86（ P=0.424），all significantly higher than combination 5（LVOT "poorly visualized" or any view "abnormal"，AUC=0.84，all P<0.05）. Conclusions:"Poorly visualized" cardiac views in the first trimester demonstrate good diagnostic efficacy for CHD，particularly when 3VT or 4CV are affected，warranting heightened clinical vigilance for fetal cardiac anomalies.

Ruxolitinib， a small molecule inhibitor， selectively targets Janus kinase （JAK） by competitively binding to adenosine triphosphate on the catalytic site of the JAK1 and JAK2 domain， thereby inhibiting JAK activation and signal transducer and activator of transcription （STAT） phosphorylation and prevents the expressions of the JAK-STAT signaling pathway. Oral ruxolitinib has demonstrated promising efficacy for myelofibrosis and polycythemia vera. The topical Ruxolitinib cream， approved by the US FDA as the first non-segmental vitiligo home treatment drug， is set to be launched in domestic medical pioneer areas in August 2023 and is expected to bring about a breakthrough in the treatment of vitiligo. Clinical cases have also shown that Ruxolitinib cream has significant curative effects on atopic dermatitis， alopecia areata， and other conditions， indicating great application prospects.

Objective:To explore the efficacy of precise and empirical bismuth-containing quadruple therapy guided by clarithromycin sensitivity testing in the first eradication of Helicobacter pylori ( H. pylori) in Ningxia. Methods:From August 12, 2022 to March 22, 2023, 600 patients diagnosed as H. pylori-positive by 14C-urea breath test ( 14C-UBT) for the first time in People′s Hospital of Ningxia Hui Autonomous Region, Ningnan Hospital of People′s Hospital of Ningxia Hui Autonomous Region, Zhongwei People′s Hospital, Yanchi County People′s Hospital, and Pingluo People′s Hospital were selected, and divided into empirical treatment group (hereinafter referred to as the empirical group), genetic testing group (hereinafter referred to as the genetic group), and drug sensitivity testing group (hereinafter referred to as the drug sensitivity group) by using random number table with 200 patients in each group. The empirical group did not undergo drug sensitivity testing and genetic testing, while the genetic and drug sensitivity groups were confirmed to be sensitive to clarithromycin through genetic testing and drug sensitivity testing, and the patients with drug-resistant were excluded, respectively. All the patients of the 3 groups received the same clarithromycin bismuth-containing quadruple therapy. Intention-to-treat (ITT) and per-protocol (PP) analyses were performed to compare the eradication rates of H. pylori among 3 groups. Cost-effectiveness ratio (CER) and incremental cost-effectiveness ratio (ICER) were used for cost-effectiveness and sensitivity analysis based on the ITT. Chi-square test was used for statistical analysis. Results:There were 200, 126, and 168 patients included in the empirical group, genetic group, and drug sensitivity group in ITT analysis, and 190, 123, and 164 patients were enrolled in the 3 groups in PP analysis, respectively. The results of ITT analysis showed that the eradication rates of H. pylori in the empirical group, genetic group, and drug sensitivity group were 80.5% (161/200), 94.4% (119/126), and 95.2% (160/168), respectively. The results of PP analysis indicated that the eradication rates of H. pylori in the 3 groups were 84.7% (161/190), 96.7% (119/123), and 97.6% (160/164), respectively, and the differences were statistically significant ( χ2=25.39 and 24.93, both P<0.001). The H. pylori eradication rates of genetic group and drug sensitivity group were both higher than that of empirical group in ITT and PP analysis( χ2=12.40, 17.80, 11.42, and 17.13; all P<0.001). The cost-effectiveness analysis showed that the direct treatment cost of the empirical group, genetic group, and drug sensitivity group was 400.8, 729.2, and 779.2 yuan, respectively, and the CER was 4.98, 7.72, and 8.18 yuan/%, respectively. Compared to the empirical group, the ICER of the genetic group and drug sensitivity group was 23.6 and 25.7 yuan/%, respectively. The sensitivity analysis demonstrated that, when the cost of genetic testing reduced or increased by 20%, the ICER of the genetic group compared to the empirical group was 21.8 or 25.5 yuan/%, respectively. When the cost of drug sensitivity testing reduced or increased by 20%, the ICER of the drug sensitivity group compared to the empirical group was 23.3 or 28.2 yuan/%. When the cost of gastroscopy reduced or increased by 20%, the ICER of the genetic group compared to the empirical group was 20.8 or 26.5 yuan/%, and the ICER of the drug sensitivity group compared to the empirical group was 23.0 or 28.4 yuan/%, respectively. Conclusion:In Ningxia, if the clarithromycin bismuth-containing quadruple regimen is applied as the first H. pylori eradication regimen, in order to achieve the clinical eradication efficacy of H. pylori, and the patients can accept an additional payment of 23.6 or 25.7 yuan for each 1% increasing in the H. pylori eradication rate, then the precision treatment after clarithromycin resistance test is recommended.