Objective To evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. Methods A retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. Results In the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. Conclusion This AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.

Objective: To evaluate the effectiveness of "Internet plus" continuous intervention on psychological status and nursing satisfaction of patients with depression after hospital discharge, so as to provide the reference for reducing the recurrence risk of patients with depression and improving the quality of life. Methods: From January to December 2024, patients with mild to moderate depression who were hospitalized in a tertiary grade-a mental health specialized hospital in Wenzhou City and met the discharge criteria were selected as the research objects. The patients were divided into the control group and the intervention group according to a ratio of 1∶1 by the random number table method. Hamilton Depression Scale, Hamilton Anxiety Scale, and Nursing Satisfaction Questionnaire were used to evaluate depressive symptoms, anxiety symptoms, and nursing satisfaction before and after intervention. Covariance analysis was used to compare the differences between the two groups before and after the intervention. Results: A total of 62 patients with mild to moderate depression were enrolled, with 31 patients in the intervention group and 31 patients in the control group. Before the intervention, there were no statistically significant differences in gender, age, course of disease, educational level, marital status, depression symptoms score, anxiety symptoms score, and nursing satisfaction score between the two groups (all P>0.05). After the intervention, the scores of depression and anxiety symptoms in the intervention group decreased by 8.87 and 5.01 points, respectively, compared with those before the intervention, and the scores of depression and anxiety symptoms in the control group decreased by 2.52 and 1.16 points, respectively (all P<0.05). After the intervention, the scores of depression and anxiety symptoms in the intervention group decreased more than those in the control group (both P<0.05). The nursing satisfaction score of the intervention group increased by 6.57 points on average compared with that before the intervention, and that of the control group increased by 4.23 points on average (both P<0.05). There was no statistically significant difference in the increase of nursing satisfaction scores between the two groups before and after intervention (P>0.05). Conclusion The "Internet plus" continuous intervention has a good effect on improving the depressive symptoms and anxiety symptoms of patients with depression after haspital discharge, which can consolidate the treatment effect and improve nursing satisfaction.

Deutetrabenazine(DTBZ)is a selective oral small molecule inhibitor of vesicular monoamine transporter 2(VMAT2).Its pharmacological action works by inhibiting VMAT2,thereby reducing the release of presynaptic dopamine and alleviating tardive dyskinesia symptoms caused by long-term use of dopamine receptor antagonists.Compared with tetrabenazine,DTBZ has longer half-life,lower peak plasma concentration,and smaller plasma concentration fluctuations.Clinical studies demonstrate that DTBZ significantly improves abnormal involuntary movement in patients with tardive dyskinesia and has a favourable safety profile.Based on available clinical evidence and practical experience,this paper discuss the common questions about DTBZ including the suitable population,dose,duration of treatment,combination administration with antipsychotics,efficacy assessment and application in special populations.This article aimed to provide guidance and recommendations on clinical application of DTBZ for clinicians.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Pancreatic cancer is a malignant tumor with a very poor prognosis,characterized by early me-tastasis and high invasiveness,and is unresponsive to traditional treatments like chemotherapy and radio-therapy.In recent years,the study of metabolic products has become a new hotspot in pancreatic cancer research,showing that the metabolic reprogramming of tumor cells is a key factor for their growth and pro-liferation,and that regulatory factors of metabolic pathways may serve as new therapeutic targets.Meta-bolic reprogramming primarily manifests as alterations in three major nutrient metabolic pathway and oxi-dative phosphorylation processes.Additionally,the tumor microenvironment of pancreatic cancer exhibits unique metabolic features.Mechanistic studies are actively underway,and future research may focus on integrating omics,artificial intelligence,and other novel research techniques to further explore how meta-bolic changes drive the development of pancreatic cancer and to improve treatment strategies,including the development of targeted drugs and metabolomics-based diagnostic tools.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Diabetes-related cognitive impairment(DCI)is a major complication of type 2 diabetes melli-tus(T2DM).Although exercise is essential in alleviating DCI,the underlying mechanisms remain un-clear.The aim of this study is to investigate the role and mechanism of exosomal miR-126a-5p induced by exercise in ameliorating DCI.Twenty-four 16-week-old male db/db mice were randomly divided into dia-betes group(n=12;DM)and exercise intervention group(n=12;DE).The control group consisted of male m/m mice of the same age group(n=12;CON).The DE group underwent 8 weeks of moderate in-tensity treadmill training(10 m/min,5 days a week).In the MWM experiment,compared to the CON group,the DM group exhibited prolonged escape latency(P<0.01),reduced swimming speed and target quadrant time(P<0.001),and decreased expression of miR-126a-5p and EX-miR-126a-5p in hipp-ocampal tissue(P<0.001).After exercise intervention,the DE group showed improved performance with decreased escape latency(P<0.05),increased swimming speed and target quadrant time(P<0.05),and elevated levels of exosomal miR-126a-5p(P<0.001).Morphological staining revealed a de-crease in the expression and proportion of NeuN in hippocampal neurons and an increase in the expression and proportion of glial cells in the CA1 and CA3 regions of DM group mice compared to CON group mice(P<0.05),while DE group mice showed increased fluorescence intensity and proportion of neurons(P<0.05).Western blotting analysis revealed that the DM group also showed significant upregulation of amy-loid β(Aβ),high mobility group box 1(Hmgb1),and NF-κB in the hippocampus(P<0.05),which were reduced after exercise(P<0.05).Moreover,exosomal miR-126a-5p overexpression greatly de-creased the levels of Hmgb1,NF-κB,and amyloid precursor protein(APP)in HT22 cells and TNF-α,IL-1β in supernatant exposed to HG(P<0.05),while inhibition of miR-126a-5p led to increased levels of these proteins(P<0.05).In conclusion,eight weeks of treadmill exercise improved cognitive function in db/db mice,likely through the EXs-miR-126/HMGB1/NF-κB pathway to reduce inflammation in hip-pocampal tissue.

Objective To analyze the implementation of MPOWER tobacco control policies in different regions and populations in China,as well as the impact of perceptions of tobacco control policies on individual smoking behavior and quit intentions,to promote the fairness of policy implementation and protection for vulnerable groups.Methods A multivariable regression model was constructed utilizing raw data from the China Adult Tobacco Survey to analyze disparities in perceived MPOWER policy implementation among various social demographics and its impact on smoking behavior and quitting intentions.Results Regarding protection from tobacco smoke(P),local economic level,urban-rural divide were significantly correlated with awareness of comprehensive smoking bans.For offering help to quit smoking(O),local tobacco industry revenue and individual age were associated with the doctor's advice for quitting.As to the warning about the harm of tobacco(W),economic level,geography and urban-rural disparity were correlated with the visibility of health warnings.About the tobacco advertising,promotion and sponsorship(E),geography was related to the exposure to tobacco advertisements,local tobacco industry revenue was associated with the tobacco promotion.For tobacco taxes(R),education level and age were significantly correlated with tobacco affordability.People who perceived comprehensive smoking bans(OR=0.69,95%CI:0.59-0.81)was associated with less smoking behavior,while people perceiving tobacco promotional activities(OR=2.51,95%CI:2.00-3.17)were more likely to smoke.Additionally,people who perceived comprehensive smoking bans(OR=1.70,95%CI:1.25-2.31)and health warning(OR=2.09,95%CI:1.48-3.01)had higher intention to quit smoking.Conclusion In economically disadvantaged regions and among specific socially vulnerable groups(such as low-income individuals,rural residents,and the elderly)in China,the perception of tobacco control policy implementation is relatively low,the perception of tobacco control policies can influence smoking behavior and quitting intentions.Legislative and enforcement efforts should be increased targeting these groups with lower perceptions of the policies to enhance the fairness of tobacco control measures.

Objective To explore the impact of suture configuration and fixation type on the biome-chanical strength of rotator cuff repair,using a factorial design study.Methods Sixteen fresh-frozen porcine shoulder samples were randomized into an anchorless double-row suture bridge transosseous su-tures(DS)group,an anchored double-row suture bridge transosseous-equivalent(DE)group,an an-chorless X-BOX construct transosseous sutures(XS)group,and an anchored X-BOX construct transos-seous-equivalent(XE)group,each of four,according to suture configuration(double-row suture bridge,traditional X-BOX construct)and fixation type(suture anchors,transosseous sutures).Then,their fatigue resistance(first-cycle excursion,gap length difference ratio,and the percentage of ex-posed footprints)and the failure strength(the maximum failure load and the re-tear type)were mea-sured using a biomechanical material testing machine.Results Different suture configurations affected failure strength(F=39.559,P<0.001),with the double-row suture bridge groups(693.07±58.35 N,746.76±138.57 N)showing significantly higher failure strength,compared to the traditional X-BOX groups(462.90±18.91 N,421.43±90.76 N).However,the fixation type did not significantly im-pact failure strength(F=1.161,P=0.302).Moreover,the suture configuration influenced the gap differ-ence ratio(F=7.781,P=0.016),but had no significant correlation with other fatigue resistance indica-tors(P>0.05).Meanwhile,failure strength and fatigue resistance were not correlated with fixation type,and the interaction between suture and fixation type(P>0.05).The incidence of failure types for the four suture configurations was as follows:Type I tendon tear:XS>XE>DS=DE;type II tendon tear:DS>XE>XS=DE;fixing material-related failure:DE>DS=XE=XS.Conclusion The failure strength and gap formation ratio in rotator cuff repair under fatigue loading are influenced by suture configuration,whereas no significant association has been observed with respect to fixation method,whether using transosseous sutures or suture anchors.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.