ObjectiveTo explore the effects of vascular endothelial growth factor A （VEGFA） rs2010963 and rs3025039 polymorphisms on chemotherapy toxicities and clinical prognosis in children with brain tumors. MethodsA total of 104 pediatric patients with brain tumors receiving standardized chemotherapy were enrolled. Matrix-assisted laser desorption/ionization time of flight mass spectrometry was used for VEGFA rs2010963 and rs3025039 genotyping. The χ² test was applied to analyze the association between genotypes and chemotherapy‑related toxicities. Cox regression was used to evaluate the correlations of clinicopathological characteristics and genotypes with the progression‑free survival （PFS）. In addition， bioinformatic analyses were conducted to investigate the regulatory factors potentially affected by the two SNP loci. ResultsThe VEGFA expression in brain tumors （5.17±1.81） was significantly higher than that in normal tissues （4.33±1.56， P<0.001）. Patients with high VEGFA expression had significantly worse overall survival than patients with low VEGFA expression （P<0.001）. Among the 104 children with brain tumors included， the rs2010963 CC， CG， and GG genotypes accounted for 14.42%， 55.77%， and 29.81%， respectively. The frequencies of C and G alleles were 42.31% and 57.69%， respectively. The rs3025039 CC， CT， and TT genotypes accounted for 70.19%， 25.96%， and 3.85%， respectively. The frequencies of C and T alleles were 83.17% and 16.83%， respectively. The children with the rs3025039 CC genotype had significantly higher incidences of thrombocytopenia （46.58%） and gastrointestinal toxicity （56.16%） than CT genotype carriers （22.22% and 33.33%， respectively， P<0.05）， and significantly lower incidences of coagulation disorders （4.11%） than TT genotype carriers （50.00%， P<0.05）.The incidence of hyperlipidemia （2.74%） was significantly lower than that in the CT genotype carriers （14.82%， P<0.05）. The tumor type and the rs2010963 genotype were significantly associated with PFS （P<0.05） in univariable and multivariable Cox regression analysis. Bioinformatic analysis indicated that the rs2010963 and rs3025039 polymorphisms regulated VEGFA expression by affecting the binding of transcription factors and miRNAs to their target gene sequences， respectively. ConclusionThe VEGFA rs3025039 CC genotype is a risk factor for thrombocytopenia and gastrointestinal toxicity， and a protective factor for coagulation disorders and hyperlipidemia. The rs2010963 CG genotype is a protective factor for brain tumor progression.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Objective: To evaluate the efficacy of a short video based health education model in enhancing e-cigarette awareness among junior high school students, so as to provide empirical evidence for optimizing school based health education in the digital era. Methods: In December 2024, a cluster sampling method was applied to recruit 465 eighth grade students from a middle school in Kunming for a survey. The intervention was implemented through playing short videos produced by the research team in the classroom, and a self designed adolescent e-cigarette questionnaire was used to conduct assessments before and after intervention, along with qualitative interviews with 60 teachers and students by purposive sampling. Pre and post intervention outcomes were compared by using paired t-test and Chi-square test. Qualitative data underwent a three stage coding process of "open, axial, and selective coding" to construct an explanatory framework. Results: Quantitative analysis showed significant post intervention increases in both baseline awareness scores（92.1±6.6）and deep awareness scores (70.0±17.6) compared with pre intervention( 78.2± 14.0，46.1±20.5)( t =23.54，26.72，both P <0.05), both meeting the criteria for large effect sizes (Cohen s d =1.09, 1.24). The improvement in deep awareness was particularly pronounced, specifically regarding addictive ingredients, illicit additives(tiletamine,metomidate,etomidate), and misconceptions about smoking cessation efficacy ( χ 2=150.54,113.27,117.40,112.92, 69.93 , all P <0.01). Qualitative insights revealed that narrative transportation and visual enhancement were the core mechanisms of efficacy: 87.3% of students preferred contextualized narratives, 85.5% of students decoded cognitive bottlenecks more effectively because of visual enhancement, and 72.7% relied on short videos for micro learning. Conclusion Short video based health education can effectively enhance e-cigarette awareness among junior high school students, offering a pragmatically viable digital pathway for school based health education.

Objective: To understand the correlation between autistic traits in children with autism spectrum disorder(ASD) and their parental behaviors, so as to provide evidence for improving social behavior in children with ASD. Methods: A total of 62 children aged 4-7 years diagnosed with ASD at Guangdong Provincial Work Injury Rehabilitation Hospital and Guangzhou Yuexiu District Children s Hospital from October 2021 to May 2025 were selected as the ASD group. A random number table method was used to select 62 healthy children of the same age from 3 ordinary kindergartens in Guangzhou as the control (typically developing,TD) group. The Parental Behavior Inventory and Social Responsiveness Scale were used to evaluate parental behaviors and autistic traits in both groups. The t-test and multiple linear regression were used to analyze the relationship between autistic traits in children with ASD and parental behaviors. Results: Compared with the TD group, the ASD group had significantly higher scores in social awareness, social cognition, social communication, social motivation, autistic behavior patterns, and the total score of social responsiveness, with statistically significant differences ( t =35.83, 46.17, 64.36, 41.45, 49.46,101.15, all P <0.01). The score of parental support/participation in the ASD group (24.61±4.30) was lower than that in the TD group (31.27±4.58), while the score of parental hostility/coercion in the ASD group (25.51±2.72) was higher than that in the TD group (12.75±2.72), with statistically significant differences ( t = -8.34, 26.14, both P <0.01). The total score of the Social Responsiveness Scale was negatively correlated with the dimension score of parental support/participation behaviors, and positively correlated with the dimension score of parental hostility/coercion behaviors ( r =-0.60, 0.91,both P <0.05). After adjusting for covariates such as children s age, gender, and primary caregiver, ASD children with exclusive breastfeeding ( β =-8.79) and parental support/participation behaviors ( β = -0.79 ) had a lower risk of autistic traits, while children with parental hostility/coercion behaviors ( β =4.62) had a higher risk of autistic traits (all P <0.05). Conclusion Autistic traits in children with ASD may be related to their parental behaviors and early feeding mode.

Objective: To explore the related factors of college students social network addiction based on problem behavior theory, so as to provide a basis for improving social networking addiction in this population. Methods: From May to June 2023, a method combining convenient sampling and cluster random sampling was used to select 1 768 college students from five universities in Shanghai for a questionnaire survey on social networking addiction, self esteem, loneliness, depressive symptoms, social support, interpersonal needs, sense of distress and frustration; at the same time, the physical exercise, smoking and drinking of college students were investigated. Multivariate Logistic regression analysis was applied to explore the association between the three systems of the problem behavior theory (personality, behavior, and social environment) and social networking addiction among college students. Results: The score on the Social Network Addiction Tendency Scale was (21.08±6.29) among college students, and the detection rate of social networking addiction was 66.29%. After adjusting for gender, family economic status, parental divorce status, and whether being an only child, multivariate Logistic regression analysis showed that in the personality system, higher loneliness ( OR =1.66) and higher depressive symptoms ( OR =2.18) were associated with increased risk of social networking addiction among college students; in the behavior system, alcohol consumption ( OR =1.42) was associated with higher risk of soical networking addiction compared to non drinkers; and in the social environment system, low social support ( OR =1.43) was associated with increased risk of social networking addiction (all P <0.05). Conclusions The rate of social networking addiction among college students is relatively high, and the systems of personality, behavior, and social environment are all related to social network addiction. Providing social support, cultivating healthy lifestyle habits, and increasing interpersonal interactions may help reduce excessive dependence on social networking among college students.

Objective: To investigate the epidemiological characteristics of spinal curvature abnormalities among primary and secondary school students in Henan Province, and to explore their association with classroom environment, so as to provide scientific evidence for targeted prevention and control strategies. Methods: From September to October 2025, 751 schools from 158 counties/districts in Henan Province were chosen by using a stratified random cluster sampling technique. A total of 4 019 classrooms were monitored and physical examinations for 202 293 students were conducted. By matching classroom environment monitoring data with student physical examination information, Chi square test and Chi square test for trend were used for between group comparisons, and a multilevel Logistic regression model was used to analyze the association between classroom environment and spinal curvature abnormalities among primary and secondary school students. Results: The detection rate of spinal curvature abnormalities among primary and secondary school students in Henan Province was 2.48% in 2025. The rate was higher in girls ( 2.65 %) compared to boys (2.32%), higher in urban students (3.23%) compared to rural students (2.01%), and higher in wasted students (3.89%) compared to non wasted students (2.39%); all differences were statistically significant ( χ 2=22.92, 298.34 , 100.84, all P <0.05). Age and school stage were associated with an increasing trend in the detection rate of spinal curvature abnormalities ( χ 2 trend = 1 494.46 , 1 430.79, both P <0.05). After controlling for confounding variables like residence area, sex, and age, multilevel Logistic regression analysis revealed that non compliance with desk-chair fit standards ( OR=1.47, 95%CI = 1.22-1.78), blackboard reflectance ( OR=1.18, 95%CI =1.06-1.33), and blackboard size ( OR =1.37, 95% CI =1.21-1.55) increased the risk of spinal curvature abnormalities among primary and secondary school students (all P <0.01). Conclusions There are population based differences in spinal curvature abnormalities among primary and secondary school students in Henan Province. The occurrence is strongly correlated with elements of the teaching environment. It is necessary to put in place comprehensive preventative and control strategies that focus on pertinent environmental elements.

Objective To systematically evaluate the efficacy and safety of robot-assisted laparoscopic surgery (RAS) and conventional laparoscopic surgery (CLS) in hiatal hernia repair. Methods PubMed, Cochrane Library, CNKI, Web of Science, VIP, and Wanfang databases were searched to collect literature comparing the efficacy and safety of RAS and CLS for hiatal hernia repair published from their inception to November 7, 2023. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included studies, and RevMan 5.4.1 software was used for meta-analysis. Results A total of 15 retrospective cohort studies with 18239 patients were finally included. The NOS scores of the included literature were all ≥7 points. Meta-analysis results showed that RAS was superior to CLS in terms of postoperative complications as the primary outcome [OR=0.56, 95%CI (0.42, 0.77), P<0.01]. There was no statistical difference between the two methods in terms of average operation time [MD=−0.74, 95%CI (−12.99, 11.51), P=0.91], average intraoperative blood loss [MD=−24.47, 95%CI (−54.80, 5.87), P=0.11], intraoperative complications [OR=0.76, 95%CI (0.29, 2.01), P=0.58], average postoperative hospital stay [MD=−0.24, 95%CI (−0.75, 0.27), P=0.36], postoperative GERD score [MD=−0.04, 95%CI (−0.41, 0.33), P=0.81], and 30-day readmission rate [OR=0.60, 95%CI (0.30, 1.20), P=0.15]. The cost of CLS surgery was less than that of RAS [SMD=1.59, 95%CI (1.16, 2.01), P<0.01]. Conclusion RAS has comparable efficacy and safety to CLS in hiatus hernia repair.

Tetralogy of Fallot (TOF) is the most common cyanotic congenital heart disease. As a critical component of surgical repair, reconstruction of the right ventricular outflow tract (RVOT) serves as a primary determinant of postoperative hemodynamics and long-term prognosis. With advancements in surgical techniques, early postoperative survival rates for TOF have significantly improved, shifting clinical focus toward addressing long-term complications such as right ventricular dysfunction and reduced exercise tolerance. However, challenges persist regarding optimal timing of surgery in relation to adverse long-term outcomes, balancing RVOT reconstruction strategies, and selecting appropriate repair materials for RVOT reconstruction, with a lack of unified high-level evidence-based guidelines. To address these issues and standardize the application of radical corrective surgery for TOF to better serve patients, the National Society for Congenital Heart Diseases has systematically reviewed existing high-level evidence. Consequently, we have registered and formulated the "Protocol for Chinese expert consensus on right ventricular outflow tract reconstruction in tetralogy of Fallot (2026 edition)", which details the methodology and development process of this upcoming consensus.

Nowadays, lung cancer is the most common and lethal invasive tumor type in Chinese population, challenging overall health level. However, personalized early-stage treatment is currently still not widely implemented, and the choice of treatment highly depends on experience of physician. Based on deep learning and radiomics principles, deep-radiomics is important for establishing objective and promotable precision medicine plans. Among all aspects, the explainability of a model is critical for its usage in clinical practice. This paper discusses the technical aspects of explainable deep-radiomics in lung cancer, and analyzes challenges we are facing. Non-fully supervised learning methods, as a current hotspot in deep learning technology, can construct more trustworthy and practically valuable deep learning models through the co-design method of performance-interpretability. Medical artificial intelligence faces three core challenges in transitioning from the laboratory to hospitals: high-level cognitive demands, data privacy and generalization capabilities, and regulatory compliance. However, with appropriate design, non-fully supervised learning holds the greatest potential to bridge the gap between design and application, enabling broader adoption.