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 evaluate the efficacy and safety of CT-guided percutaneous radiofrequency ablation (RFA) as an alternative for video-assisted thoracoscopic surgery (VATS) in treating multiple pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of 113 patients with multiple pulmonary nodules admitted to Jiangsu Provincial Hospital of Traditional Chinese Medicine from October 2020 to October 2022. The patients were divided into the RFA group (n＝50) and the VATS group (n＝63) based on the treatment method. Perioperative indicators (operation time, intraoperative blood loss, postoperative length of hospital stay), oncological outcomes (recurrence-free survival [RFS], overall survival [OS]), and postoperative complication rates were compared between the two groups. Univariate and multivariate Cox regression analysis was performed to identify independent prognostic factors. Results The operation time in the RFA group was significantly shorter than that in the VATS group ([75.2±20.1] min vs [102.3±28.7]) min, P＜0.001). No statistically significant differences were observed in intraoperative blood loss and postoperative length of hospital stay. After follow-up of 24 (12, 30) months, no statistically significant differences were found in RFS (HR＝1.25, P＝0.445) or OS (HR＝1.42, P＝0.402) between the two groups. Mixed ground-glass nodules with high solid component and solid nodule were identified as independent risk factors for RFS (HR＝2.44, P＝0.023; HR＝2.97, P＝0.007) and OS (HR＝2.87, P＝0.022; HR＝3.43, P＝0.005) in patients with multiple pulmonary nodules. The total complication rate in the RFA group was lower than that in the VATS group (12.0% vs 34.9%, P＝0.009). Conclusions The efficacy of CT-guided RFA in treating multiple pulmonary nodules is comparable to that of VATS, with good safety, and it shows promise as an alternative to surgical treatment for multiple pulmonary nodules.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

This paper introduces a real-world cohort research model for community-acquired pneumonia （CAP） based on the Jiangsu Traditional Chinese Medicine （TCM） Dominant Diseases Diagnosis and Treatment Data Platform. Firstly， data cleaning is performed by standardizing diagnosis， symptoms， treatment and imaging， intelligently extracting unstructured information， and cleaning and constructing a standardized database. Secondly， for cohort establishment， CAP patients across the province are screened in accordance with CAP diagnostic criteria to build a high-quality disease-specific cohort. Lastly， in terms of protocol design， the characteristics of TCM research and the CAP disease profile are considered to determine appropriate inclusion and exclusion criteria， estimate sample size， define interventions， outcomes and economic evaluations， providing a reference for real-world TCM research on CAP.

Background: Our genomic understanding of lymphomas, a heterogeneous group of neoplasms, has grown exponentially. The latest World Health Organization (WHO) and International Consensus classifications reflect the importance of genetic assessment in the diagnosis and prognostication of and therapeutic decision making in lymphoid neoplasms. To address this clinical need for routinely available and timely testing, we aimed to validate the Ion AmpliSeq Liverpool Lymphoid Network Panel (IALLNP; Thermo Fisher Scientific, Waltham, MA, USA). Methods: We clinically validated the IALLNP on the Ion Torrent Genexus Sequencer (Thermo Fisher Scientific). The panel detects single-nucleotide variants (SNVs) and insertions/deletions (indels) in 60 clinically relevant genes. The validation set included a commercial control and 54 DNA samples covering the spectrum of clinically aggressive and indolent lymphomas. Results: After optimizing for poor coverage regions, recurrent artifacts, and false-negative calls, the panel showed good performance in terms of depth of coverage, on-target reads, and uniformity. Its sensitivity for SNVs and indels at a lower limit of detection of 5% variant allele frequency (VAF) was 100%. Specificity in variant-negative samples was 100%, and the mean per-sample number of false-positive variants—which were easily identifiable and excluded upon interrogation of raw data—was 0.4. The panel demonstrated 92.8% reproducibility; however, all nonreproducible variants fell below the 5% VAF analytical threshold. Conclusions The IALLNP is an accurate and reproducible next-generation sequencing panel that delivers genetic results for lymphoid neoplasms in a clinically meaningful timeframe.

The surgical management of empyema (excluding those caused by mycobacterium tuberculosis and non-tuberculous mycobacteria) is rapidly evolving towards minimally invasive, precise, and stepwise approaches. The traditional three-stage classification (exudative, fibrinopurulent, and organizing) has limitations in guiding dynamic clinical decision-making. For the first time, this consensus explicitly identifies two critical junctures in the pathological progression of empyema: "early transformation" (stage Ⅰ to Ⅱ) and "late transformation" (stage Ⅱ to Ⅲ), and thereby constructs a corresponding "identification-early warning-intervention" stepwise therapeutic framework. The consensus emphasizes that proactive debridement via video-assisted thoracoscopic surgery should be performed during the early transformation phase to halt disease progression. Conversely, during the late transformation phase, therapeutic goals should be rationally adjusted to prioritize adequate drainage, avoiding futile pleural decortication. Moreover, the consensus underscores the pivotal role of precise perioperative etiological diagnosis (e.g. metagenomic nest-generation sequencing) and standardized anti-infective therapy. Integrating practical experiences from multiple thoracic surgery centers in China and relevant evidence-based literature, this consensus formulates recommendations on the precise definitions of staging, surgical indications for each phase, key technical points, perioperative management, and training systems. It aims to promote the standardized and individualized surgical management of empyema, ultimately optimizing patient prognosis.

Thousands of variant gene sequences of SARS-CoV-2 have been emerged since the COVID-19 epidemic broke out in Dec.2019.Alpha(B.1.1.7),Beta(B.1.351),Gamma(P.1),Delta(B.1.617.2)and Omicron(B.1.1.529)were the most representative variants.With the continuous emergence of new variants,the predomi-nant strain in the global pandemic as of Jan.2025 is the Omicron BA.2.86-derived mutant,JN.1.The effectiveness of drugs against Omicron variants remains a key research focus in the treatment of SARS-CoV-2 infections.The infectiousness and pathogenicity of the variants altered remarkably due to mutations in the genome on S protein of the mutant strains,and these emerging variants are more likely to evade immunity and were more infectious than the previous prevalent variants.During the process of combating with the constantly emerging novel variants,drugs showed various effects on treatment of diseases caused by different variants.New drugs and treatment coun-termeasures are constantly updated with the prevalence of various variants.The current status of research on pres-ent drugs for treatment of SARS-CoV-2 and the therapeutic effects on emerging variants are reviewed in the article so as to provide reference for prevention and treatment of the upcoming evolved variants.

Objective To explore the therapeutic effect and prognostic influencing factors of endovascular therapy for periprocedural acute ischemic stroke with large vessel occlusion(AIS-LVO)after cardiac surgery,and to analyze the pathological characteristics of thrombi leading to LVO.Methods This study retrospectively and consecutively enrolled patients who experienced AIS-LVO during the perioperative period of cardiac surgery at the Cardiovascular Surgery Department of Guangdong Provincial People's Hospital from June 1,2017,to March 31,2024.The patients were divided into a surgical group and a non-surgical group based on whether they received endovascular treatment.The clinical and imaging data of the patients were collected,including gender,age,hypertension,diabetes,history of smoking,low-density lipoprotein cholesterol,cardiac surgical methods(coronary artery bypass grafting,total arch replacement[TRA],cardiac valve replacement,other types of surgery[valve repair,atrial septal repair,ventricular septal repair,heart transplantation]),time from cardiac surgery to AIS onset,location of vascular occlusion(vertebral-basilar artery,right internal carotid artery,left internal carotid artery),National Institutes of Health stroke scale(NIHSS)score at the onset of stroke,time from stroke onset to puncture,stroke etiology(embolism,arterial dissection,atherosclerotic stenosis),and imaging data including Alberta stroke program early CT score(ASPECTS)of the anterior circulation,ASPECTS of the posterior circulation(pc-ASPECTS)and CT angiography,CT perfusion,etc.The thrombus samples were stained with hematoxylin-eosin and martius scarlet blue staining.According to the proportion of red blood cells(RBC)in the thrombus,the thrombi were classified as erythrocyte-rich thrombi(RBC proportion≥70%),mixed thrombi(RBC proportion 31%-69%),and fibrin-rich thrombi(RBC proportion≤30%).At 90d after stroke onset,the modified Rankin scale(mRS)was used for both outpatient and telephone follow-up.The mRS score 0-2 indicated a good prognosis,3-6 indicated a poor prognosis,and 6 points indicated death.Factors with P＜0.1 in univariate Logistic regression and those factors may influence the prognosis according to clinical experience were included and further analyzed with multivariate Logistic regression to analyze the 90 d prognostic influencing factors after stroke onset for AIS-LVO patients during the perioperative period of cardiac surgery.Results A total of 102 patients who experienced AIS-LVO during the perioperative period of cardiac surgery(68 males,34females,with mean age of[58±14]years)were enrolled in this study,50 were in the non-surgery group and 52 in the surgery group.(1)Significant differences were observed between the surgical and non-surgical group in hypertension(32.7%[17/52]vs.54.0%[27/50],P=0.030),NIHSS score at stroke onset(12.3[12.3,21.8]vs.35.0[18.0,35.0],P＜0.01),vascular occlusion site(P=0.048),cardiac surgery type(P＜0.01),ASPECTS/pc-ASPECTS(9[8,9]vs.4[3,6],P＜0.01),favorable90-day prognosis rate(75.0%[39/52]vs.10.0%[5/50],P＜0.01)and 90-day mortality rate(7.7%[4/52]vs.68.0%[34/50],P＜0.01).Other clinical and imaging data showed no significant differences(all P＞0.05).In the surgery group,86.5%(45/52),7.7%(4/52),and 5.8%(3/52)patients were attributed to embolism,arterial dissection,and atherosclerotic stenosis,respectively.(2)Univariate Logistic regression analysis identified NIHSS score at stroke onset,TRA,endovascular therapy,and ASPECTS/pc-ASPECTS score as prognostic factors affecting the 90-day prognosis after stroke.Set the patient's 90-day prognosis(assign favorable prognosis as 1 and poor prognosis as 0)as dependent variable,incorporate factors with P＜0.1 in the univariate Logistic analysis and location of vascular occlusion in a multivariate Logistic regression analysis.The results showed that high NIHSS score on stroke onset(OR,0.86,95%CI 0.78-0.95,P=0.002)and non endovascular therapy(OR,6.93,95%CI 1.05-45.55,P=0.044)were independent risk factors of poor prognosis 90-day after stroke onset for AIS-LVO patients during the perioperative period of cardiac surgery.(3)Among 45 cardioembolic patients,thrombus samples from 33 patients were analyzed.The analysis revealed no erythrocyte-rich thrombi,3 mixed thrombi,and 30 fibrin-rich thrombi,with 12having a fibrin proportion exceeding 90%.Conclusion Endovascular therapy improved the prognosis and reduces mortality rate in patients with periprocedural AIS-LVO after cardiac surgery,and thrombi in these patients are predominantly fibrin-rich.

Objective:To systematically evaluate the current financing status of Beijing's urban and rural residents'basic medical insurance,analyze equity disparities among different groups under the existing flat-rate financing policy.By simulating the equity changes of various financing schemes with different contribution rates,this study aims to provide foundations for advancing medical insurance financing system reform.Methods:Based on the per capita disposable income and number of insured residents in Beijing's 16 districts from 2018 to 2023,we separately calculated the Gini coefficient,concentration index,and Kakwani index,along with their changes before and after financing adjustments,to assess the funding burden among different insured groups.Using the geometric mean method,we projected per capita disposable income and insured populations for each district in Beijing from 2024 to 2035,simulating various financing schemes under different premium rate systems.Results:From 2018 to 2023,the Gini coefficient of net income after financing consistently exceeded that of original income before financing.The concentration index remained positive and showed an upward trend,while the Kakwani index was negative for all periods.The Gini coefficient after financing slightly decreased for the elderly and working populations with fiscal subsidy support,whereas it remained higher than that for students and children.Under the simulated differentiated rate system,the post-funding Gini coefficient for 2024-2035 was lower than the original value,and the Kakwani index was positive.Conclusions:The current fixed-amount financing mechanism for urban and rural residents'basic medical insurance exhibits regressive characteristics and insufficient fairness,with disparities in equity among different insured groups.It is necessary to establish differentiated financing standards based on differential rates,particularly implementing a financing mechanism with dynamic adjustments according to regional and group income levels,to enhance the fairness of financing for urban and rural residents'basic medical insurance.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.