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.

Definitive chemoradiotherapy (dCRT) has become a cornerstone in the treatment of locally advanced esophageal cancer; however, local control remains suboptimal, and persistent lesions or locoregional recurrences after treatment are not uncommon. For patients without distant metastases but with local failure, whether surgical intervention can still offer curative potential remains a major clinical dilemma. Salvage esophagectomy (SE) offers potential long-term survival for selected patients, but this procedure is performed in the context of severe fibrosis, impaired local blood supply, and obscured anatomical planes following chemoradiotherapy, resulting in significantly higher perioperative risk compared to primary esophagectomy. Consequently, controversies exist regarding patient selection, preoperative restaging, choice of surgical approach, extent of lymphadenectomy, gastrointestinal reconstruction, and perioperative management. In recent years, with the refinement of restaging modalities such as PET/CT, the accumulation of experience in high-volume centers, and emerging evidence from clinical studies, the clinical role of SE has gradually shifted from a "high-risk salvage measure" to a "selective curative strategy aimed at achieving long-term survival in carefully selected patients". Nevertheless, standardized guidelines for patient selection, technical approaches, and perioperative management are still lacking. Based on current evidence and clinical experience, experts organized by the Integrated Esophageal Cancer Committee of Chinese Anti-Cancer Association systematically reviewed key issues regarding SE, including its definition, indications, preoperative evaluation, choice of surgical approach, lymphadenectomy, gastrointestinal reconstruction, and perioperative management, and formulated a Chinese expert consensus. This consensus aims to provide guidance for standardized assessment, appropriate referral, individualized surgical decision-making, and optimized perioperative management of patients with locoregional failure after dCRT. Ultimately, this will increase the likelihood of R0 resection, reduce the risk of severe complications, and promote the safer, more judicious, and standardized implementation of SE in high-risk scenarios.

Background: This study was performed to characterize the incidence, costs, and risk factors associated with renal and cardiovascular adverse outcomes following primary percutaneous coronary intervention (pPCI) in patients with ST-elevation myocardial infarction (STEMI). Methods: Patients with STEMI who underwent pPCI using iso-osmolar contrast were enrolled at 39 centers. The incidence of acute kidney injury (AKI) and major adverse renal and cardiovascular events (MARCE) was analyzed, as well as inpatient costs. Logistic regression analysis was performed to identify risk factors. Results: Among 2,293 patients, the incidence of AKI and MARCE within 72 hours post-pPCI was 4.14% (n = 95) and 4.40% (n = 101), respectively. AKI and/or MARCE were associated with systolic blood pressure (AKI: odds ratio [OR], 1.009; 95% confidence interval [CI], 1.000–1.018), hypertension (AKI: OR, 1.815; 95% CI, 1.133–2.906; MARCE: OR, 1.760;95% CI, 1.118–2.769), anterior wall infarction (AKI: OR, 1.895; 95% CI, 1.196–3.004; MARCE:OR, 1.939; 95% CI, 1.240–3.032), Killip class (AKI: OR, 1.465; 95% CI, 1.117–1.922; MARCE:OR, 1.467; 95% CI, 1.131–1.903), and serum creatinine (SCr; MARCE: OR, 1.006; 95% CI, 1.000–1.012). Hospitalization costs for patients with STEMI who developed AKI or MARCE were significantly higher than for those without AKI (9,595 ± 5,795 vs. 8,279 ± 3,872 USD, P = 0.003) or without MARCE (9,890 ± 5,616 vs. 8,255 ± 3,859 USD, P < 0.001). Conclusions In patients with STEMI undergoing pPCI with iso-osmolar contrast, the incidence of AKI and MARCE was associated with higher hospitalization costs. Systolic blood pressure, hypertension, anterior wall infarction, Killip class, and SCr were identified as risk factors for these outcomes.

ObjectiveBiochemistry and Molecular Biology, a discipline that elucidates life phenomena at the molecular level, serves as a core foundational course in medical education. It provides the theoretical basis for studying other basic and clinical medical subjects, as well as for understanding pathogenesis, disease diagnosis, and treatment. However, its complex content and highly abstract concepts have posed a dual challenge to traditional teaching models: “inefficient instruction” and “inadequate learning outcomes”. Within limited classroom hours, how to engage students and stimulate their intrinsic motivation, and how to help them recognize, understand, and develop a passion for biochemistry from the perspective of the discipline’s essence, have long been key focuses of curriculum research. MethodsUsing the lipid metabolism chapter as an example, this study employs “Rain Classroom”, a generative artificial intelligence (AI)-assisted platform, to support education in four dimensions: teaching, learning, evaluation, and research. In teaching, it assists instructors through virtual experiments, lesson preparation support, knowledge mapping, and assignment design. For learning, it serves as an intelligent study assistant for students, providing automated assignment review, enabling educational resource sharing, and facilitating personalized learning pathways. In evaluation, the platform automates assignment grading, analyzes student performance data, and offers diagnostic feedback and teaching recommendations. In research, it aids educators in collecting and analyzing teaching data, as well as searching for and summarizing relevant literature. ResultsThe results indicate that an educational model integrating teacher-led instruction, student-centered learning, and generative AI assistance significantly enhances teaching quality, students’ self-directed learning abilities, and knowledge mastery. Furthermore, with the support of generative AI, curriculum-based ideological education—focusing on cutting-edge disciplinary advances and topical medical issues—helps cultivate students’ medical spirit of “honoring life and healing the wounded”, thereby fostering the establishment of appropriate professional values. Finally, while generative AI presents both opportunities and challenges for higher education, this study also analyzes potential risks in its teaching applications, emphasizing the need for both instructors and students to avoid over-reliance and to ensure that technological tools consistently serve the fundamental goals of education. ConclusionThis study demonstrates that integrating generative AI, specifically via the “Rain Classroom” platform, can effectively enhance biochemistry education. By supporting teaching, learning, evaluation, and research, this approach improves both educational effectiveness and student outcomes. It also facilitates the incorporation of cutting-edge knowledge and professional ethics, nurturing a patient-centered mindset. Additionally, the study addresses potential implementation risks to ensure that such technological tools remain aligned with the core purpose of education.

Based on LI Gao's Academic Thought， focusing on the process of qi transformation and taking the regulation and restoration of metabolism and immunity as the entry point， a "source-pivot-convergence" diagnostic and therapeutic model for malignant tumors is constructed. In this model， spleen and stomach internal injury is the source of malignant tumor occurrence， while the disorder of ascending and descending is the pivot of the disease development， and the generation of yin fire is the convergence of malignant tumor progression. Based on this， the three major therapeutic methods of clearing the source， harmonizing the pivot， and resolving the convergence are established. To fortify spleen and boost qi， consolidate the root and clear the source， modified Buzhong Yiqi Decoction（补中益气汤）can be used. To raise the clear and direct the turbid downward， regulate qi and harmonize the pivot， modified Shengyang Yiwei Decoction （升阳益胃汤） is suggested. To restore balance and promote circulation， disperse accumulation and resolve convergence， modified Shengyang Sanhuo Decoction （升阳散火汤） is selected. In clinical practice， these formulas can be used in combination according to the complexity of the pathogenesis， and further adapted with prescriptions for promoting dispersion and penetrating pathogenic factors， resolving phlegm and promoting circulation， activating blood and eliminating concretions， which can provide a reference for the prevention and treatment of tumor diseases.

Based on LI Gao's Academic Thought， focusing on the process of qi transformation and taking the regulation and restoration of metabolism and immunity as the entry point， a "source-pivot-convergence" diagnostic and therapeutic model for malignant tumors is constructed. In this model， spleen and stomach internal injury is the source of malignant tumor occurrence， while the disorder of ascending and descending is the pivot of the disease development， and the generation of yin fire is the convergence of malignant tumor progression. Based on this， the three major therapeutic methods of clearing the source， harmonizing the pivot， and resolving the convergence are established. To fortify spleen and boost qi， consolidate the root and clear the source， modified Buzhong Yiqi Decoction（补中益气汤）can be used. To raise the clear and direct the turbid downward， regulate qi and harmonize the pivot， modified Shengyang Yiwei Decoction （升阳益胃汤） is suggested. To restore balance and promote circulation， disperse accumulation and resolve convergence， modified Shengyang Sanhuo Decoction （升阳散火汤） is selected. In clinical practice， these formulas can be used in combination according to the complexity of the pathogenesis， and further adapted with prescriptions for promoting dispersion and penetrating pathogenic factors， resolving phlegm and promoting circulation， activating blood and eliminating concretions， which can provide a reference for the prevention and treatment of tumor diseases.

Objective To analyze the risk factors of type 2 diabetes mellitus (T2DM) with metabolic-associated fatty liver disease (MAFLD) and explore their relationship with BMI management. Methods A retrospective analysis was conducted of 310 patients with type 2 diabetes who underwent physical examinations at the 363 hospital between March 2023 and March 2025. Among these patients, those with MAFLD were counted. The risk factors of T2DM with MAFLD were analyzed by logistic regression analysis. The relationship between T2DM with MAFLD and BMI management was explored by Spearman correlation coefficient analysis. Results Compared with the non-MAFLD group, the levels of alanine aminotransferase (ALT), fasting insulin (I₀), fasting blood glucose (G₀), BMI, triglyceride (TG), aspartate aminotransferase (AST), and serum uric acid (SUA) were higher while the level of high-density lipoprotein cholesterol (HDL-C) was lower in the MAFLD group (P<0.05). Logistic regression analysis showed that BMI, SUA, I₀, ALT, G₀, and BMI control scale score were risk factors of T2DM with MAFLD (P<0.05). The score of BMI control scale of patients in the MAFLD group was higher than that in the non-MAFLD group (P<0.05). Correlation analysis indicated that T2DM with MAFLD was negatively correlated with BMI management (P<0.05). Conclusion BMI, SUA, I₀, ALT, and G₀ are all risk factors of T2DM with MAFLD. BMI management is negatively correlated with T2DM with MAFLD. Patients with T2DM should control BMI and blood glucose to reduce the occurrence of MAFLD.

Objective To understand the status of body image disturbance and its influencing factors in patients with ankylosing spondylitis (AS), so as to provide a scientific basis for the clinical management of AS. Methods A total of 353 AS patients admitted from January 2022 to December 2024 were selected as research subjects. Chinese version of Body Image Disturbance Questionnaire (BIDQ) was used to investigate the body image disturbance in AS patients. Single factor analysis was performed by t test and analysis of variance, and multiple factors were analyzed by multivariate linear regression. Results The total score of BIDQ in 342 AS patients was (25.01±4.22). Multivariate linear regression analysis results showed that self-paid medical expense, nighttime VAS score and negative emotion PANAS score could positively predict body image disturbance in AS patients (standardized regression coefficient=0.413, 0.413, 0.460, P<0.05), and PSSS score, positive emotion PANAS score and exercise management CDSSM score could negatively predict body image disturbance (standardized regression coefficient=-0.245, -0.134, -0.247, P<0.05). Conclusion The body image disturbance in AS patients is worthy of clinical attention. Nighttime pain, negative emotion and self-paid medical treatment can increase the risk of body image disturbance. Positive emotion, social support and high self-management level of exercise behavior can reduce the formation of body image disturbance, which can provide new ideas for clinical management of AS patients.

OBJECTIVE To investigate the effects of baicalin （BC） on insulin resistance in rats with gestational diabetes mellitus （GDM） and its underlying mechanism based on the adenosine monophosphate-activated protein kinase （AMPK）/suppressor of variegation 3-9 homolog 1 （SUV39H1）/histone H3 lysine 9 trimethylation （H3K9me3） axis. METHODS A GDM rat model was established by a combination of a high-fat diet and streptozotocin injection. The successfully modeled rats were divided into the GDM group， BC low-dose group， BC high-dose group， and high-dose of BC+AMPK inhibitor （Compound C） group， with 10 rats in each group. Another 10 pregnant rats fed a normal diet served as the control group. Rats in each group were given corresponding drugs/normal saline intragastrically and/or intraperitoneally， once daily for 2 consecutive weeks. After the last administration， the levels of fasting blood glucose （FBG）， pancreatic function indexes [fasting insulin （FINS）， homeostasis model assessment of insulin resistance （HOMA-IR）， insulin sensitivity index （ISI）]， blood lipid indexes （total cholesterol， triglyceride， low-density lipoprotein cholesterol）， liver function indexes （alanine transferase， aspartate transferase， alkaline phosphatase）， inflammatory indicators （C-reactive protein， interleukin-1β， interleukin-6）， metabolic regulatory protein [complement-C1q/tumor necrosis factor-related protein 3 （CTRP3）]， insulin sensitivity related factors [glucose transporter 4 （GLUT4）， adiponectin]， and oxidative stress indicators [superoxide dismutase （SOD）， catalase （CAT）， malondialdehyde （MDA）] were measured. Pathological changes in liver tissue were observed， and the expressions of proteins related to the AMPK/SUV39H1/H3K9me3 axis in liver tissue were detected. RESULTS Compared with the GDM group， rats in the BC low- and high-dose groups showed varying degrees of improvement in pathological changes such as disordered cell arrangement， vacuolar degeneration， lipid deposition， and inflammatory cell infiltration in liver tissue. Their FBG and FINS levels， HOMA-IR， the levels of blood lipid indexes， liver function indexes， inflammatory indicators and MDA， and the expressions of SUV39H1 and H3K9me3 were significantly decreased or down-regulated， while metabolic regulatory protein， insulin sensitivity-related factors and AMPK protein phosphorylation levels were significantly increased （ P ＜0.05）. The improvement was more significant in the BC high-dose group （ P ＜0.05）. Compound C could significantly reverse the ameliorative effects of high-dose BC on the above quantitative indicators （ P ＜0.05）. CONCLUSIONS BC can significantly reduce oxidative stress and inflammatory responses， increase serum levels of CTRP3， GLUT4 and adiponectin， thereby improving insulin resistance in GDM rats. These effects may be related to the activation of AMPK and inhibition of SUV39H1-mediated H3K9me3 modification.

Objective: To understand the current status of neglect among rural preschool children in Xi an under the multi child policy and the association with family characteristics, so as to provide a reference for preventing and reducing the occurrence of child neglect. Methods: A total of 7 052 parents of preschool children were selected using stratified cluster sampling across 9 suburban counties/districts in Xi an from March to April 2025. A questionnaire survey was administered using the Chinese Norm Scale for Neglect Assessment of Rural(Preschool) Children Aged 3-6. The t-test, Chi-quare test, and analysis of variance (ANOVA) were used for inter group comparisons. Results: The overall prevalence rate and mean score of neglect among rural preschool aged children in Xi an were 32.4% and 38.27±6.70, respectively. Statistically significant differences were detected in neglect rates and neglect degrees among preschool children of different genders and grade levels ( χ 2=30.41, 15.15, t/F =4.92,7.03, all P <0.05). Statistically significant differences were also detected in neglect rates and neglect degrees among preschool children from whether only one child, different family structures, numbers of children in a family and families with different annual incomes ( χ 2=29.22, 10.41 , 31.99, 186.47, t/F =-9.96, 5.50, 33.57, 68.63, all P <0.05). In multi child families, there was a statistically significant difference in neglect degree among children with different birth orders ( F =4.25, P <0.05), but there was no statistically significant difference in neglect rate ( χ 2=5.73, P >0.05). Among all subgroups, the highest neglect rates and neglect degrees were observed in children from multi child families(35.0%,39.00±6.71), other family types(50.0%，42.38±12.34) and families with three children(39.9%，39.50±7.43). Lower annual family income was associated with higher neglect rates and neglect degrees among preschool children( χ 2 trend =186.47， F =270.68，both P <0.05). Conclusions Under the multiple child policy, the neglect of preschool children in rural areas of Xi an is quite severe, particularly in families with multiple children and low income households. Targeted interventions should be implemented for high risk groups.