Chronic post-surgical pain (CPSP) is a common long-term complication following lung surgery. Its high incidence significantly impacts patients’ quality of life and functional recovery, and imposes a substantial socioeconomic burden. This consensus aims to systematically establish a standardized integrated Chinese and Western medicine diagnostic and treatment framework for chronic post-lung surgery pain (CPLSP). Based on the latest domestic and international evidence-based medical research and multidisciplinary clinical experience, the working group comprehensively elaborates on core issues regarding CPLSP, including its definition, epidemiology, pathogenesis, clinical assessment, Western medical treatment, traditional Chinese medicine (TCM) treatment, and integrated strategies. The consensus emphasizes a patient-centered approach, adhering to the principles of multimodality, individualization, and stepwise management, highlighting the synergistic advantages of integrating Chinese and Western medicine throughout the entire perioperative management cycle encompassing "perioperative anti-inflammation, acute analgesia, and chronic rehabilitation." Through systematic literature retrieval and evidence integration, a total of 9 core recommendations were established to provide scientifically sound and clinically practical guidance.

Background Lead is a typical persistent environmental pollutant that can accumulate in bones for decades. During pregnancy, alterations in calcium metabolism promote the mobilization of bone lead, resulting in secondary exposure; however, the mechanisms by which pregnancy-associated bone lead mobilization affects maternal renal function remain unclear. Objective To investigate the role of mitochondrial dysfunction in pregnancy-related bone lead mobilization-induced renal injury. Methods Newly weaned female Wistar rats were randomly assigned to a control or a lead-exposed group administered either 0.05% sodium acetate or 0.05% lead acetate in drinking water. Following a 4-week lead exposure and a 4-week washout period, the females were co-housed with healthy age-matched males for mating. Rats were sacrificed at early (gestational day 3) and late (gestational day 17) pregnancystages, respectively. Renal histopathology was assessed using hematoxylin and eosin staining staining. Mitochondria-related indicators, including oxidative stress, inflammatory responses, and energy metabolism, were measured. Differential metabolites were identified using serum metabolomics. Results Renal injury in the lead-exposed pregnant rats progressed in a time-dependent manner, characterized by degeneration of proximal tubular epithelial cells, glomerular hyaline changes, and interstitial inflammatory cell infiltration. Repeated measures ANOVA indicated a significant interaction between the treatment factor (lead exposure) and the temporal factor (gestational stage) on renal injury (P<0.001). Further analysis of mitochondrial function-related indicators in late-pregnancy renal tissue revealed that the lead exposure group exhibited significantly increased levels of malondialdehyde (MDA) and reactive oxygen species (ROS) (P<0.05), accompanied by a reduction in superoxide dismutase (SOD) and reduced glutathione (GSH) activities (P<0.05); regarding inflammatory markers, levels of interleukin-18 (IL-18) and interleukin-1β (IL-1β) were elevated (P<0.01), whereas interleukin-33 (IL-33) was decreased in the lead-exposed group (P<0.05); energy metabolism-related indicators, including adenosine triphosphate (ATP) level, Na⁺-K⁺-ATPase and Ca²⁺-Mg²⁺-ATPase activities, and mitochondrial respiratory chain complexes I, III, and V activities, were significantly reduced (P<0.05) in the lead-exposed gorup. The typical differential metabolite N-methylisoleucine, identified through serum metabolomics analysis, was negatively correlated with blood lead levels, kidney injury scores, and IL-1β, while positively correlated with catalase (CAT) activity and Ca²⁺-Mg²⁺-ATPase. Conclusions Mitochondrial dysfunction may play a critical role in renal injury induced by bone lead mobilization during late gestation.

Objective To preliminary explore the imaging manifestations of digital breast tomosynthesis (DBT) and contrast-enhanced mammography (CEM) in triple-negative breast cancer (TNBC) patients with different levels of human epidermal growth factor receptor 2 (HER2) expression. Methods A retrospective analysis was conducted on TNBC patients who underwent preoperative DBT or CEM examinations at Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from January 2018 to December 2019 and Shanghai Second People’s Hospital from January 2022 to May 2025. Clinical data, pathological and immunohistochemical results, and imaging data were collected. Results A total of 69 TNBC patients pathologically confirmed as invasive ductal carcinoma were included, among which 34 underwent DBT and 35 underwent CEM. Among these patients, 34 (49.28%) had HER2-low expression and 35 (50.72%) had HER2-zero expression. DBT results showed that the proportion of spiculation signs in HER2-low group (n＝14) was significantly higher than that in HER2-zero group (n＝20; P＝0.009, P_adj＝0.045). However, there were no significant differences in breast density type, mass shape, or calcification between the two groups. CEM results showed that on low-energy images, the proportion of spiculation signs in the HER2-low group (n＝20) was higher than that in the HER2-zero group (n＝15; P＝0.011, P_adj＝0.077). Results of CEM showed that on reconstructed images, differences in background parenchymal enhancement and mass enhancement patterns between the two groups were not statistically significant; in both groups, heterogeneous enhancement was the most common, followed by homogeneous enhancement, with ring enhancement being the least common. Conclusions TNBC with low HER2 expression and TNBC with zero HER2 expression may have potential differences in the presentation of spiculation signs on DBT. However, the correlation between CEM manifestations and TNBC with different HER2 expression levels requires further research.

Objective: To evaluate the diagnostic performance of magnetic resonance spectroscopy (MRS) in discriminating fumarate hydratase-deficient (FH-d) uterine leiomyomas (ULs) from FH-preserved ULs. Materials and Methods: This study consisted of three stages, with independent cohorts recruited for each stage: 1) sample-size estimation was retrospectively performed on UL specimens (diameter ≥3 cm; age, 20–40 years) from our database with immunohistochemistry (IHC) for 2-succinocysteine (2-SC) as the reference, without genetic testing, 2) MRS sequence optimization in confirmed FH germline mutation participants with ultrasound-detected ULs (diameter ≥3 cm), without IHC analysis, and 3) prospective diagnostic test accuracy was evaluated in consecutive participants with ultrasound-detected ULs (diameter ≥3 cm;age, 20–40 years), using IHC for 2-SC for determining the FH status and subsequent genetic testing in those with positive 2-SC results to identify whether FH mutations were germline or somatic in origin. The choline and fumarate peaks in MRS were classified as positive, negative, or technical failure (TF). TFs were analyzed separately and excluded from the primary diagnostic accuracy calculations. T1-, T2-, and diffusion-weighted images were interpreted as hyperintense or hypointense. The enhancement rate and apparent diffusion coefficient were also acquired. Diagnostic performance was compared between MRS and various magnetic resonance imaging (MRI) features. Results: The optimal MRS parameters for the fumarate peak were echo time (TE) = 140 ms and an average of 256. Among the 360 prospective participants, 37 were confirmed to have FH-dULs. MRS showed positive fumarate peaks in 35 of 37 FH-dULs.After excluding six TFs, the positive fumarate peak on MRS showed 94.6% (35/37) sensitivity, 99.7% (316/317) specificity, and 99.2% (351/354) accuracy, all of which were significantly superior to those of other MRI features (P ≤ 0.002). Conclusion A positive fumarate peak on MRS may be a useful imaging biomarker for diagnosing FH-dULs.

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.

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 analyze the relationship between microRNA-21(miR-21)expression and the risk of very early relapse post-induction chemotherapy in children with acute lymphoblastic leukemia(ALL).Methods:A total of 110 newly diagnosed children with ALL admitted to Huanggang Central Hospital from March 2020 to September 2022 were included.All patients received induction chemotherapy according to the CCLG-2008 protocol.The patients who achieved complete response(CR)after induction chemotherapy were followed up for 18 months,with very early relapse as the endpoint event.Then the patients were divided into a relapse group and a non-relapse group.Cox regression was used to analyze the influencing factors of very early relapse after induction chemotherapy in children with ALL.ROC curve and decision curve were used to evaluate the predictive value of peripheral blood miR-21 for very early relapse after induction chemotherapy in children with ALL.Restricted cubic splines were used to analyze the dose-response relationship between peripheral blood miR-21 and very early relapse after induction chemotherapy in children with ALL.Results:A total of 102 children with ALL achieved CR after induction chemotherapy,among whom 24 cases(23.53％)experienced very early relapse,with a median relapse time of 14 months.The proportions of patients with high-risk stratification at initial diagnosis,extramedullary infiltration,and minimal residual disease(MRD)positivity were significantly higher in the relapse group than those in the non-relapse group;The absolute lymphocyte count(ALC)in peripheral blood was significantly lower,while the expression levels of miR-21 and lactate dehydrogenase(LDH)were significantly higher in the relapse group compared with the non-relapse group(all P＜0.05).Cox regression analysis showed that very early relapse after induction chemotherapy in children with ALL was associated with medium risk and high risk at initial diagnosis,extramedullary infiltration,decreased ALC in peripheral blood,MRD positivity,as well as high expression levels of miR-21 and LDH(all P＜0.05).ROC curve analysis indicated that the area under the curve(AUC)of peripheral blood miR-21 for predicting very early relapse after induction chemotherapy in children with ALL was 0.800,with an optimal cutoff value of 4.830.Restricted cubic spline analysis revealed that there was a non-linear dose-response relationship between peripheral blood miR-21 and the risk of very early relapse after induction chemotherapy in children with ALL.When the expression level of peripheral blood miR-21 exceeded 4.830,the risk of very early relapse increased with the elevation of miR-21 expression.Decision curve analysis demonstrated that combining peripheral blood miR-21 with other risk factors enhanced the predictive performance for the risk of very early relapse after induction chemotherapy in children with ALL.Conclusion:Very early relapse after induction chemotherapy in children with ALL is associated with elevated expression of miR-21 in peripheral blood,and high expression of miR-21 may increase the risk of very early relapse.Detecting miR-21 before induction chemotherapy has predictive significance for very early relapse in children with ALL,and combining it with other risk factors can improve the predictive efficacy.

Objective:To analyze the potential risk factors for early relapse/progression in patients with multiple myeloma(MM)and develop a risk prediction model based on these factors.Methods:A retrospective analysis was conducted on 187 newly diagnosed multiple myeloma(NDMM)patients who treated at the Affiliated Hospital of North Sichuan Medical College from February 2014 to December 2020.The clinical,laboratory examination,and follow-up data of patients experiencing relapse/progression within 24 months after treatment(ER/EP24)were analyzed using univariate and multivariate analyses,and a nomogram prediction model was established.Results:Among the 187 patients,58(31.0％)experienced ER/EP24,with a median survival time of only 24 months.The results of multivariate logistic regression analysis showed that failure to achieve partial response(PR)or better after 3-4 cycles of chemotherapy and albumin(ALB)levels＜35 g/L were independent risk factors for ER/EP24(P＜0.05).These factors,along with other clinically relevant variables,were further incorporated into the nomogram prediction model.The model demonstrated a concordance index(C-index)of 0.784,indicating strong predictive accuracy.Conclusion:MM patients experiencing ER/EP24 exhibit poor outcome,and the nomogram model developed in this study effectively predicts the risk of ER/EP24 in NDMM patients,providing a valuable tool for clinical risk assessment.

China now has the largest number of people living with diabetes worldwide. To address such a burden, the Healthy China 2030 initiative and subsequent Healthy China Initiative-Diabetes Prevention and Treatment Action Plan(2024-2030)were launched. A shift from " disease-centred" approach to " health-centred" approach and from treatment to prevention is the core of diabetes management in China. Various regions have formed some characteristic prevention and control models with local features in their long-term diabetes prevention and control work, such as the " Community Three-in-One" management model, hospital-community integrated prevention and control model, " Three Doctors Shared Management" model, and family doctor model. Based on the description of the current situation of diabetes prevention and control in China, this article elaborates on the diabetes prevention and control model, key measures, and practical effects in Bao′an District, Shenzhen. It aims to introduce the practices and reflections on diabetes prevention and control in Bao′an, Shenzhen, and provide experiential reference for diabetes prevention and control in other areas.

Objective To construct a prediction model for the progression of knee osteoarthritis(KOA)based on ultrasound score,magnetic resonance imaging(MRI)score,and serum levels of transforming growth factor-β1(TGF-β1)and cathepsin D(Cat D).Methods Clinical data from 270 patients with KOA in Hengshui People's hospital from December 2022 to June 2024 were retrospec-tively analyzed.The patients were randomly divided into a modeling set(n=189)and validation set(n=81)at a ratio of 7∶3.The patients in the modeling set were categorized into mild-to-moderate and severe groups based on the degree of disease progression.Mul-tivariate logistic regression analysis was used to identify factors influencing KOA progression,and a prediction model was constructed using R software.Results Multivariate logistic regression analysis showed that body mass index,knee injury history,ultrasound score,WORMS score,TGF-β1,and Cat D were significant predictors of KOA progression(P＜0.05).A nomogram-based prediction model was developed using these variables.The areas under the curve(AUC)of the nomograms for predicting disease progression in the modeling and validation sets were 0.889 and 0.860,respectively.The calibration curves showed that the predicted probability was in good agreement with the actual probability.Conclusion The prediction model developed in this study is effective in identifying patients at high-risk of KOA progression and may servce as a valuable tool for clinical assessment and decision making.