Objective: To evaluate the effectiveness of school-family-community comprehensive intervention aimed at reducing sugar sweetened beverage(SSB) consumption among primary and secondary school students, so as to provide scientific basis for relevant departments to formulate campus sugar reduction measures for primary and secondary school students. Methods: In October 2023, a multi stage random cluster sampling method was used to select four primary schools (grade 4) and four junior high schools (grade 7) in Nanjing City, and they were randomly divided into the intervention group ( n =1 601) and the control group ( n = 1 433 ) (primary and secondary schools). The intervention group received a one year comprehensive intervention activity with the theme of “drinking less SSBs and enjoying a healthy life” for one consecutive year. The measures included the school conducting health education courses (once a month, 15 minutes each time), environmental support (posting promotional posters, changing them once a month), class bulletin board and interest projects (once a semester); interventions for families included conducting parent lectures (once per semester 30 min each time), new media (once a month) and hand in hand activities (once per semester); community carried out square promotion and create a supportive environment (post promotional posters and replace them once a month). The control group did not receive any intervention measures except regular health education classes. Self administered questionnaires were used to collect knowledge, attitudes, and intake of SSB at the baseline and after the intervention among primary and secondary school students. Results: After the intervention, the proportion of students in the intervention group with an SSB related knowledge awareness rate of 60% or above was 91.4%, which was higher than that of the control group (81.9%) ( χ 2=60.35, P < 0.01 ). The reporting rate of the behavior of choosing to drink beverages when thirsty in the intervention group was 25.4%, which was lower than that of the control group (33.3%); the reporting rates of informing parents about the hazards of sugar sweetened beverages and paying attention to nutrition labels were 62.0% and 78.0% respectively, both of which were higher than those of the control group (52.3%, 65.9%); the reporting rate of having sugar sweetened beverages at home frequently was 43.7% in the intervention group, which was lower than that of the control group (62.3%); the reporting rate of parents restricting the consumption of sugar sweetened beverages was 84.1%, which was higher than that of the control group (78.4%) ( χ 2= 22.66, 29.05, 54.40, 104.84, 16.24, all P <0.05). The reporting rate of students in the intervention group who consumed sugar sweetened beverages more than 4 times per week was 21.5%, which was lower than that of the control group (28.6%), and the difference was statistically significant ( χ 2=20.36, P <0.01). Conclusion The comprehensive intervention of school-family-community can effectively improve primary and secondary school students knowledge and attitudes regarding SSBs and reduce their consumption of these beverages.

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.

ObjectiveTo investigate the mechanism by which Simiao Wan improve intestinal uric acid excretion in hyperuricemic （HUA） rats by inhibiting ferroptosis via the solute carrier family 7 member 11/glutathione peroxidase 4 （SLC7A11/GPX4） signaling pathway. MethodsForty-eight male SD rats were randomly assigned to a normal group， a model group， a low-dose Simiao Wan group （282.6 mg·kg^-1）， a high-dose Simiao Wan group （565.2 mg·kg^-1）， an etoricoxib group （5.4 mg·kg^-1）， and a ferroptosis inhibitor （Fer-1） group （2 mg·kg^-1）. Except for the normal group， rats in all other groups were intraperitoneally injected with potassium oxonate （100 mg·kg^-1） combined with intragastric administration of hypoxanthine （500 mg·kg^-1） to establish the HUA rat model. After 21 days， rats in each administration group received the corresponding dose of drug by gavage， rats in the Fer-1 group received intraperitoneal injection of Fer-1， and rats in the normal and model groups were given an equal volume of normal saline by gavage. After sampling， serum levels of uric acid （SUA）， creatinine （Cr）， and blood urea nitrogen （BUN） were measured using biochemical assays. The content of ferrous ion （Fe²⁺）， reduced glutathione （GSH）， and malondialdehyde （MDA）， as well as superoxide dismutase （SOD） activity in ileal tissues were determined. Hematoxylin and eosin （HE） staining was performed to observe the histopathological changes in the ileum， and transmission electron microscopy was used to observe mitochondrial alterations in the ileal tissue. Immunohistochemistry （IHC） and Western blot were used to detect the protein expression levels of GPX4， ferritin heavy chain 1 （FTH1）， acyl-CoA synthetase long-chain family member 4 （ACSL4）， SLC7A11， and ATP-binding cassette transporter G2 （ABCG2） in ileal tissue. Real-time quantitative polymerase chain reaction （Real-time PCR） was used to determine the mRNA expression of GPX4， FTH1， ACSL4， SLC7A11， and ABCG2 in the ileal tissue. ResultsCompared with the normal group， the model group exhibited significantly elevated serum UA， Cr， and BUN levels （P<0.01）， significantly increased content of MDA and Fe²⁺ in ileal tissue， and significantly decreased GSH content and SOD activity （P<0.01）. HE staining showed extensive shedding of the ileal villous epithelium， disrupted and disorganized villous structure， and incomplete local tissue architecture. Transmission electron microscopy revealed reduced mitochondrial volume， decreased or even vanished cristae， indicating ultrastructural features characteristic of ferroptosis. Molecular and immunohistochemical results demonstrated that the protein and mRNA expression levels of GPX4， FTH1， SLC7A11， and ABCG2 were significantly downregulated， whereas those of ACSL4 were significantly upregulated （P<0.05， P<0.01）. Compared with the model group， after intervention with Simiao Wan， the serum UA， Cr， and BUN levels significantly decreased in all dose groups （P<0.05，P<0.01）， the MDA and Fe²⁺ content in ileal tissues were significantly reduced （P<0.01）， and the GSH content and SOD activity significantly increased （P<0.01）. HE staining showed that the shedding of ileal villous epithelium was markedly alleviated， the villous structure became relatively intact and regularly arranged， and local tissue damage was obviously improved. Transmission electron microscopy showed attenuated mitochondrial damage， improvement in mitochondrial shrinkage， and partial restoration of mitochondrial cristae. Molecular and immunohistochemical results showed that Simiao Wan upregulated GPX4， FTH1， SLC7A11， and ABCG2， and downregulated ACSL4 in a dose-dependent manner. In the high‑dose group， all indices improved significantly （P<0.05，P<0.01）， whereas in the low‑dose group， only improving trends were observed for FTH1， SLC7A11 mRNA， and ABCG2 protein. ConclusionSimiao Wan may inhibit ferroptosis of ileal epithelial cells by regulating the SLC7A11/GPX4 signaling pathway， and upregulate the expression of the uric acid transporter ABCG2， thereby promoting intestinal uric acid excretion and improving HUA.

ObjectiveTo construct and validate a risk assessment model for post-transplant diabetes mellitus （PTDM） using multiple machine learning algorithms， and to realize the early identification of PTDM. MethodsA retrospective analysis was performed for the clinical data of the patients who underwent allogeneic liver transplantation in The First Hospital of Shanxi Medical University from April 1， 2020 to December 31， 2024， and they were randomly divided into a training set and a validation set at a ratio of 7∶3. The LASSO regression analysis combined with 5-fold cross-validation was used for feature selection. Seven machine learning models were developed in the training set， i.e.， logistic regression （LR）， decision tree （DT）， Naive Bayes （NB）， random forest （RF）， K-nearest neighbor （KNN）， extreme gradient boosting （XGBoost）， and adaptive boosting （AdaBoost）. In the validation set， various methods were used to assess the predictive performance of each model， such as accuracy， precision， recall rate， specificity， F1 score， area under the receiver operating characteristic curve （AUROC）， and area under the precision-recall curve （AUPRC）. The Brier score and decision curve analysis were used to assess the calibration and clinical practicability of the models， and the SHAP method was used to analyze feature importance. The independent-samples t test or the Wilcoxon rank-sum test was used for comparison of continuous data between two groups， and the chi-square test or the Fisher’s exact test was used for comparison of categorical data between two groups. ResultsA total of 135 liver transplant recipients were enrolled， among whom 26 developed PTDM， and there were 94 patients in the training set and 41 in the validation set. Feature extraction and screening identified 7 key features of sex， overweight or obesity， anhepatic phase， time of operation， length of hospital stay， early postoperative hypomagnesemia， and impaired fasting glucose （IFG）. In the validation set， the XGBoost model showed the best predictive performance， with an AUROC of 0.907 （95% confidence interval ［CI］： 0.807 — 0.989）， an AUPRC of 0.649 （95%CI： 0.339 — 0.955）， an accuracy of 0.878， a precision of 0.667， a recall rate of 0.750， an F1-score of 0.706， a specificity of 0.909， and a Brier score of 0.104. The decision curve analysis showed that when the threshold probability was below 0.667， application of the XGBoost model in clinical decision-making provided relatively high net benefit. The SHAP analysis showed that the length of hospital stay ranked first in terms of feature importance， followed by time of operation， overweight or obesity， sex， IFG， anhepatic phase， and early postoperative hypomagnesemia. ConclusionThe risk assessment model for PTDM in liver transplant recipients based on XGBoost algorithm has excellent performance and can effectively identify high-risk individuals； however， multicenter large-sample data are needed for further validation.

Objective To propose a framework for risk assessment and stepwise management of pulmonary nodules based on existing guidelines and key studies, and to clarify the boundaries for triage, workup escalation, and referral in different detection scenarios. Methods A problem-oriented evidence synthesis approach was used, prioritizing Chinese expert consensus, Fleischner Society Guidelines, British Thoracic Society (BTS) Guidelines, and Lung-RADS. This was supplemented by studies on risk models, imaging measurements, liquid biopsy, tissue sampling, and surgical treatments. This paper is not presented as a formal consensus or a validated clinical pathway. Results A three-tiered framework consisting of basic non-invasive assessment, advanced assessment, and invasive diagnosis was established. It specified the applicable populations, boundaries for the use of risk models, imaging review, management of conflicting results, evaluation of histopathological consistency, and post-intervention follow-up. Liquid biopsy, genetic testing, and artificial intelligence were defined as investigational or adjunctive tools. Conclusion This framework can serve as a preliminary reference for multidisciplinary teams in developing local protocols. It cannot replace applicable guidelines, pathological diagnosis, shared decision-making with patients, or real-world validation.

Objective To systematic analyze the risk factors for new-onset urethral stricture after transurethral holmium laser enucleation of the prostate(HoLEP)in the treatment of benign prostatic hyperplasia(BPH).Methods A case-control study was conducted on 746 BPH patients undergoing HoLEP treatment in Department of Urology of the Third Medical Center of Chinese PLA General Hospital from November 2021 to August 2024.After 23 cases were excluded because of complication of prostate cancer,finally 723 patients were included.General clinical data such as age,height,weight,history of smoking and drinking,perioperative parameters,and follow-up data at 1,3 and 6 months after operation were collected.Univariate and multivariate logistic regression analyses were used to identify the clinical risk factors for new-onset urethral stricture after HoLEP.Results The subjected patients had a median age of 66.5(64.0,75.0)years,and a preoperative median prostate volume of 66(45,92)mL,and a median indwelling catheter time of 4(4,5)d.The incidence of new urethral stricture after operation was 5.8%(42/723),with membranous part of the urethra(61.9%)the most common site,followed by the external urethral orifice(21.4%)and the bladder neck(7.1%).Risk factor analysis indicated that low BMI(<18.5 kg/m2)(OR=4.682,P=0.037),young age(OR=0.946,P=0.005),and postoperative urinary tract infection(OR=4.513,P=0.001)were independent risk factors for new-onset urethral stricture after surgery.Prostate volume and indwelling time of urinary catheter had no significant association with the occurrence of new urethral stricture after surgery.Conclusion The occurrence of new-onset urethral stricture after HoLEP is significantly correlated with BMI,age and urinary tract infection.The above 3 factors can be used as better predictors of new-onset urethral stricture after HoLEP.

As a cheap and effective antibiotic,sulfonamides are often used in animal husbandry.However,their residues in animal-derived foodstuffs will threaten human health.Consequently,a high-performance liquid chromatography(HPLC)method integrated with supramolecular solvent microextraction was successfully established for simultaneous quantification of sulfonamide residues sulfachlorpyridazine,sulfamethoxazole,sulfamethoxypyridazine and sulfadimethoxine in milk matrices.This approach exhibited prominent characteristics of operational simplicity,environmental sustainability,and high extraction efficiency.The supramolecular solvents prepared by tributyl octylphosphine tetrafluoroborate and tetrahydrofuran were employed as extraction solvents.The analytes underwent isolation and concentration via dispersive liquid-liquid microextraction(DLLME)prior to quantitative determination using high-performance liquid chromatography(HPLC).The composition and microscopic morphology of the supramolecular solvent were characterized through a series of analytical techniques,including phase diagram,Fourier transform infrared spectroscopy,scanning electron microscopy,and inverted fluorescence microscopy and so on.The density and pH value of supramolecular solvents were determined.The extraction conditions were optimized through the one-factor experiments.The experimental results demonstrated that under the optimal extraction conditions,the four kinds of sulfonamide antibiotics exhibited excellent linearity within respective detection range(R2 ≥ 0.9998)and the limits of detection(LOD)were 0.67-1.45 μg/L.Compared with literature methods,this approach offered some advantages such as simplicity of operation and less reagent consumption,and could be used for analysis and detection of sulfonamide antibiotic residues in milk samples.The present method provided technical support for food safety regulation and paved a new way for the application of supramolecular solvents in the field of extraction and separation.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Background and Objectives: The Fractional Flow Reserve and Intravascular UltrasoundGuided Intervention Strategy for Clinical Outcomes in Patients with Intermediate Stenosis (FLAVOUR) trial demonstrated non-inferiority of fractional flow reserve (FFR)-guided percutaneous coronary intervention (PCI) compared with intravascular ultrasound (IVUS)-guided PCI. We sought to investigate the cost-effectiveness of FFR-guided PCI compared to IVUS-guided PCI in Korea. Methods: A 2-part cost-effectiveness model, composed of a short-term decision tree model and a long-term Markov model, was developed for patients who underwent PCI to treat intermediate stenosis (40% to 70% stenosis by visual estimation on coronary angiography).The lifetime healthcare costs and quality-adjusted life-years (QALYs) were estimated from the healthcare system perspective. Transition probabilities were mainly referred from the FLAVOUR trial, and healthcare costs were mainly obtained through analysis of Korean National Health Insurance claims data. Health utilities were mainly obtained from the Seattle Angina Questionnaire responses of FLAVOUR trial participants mapped to EQ-5D. Results: From the Korean healthcare system perspective, the base-case analysis showed that FFR-guided PCI was 2,451 U.S. dollar lower in lifetime healthcare costs and 0.178 higher in QALYs compared to IVUS-guided PCI. FFR-guided PCI remained more likely to be cost-effective over a wide range of willingness-to-pay thresholds in the probabilistic sensitivity analysis. Conclusions Based on the results from the FLAVOUR trial, FFR-guided PCI is projected to decrease lifetime healthcare costs and increase QALYs compared with IVUS-guided PCI in intermediate coronary lesion, and it is a dominant strategy in Korea.

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*