Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
Humans ; Magnetoencephalography/methods* ; Brain/physiopathology* ; Depression/diagnosis* ; Electroencephalography ; Magnetic Resonance Imaging

Objective:To investigate the clinical efficacy of a self-detachable single-J internal stent drainage tube in the laparoscopic primary suture of the common bile duct for hepatolithiasis.Methods:Clinical data of 36 patients with hepatobiliary duct stones who underwent laparoscopic common bile duct primary suture combined with self-detached single J-type internal stent drainage at the First Affiliated Hospital of Xiamen University from April 2022 to April 2025 were retrospectively analyzed, including 17 males and 19 females, aged (54.3±8.7) years. All 36 patients underwent choledochoscopic stone extraction, primary common bile duct suture, and drainage with the self-expelling single-J internal stent. Total bilirubin, aspartate transaminase, and alanine transaminase before and 3 days after operation, as well as operation time, intraoperative blood loss, postoperative hospital stay, postoperative complications (bile leakage, cholangitis, intestinal obstruction, and stent retention), and stent expulsion time were collected.Results:All 36 patients successfully underwent the operation. Total bilirubin, aspartate transaminase, and alanine transaminase 3 days after operation showed significant improvement compared to preoperative levels (all P<0.05). The operation time was (86.5±22.6) min, intraoperative blood loss was 34.2 (13.7, 56.8) ml, and the postoperative hospital stay was (6.6±1.8) days. All single-J internal stents were spontaneously expelled via the anus between 6 and 21 days postoperatively, with the expulsion time of (10.7±2.1) days. No single J-type internal stent drainage tube was displaced into the bile duct in all cases, and there were no complications such as intestinal obstruction, bile leakage, cholangitis, or residual internal stent drainage tube. Conclusion:The self-detachable single-J internal stent drainage tube has been applied in the laparoscopic primary suture of the common bile duct for patients with hepatolithiasis, which demonstrated a good safety and effectiveness.

Objective:To investigate the effect of "four-staff co-management" follow-up mode on risk factor control and medium-term prognosis improvement in patients with coronary heart disease after percutaneous coronary intervention (PCI).Methods:This was a intervention study. Patients with coronary heart disease who were admitted to the Xiamen Cardiovascular Hospital of Xiamen University from June 2021 to January 2022 and successfully discharged after PCI were included. According to the different types of follow-up after discharge, patients were divided into the traditional follow-up group and the "four-staff co-management" follow-up group. The "four-staff co-management" follow-up mode means that specialists, specialist managers in third-level A hospitals and general practitioners and health managers in basic hospitals were jointly responsible for post-discharge follow-up of PCI patients. Baseline clinical data were collected. The primary endpoints were the rate of compliance of coronary heart disease risk factor control at 12 months after surgery, the rate of secondary surgery, and the incidence of mid-term major adverse cardiovascular and cerebrovascular events (MACCE). Unplanned secondary PCI included symptom-driven secondary PCI and asymptomatic secondary PCI. MACCE includes myocardial infarction, hospitalization for heart failure, stroke, major bleeding, all-cause death, and composite endpoints including these events.Results:A total of 2 181 patients were enrolled, including 1 097 patients in the traditional follow-up group and 1 084 patients in the "four-staff co-management" follow-up group. At baseline, there were no statistically significant differences in gender, age, discharge diagnosis, co-existing diseases, echocardiographic indexes, and coronary artery lesions between the two groups (all P>0.05). There were no significant differences between the two groups in total PCI stent length, maximum internal diameter of stent, proportion of patients using drug balloon, proportion of patients with a planned second surgery during hospitalization, and discharge with drugs (all P>0.05). Twelve months after PCI, the reduction in HbA1c and low-density lipoprotein cholesterol was greater in the "four-staff co-management " follow-up group than that in the traditional follow-up group (all P<0.05), and the rate of reaching the standard for low-density lipoprotein cholesterol was higher than that in the traditional follow-up group ( P=0.001), but there was no statistical significance between the two groups for blood pressure and blood glucose (all P>0.05). During the follow-up period, the proportion of symptom-driven second operation patients was lower in the "four-staff co-management" follow-up group than that in the traditional follow-up group ( P<0.001), and there was no significant difference in the proportion of asymptomatic second operation patients between the two groups ( P=0.191). The proportion of hospitalized patients with heart failure in the "four-staff co-management" follow-up group was lower than that in the traditional follow-up group ( P=0.029), and there was no significant difference in the proportion of myocardial infarction, cerebral infarction, cerebral hemorrhage, massive hemorrhage, death and complex endpoint events between the two groups (all P>0.05). Conclusion:The "four-staff co-management" follow-up mode can effectively improve the control of risk factors and medium-term prognosis in patients with coronary heart disease after PCI.

Objective: To establish and validate a whole blood (WB) supply model, thereby providing practical experience for the clinical application of WB in domestic trauma emergency care and informing the development of a wartime blood supply system for the military. Methods: A “10×24” WB supply model was established by formulating blood collection protocols, storage standards, and transfusion criteria. Multiple WB samples were tested under specific storage conditions to assess key indicators at different time points, including red blood cell (RBC), white blood cell (WBC), and platelet counts, hemoglobin concentration, coagulation parameters (PT, APTT, TT, FIB), coagulation factor activity, thromboelastography (TEG) parameters, and electrolyte levels. Additionally, clinical data from hemorrhagic patients who met the criteria for WB transfusion and were admitted between March and July 2024 were analyzed to evaluate WB transfusion volume. Results: RBC counts and hemoglobin levels remained stable in WB stored at 4℃ for up to 10 days. However, platelet counts and coagulation function (PT, APTT) significantly declined with prolonged storage, while potassium levels increased. From March to July 2024, the model was successfully applied to 23 patients with acute hemorrhage, with a median WB transfusion volume of 543 mL. A detailed case study of a severe traumatic hemorrhagic shock patient was reported, who was successfully treated with 5.5 units of refrigerated WB combined with component blood. Conclusion: The “10×24” WB supply model demonstrated acceptable changes in critical quality parameters under strict management and a 10-day rotation cycle. This model effectively supports the treatment of acute hemorrhage and holds promise for integration into the future wartime blood supply system of the military.

The in-depth research of artificial intelligence in the medical field has greatly improved the workflow and diagnostic ability of diagnostic radiology.This article focuses on artificial intelligence technology in the field of interventional medicine,and enumerates its potential application scenarios,including improving image analysis capabilities to assist diagnosis and predict treatment response.It also describes the challenges that need to be overcome for practical application.Finally,with the continuous development of artificial intelligence in interventional medicine,artificial intelligence will further optimize the channels of interventional medicine and bring revolutionary changes to the clinical practice of interventional medicine.

Dialectical behavior therapy （DBT） is one of the empirically supported therapeutic approaches for borderline personality disorder （BPD）. By integrating behaviorism， dialectical philosophy， biosocial theory and Zen principles， DBT aims to enhance patients' emotional regulation， interpersonal effectiveness and distress tolerance， thereby alleviating BPD symptoms. This article systematically reviews the theoretical foundations of DBT and its research progress in BPD treatment， to Delve into the intervention effects of DBT， as well as the adjuvant role of pharmacotherapy and physiotherapy in enhancing DBT for BPD， and analyzes the challenges faced in DBT research and clinical application. The findings are expected to provide new insights for the localization and theoretical research of DBT in China.

With the advancement of China's healthcare reform,enhancing the capacity of primary healthcare services has become a pivotal task.Colorectal cancer,one of the most prevalent malignancies in China,highlights the critical importance of early screening and diagnosis to improve patient survival rates.This study,guided by the principles of Party building and Xi Jinping Thought on Socialism with Chinese Characteristics,examines the implementation and outcomes of a rural outreach program focused on colorectal cancer screening and diagnostic technologies.By promoting the dissemination of colorectal cancer screening initiatives,the paper aims to provide empirical evidence to support the deepening of primary-care services,foster high-quality ad-vancement of grassroots health services,and align with the national Healthy China Initiative,thereby more effectively safeguarding population health.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:To explore the impact of adolescent mental health status on smartphone addiction, and construct a predictive model for smartphone addiction based on the eXtreme Gradient Boosting(XGBoost) algorithm and multivariate Logistic regression.Methods:In April 2023, a cross-sectional survey was conducted among 14 666 adolescents.All participants were systematically evaluated using a self-developed general information questionnaire, the middle school student mental health scale(MSSMHS), the adolescents self-harm scale(ASHS), the interaction anxiousness scale(IAS), the mobile phone addiction index(MPAI), the middle school students shame scale(MSSS), the UCLA loneliness scale(UCLA-LS), the multidimensional peer victimization scale(MPVS), and the basic psychological needs scale(BPNS).R software version 4.3.2 was used for data analysis. Participants were randomly divided into training set and validation set at the ratio of 7∶3.The XGBoost model and multivariate logistic regression model were constructed to predict the risk of smartphone addiction, and a nomogram was plotted.Model performance was evaluated using the Hosmer-Lemeshow test, area under the curve(AUC), and accuracy(ACC).Results:(1) A total of 14 036 high school students were included in the study, with 5 069(36.1%) exhibited smartphone addiction.The training set comprised 9 826 students, with 3 549(36.1%) being smartphone addicts.The validation set included 4 210 students, with 1 520(36.1%) being smartphone addicts.(2) The XGBoost model identified shame-proneness and social anxiety as the two main predictors of smartphone addiction.(3) Multivariate Logistic regression analysis revealed that anxiety( B=0.328, OR(95% CI)=1.39(1.07-1.81), P=0.015), interpersonal sensitivity( B=0.311, OR(95% CI)=1.36(1.05-1.77), P=0.018), learning pressure( B=0.606, OR(95% CI)=1.83(1.46-2.31), P<0.001), mood swings( B=0.775, OR(95% CI)=2.17(1.70-2.78), P<0.001), social anxiety( B=0.024, OR(95% CI)=1.02(1.01-1.04), P<0.001), shame-proneness( B=0.049, OR(95% CI)=1.05(1.04-1.06), P<0.001), and peer victimization( B=0.037, OR(95% CI)=1.04(1.02-1.06), P<0.001) were significant predictors of smartphone addiction.(4) The ACC and AUC values of the XGBoost model were 0.890 and 0.929 in the training set, and 0.865 and 0.864 in the validation set, respectively.The multivariate Logistic regression model achieved ACC and AUC values of 0.870 and 0.854 in the training set, and 0.867 and 0.859 in the validation set, respectively. Conclusion:Anxiety, interpersonal sensitivity, learning pressure, mood swings, social anxiety, shame-proneness, and peer victimization are identified risk predictors of smartphone addiction in high school adolescents.

Objective:To evaluate the validity and reliability of the Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS).Methods:Totally 188 patients with schizophrenia meeting the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)diagnostic criteria were selected for SC-BCIS and the Insight and Treatment Attitudes Questionnaire(ITAQ)assessment.Thirty-eight patients were selected for retest-ing within 4 weeks.The item analysis was conducted using the Spearman correlation method,and the reliability of the scale was tested with Cronbach's α coefficient and ICC coefficient.The structural validity of the scale was ex-amined through principal component analysis and exploratory factor analysis.Results:The correlation coefficients between the 15 item scores and the total score of the SC-BCIS all met the screening criteria.The Cronbach's α coef-ficient of the scale was 0.69,the test-retest ICC value was 0.82,the ICC coefficient between the SC-BCIS and ITAQ scales was 0.83,and the scale had a two-factor structure,with a cumulative contribution rate of 42.4％for the two factors.Conclusion:The Simplified Chinese version of the Beck Cognitive Insight Scale(SC-BCIS)has good validity and reliability in measuring cognitive insight in patients with schizophrenia.