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

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Hypoxia and aberrant activation of epidermal growth factor receptor (EGFR) are considered important features of various malignancies. However, whether hypoxia can directly trigger EGFR activation and its clinical implications remain unclear. In this study, we demonstrated that in oral cancer, a typical hypoxic tumor, hypoxia can induce chronic but constitutive phosphorylation of wild-type EGFR in the absence of ligands. Oral cancer cell lines exhibit different EGFR phosphorylation responses to hypoxia. In hypoxic HN4 and HN6 cells, ubiquitination-mediated endocytosis, lysosomal sorting, and degradation lead to low levels of EGFR phosphorylation. However, in CAL-27 and HN30 cells, a novel HIF-1α-induced long noncoding RNA (lncRNA), EUDAL, can compete with the E3 ligase/adaptor complex c-Cbl/Grb2 for binding to EGFR, stabilizing phosphorylated EGFR (pEGFR) and resulting in sustained activation of EGFR and its downstream STAT3/BNIP3 signaling. STAT3/BNIP3-mediated autophagy leads to antitumor drug resistance. A high EUDAL/EGFR/STAT3/autophagy pathway activation predicts poor response to chemotherapy in oral cancer patients. Collectively, hypoxia can induce noncanonical ligand-independent EGFR phosphorylation. High EUDAL expression facilitates sustained EGFR phosphorylation in hypoxic tumor cells and leads to autophagy-related drug resistance.
Humans ; ErbB Receptors/metabolism* ; Mouth Neoplasms/pathology* ; RNA, Long Noncoding/genetics* ; Drug Resistance, Neoplasm/genetics* ; Cell Line, Tumor ; Phosphorylation ; Signal Transduction ; STAT3 Transcription Factor/metabolism* ; Cell Hypoxia ; Autophagy ; Proto-Oncogene Proteins c-cbl/metabolism*

Objective To look for cognitive assessment paradigms and electrocardiographic(ECG)characteristics sensitive to mental fatigue through an experimental study.Methods Data were collected from 10 healthy students of the University of Shanghai for Science and Technology who participated in the 4-day National Undergraduate Electronics Design Contest,including their Stanford sleepiness scale(SSS)scores,sleep duration,cognitive task performance,and ECG signal data.Ten ECG features,including time-domain,frequency-domain,and information-domain characteristics,were extracted during cognitive tasks.The cognitive task performance and ECG features sensitive to changes in mental fatigue were analyzed.Results Significant differences were observed in SSS scores(x2=23.116,P＜0.001)and sleep duration(x2=19.608,P＜0.001)across the 4 d.For cognitive task performance,word-color congruent accuracy and word-color incongruent accuracy in the Stroop task and Single-visual target stimulus accuracy and Visual target-auditory non-target stimulus accuracy in the audiovisual competition task all showed significant negative correlations with SSS scores(all P＜0.05).Regarding ECG features,Poincaré plot SD2 during the Stroop task was positively correlated with sleep duration(P＜0.05),while Poincaré plot SD2 during the 2-back task was positively correlated with mental fatigue assessment scores and was negatively correlated with sleep duration(both P＜0.05).Conclusion The accuracy of the Stroop task and audiovisual competition task is a cognitive ability indicator sensitive to mental fatigue,while the Poincaré plot SD2 during Stroop and 2-back tasks is an ECG indicator sensitive to mental fatigue.

Objective To investigate the predictive value of combined radiomic features derived from chest CT scans with clinical characteristics for epidermal growth factor receptor(EGFR)gene mutations in non-small cell lung cancer(NSCLC).Methods A multi-center case-control study was conducted on the clinical data and CT images of 1 070 NSCLC patients from the radiology departments of the 3 medical institutions between January 2013 and October 2023.The 719 NSCLC patients from the First Affiliated Hospital of Army Medical University were randomly divided into a training set and an internal validation set in a ratio of 7∶3;The 173 patients in the Eastern Theatre General Hospital and the 178 patients in Army Medical Centre of PLA were assigned into the external validation set 1 and 2,respectively.Least absolute shrinkage and selection operator(LASSO)regression was employed to identify the optimal radiomic features,which were subsequently used to construct a radiomics model.Univariate and multivariate logistic regression analyses were applied to identify clinical features associated with EGFR mutation,thereby developing a clinical model.The radiomic and clinical features were subsequently combined to develop a comprehensive model.All the 3 classification models were built using random forest(RF)machine learning.The area under curve(AUC),accuracy,sensitivity and specificity were utilized to evaluate the predictive performance of the models.Calibration curve was plotted to assess the goodness of fit of the comprehensive model,while decision curve analysis was performed to assess the clinical utility of the model.Results The AUC value of the radiomics model was 0.762 4(95%CI:0.692 4～0.825 1),0.745 4(95%CI:0.671 1～0.814 3),and 0.724 7(95%CI:0.639 7～0.801 6),respectively,in the internal validation set,external validation set 1,and external validation set 2;The AUC value of the clinical prediction model was 0.691 7(95%CI:0.627 9～0.757 6),0.652 5(95%CI:0.576 7～0.729 1),and 0.779 2(95%CI:0.712 5～0.847 3),respectively in the above sets in turn;The comprehensive model constructed based on clinical features and radiomic features showed the best predictive efficacy,with an AUC value of 0.818 0(95%CI:0.757 7～0.874 3),0.782 4(95%CI:0.703 1～0.848 2),and 0.796 6(95%CI:0.718 1～0.868 6),respectively in the above sets.Calibration curve analysis indicated that the comprehensive model had a good fit,while decision curve analysis revealed that the model provided a favorable net benefit.Conclusion Our comprehensive model constructed based on chest CT radiomic features and clinical characteristics shows superior predictive performance for EGFR gene mutations in NSCLC across multiple center datasets,which may be helpful for clinical decision-making for treatment strategies.

Objective To investigate the effect of gender on prognosis after transcatheter patent foramen ovale(PFO)closure in patients with cerebral infarction or transient ischemic attack.Methods A retrospective cohort study was conducted involving patients with cerebral infarction or transient ischemic attack(TIA)who underwent PFO closure at our hospital between January 2013 and December 2023.The patients were grouped by gender,and related data were collected,including age,comorbidities,Risk of Paradoxical Embolism(RoPE)score,laboratory results,findings of transthoracic/transesophageal echocardiography(TTE/TEE),and post-procedural complications,such as device-related thrombosis(DRT),recurrent stroke,bleeding,and atrial fibrillation(AF).Results A total of 112 patients were enrolled,including 59 males and 53 females,at a mean age of 42.47±12.35 years.The females had significantly higher preoperative RoPE score than the males(6.6±1.4 vs 6.0±1.5,P=0.046),and a statistical difference was observed in the distribution of infarction sites between them(Chi-square=10.25,P=0.006),indicating that the males were prone to posterior circulation infarction.Intraoperative transthoracic echocardiography revealed a greater distance from the PFO to the aortic root in the females(9.3±2.4 mm vs 7.6±2.0 mm,P<0.001).During a median follow-up of 4 years,the male group had 1 case of myocardial infarction,1 cerebral hemorrhage,1 paroxysmal AF,2 gingival bleeding episodes,and 1 skin ecchymosis.In the female group,1 case experienced pulmonary embolism,1 paroxysmal atrial fibrillation,3 gingival bleeding episodes,2 skin ecchymoses,2 recurrent cerebral infarctions,and 2 recurrent TIAs.There was no statistical difference in overall adverse events between gender(P=0.291).Although the females had higher rates of recurrent cerebral infarction and TIA,this difference lacked statistical significance(P=0.222).Multivariate Cox regression analysis indicated that after adjusting for various potential confounding factors,such as RoPE score,age,hypertension,coronary heart disease,and other factors,gender was not an independent predictor of composite endpoint events after surgery.Conclusion Gender does not significantly affect overall prognosis after PFO closure in patients with cerebral infarction or TIA.However,females showed a trend toward higher rates of recurrent cerebral infarction and TIA.

Objective To evaluate the predictive value of deep learning features from chest CT images combined with clinical and radiomics features for epidermal growth factor receptor(EGFR)mutations in non-small cell lung cancer(NSCLC).Methods This case-control study retrospectively analyzed clinical and imaging data of 1 070 NSCLC patients from radiology departments at three hospitals(January 2013 to October 2023).Patients were divided into:a training set(n=502)and internal validation set(n=217)via 7∶3 randomization of 719 cases from the First Affiliated Hospital of Army Medical University;external validation set 1(n=173)from General Hospital of Eastern Theater Command;external validation set 2(n=178)from Daping Hospital of Army Medical University.Deep learning features were extracted using a 2.5D convolutional neural network(CNN)with ResNet101 backbone,radiomics features were derived from CT images,and clinical risk factors were identified to construct models.An integrated model combined deep learning,clinical,and radiomics features.All four models were developed using random forest(RF)classifiers.Calibration curves assessed goodness-of-fit,and decision curve analysis(DCA)evaluated clinical utility.Results The deep learning model achieved AUCs of 0.833 7(95%CI:0.770 6～0.884 7),0.815 1(0.741 6～0.882 8),and 0.810 1(0.745 2～0.873 6)in the internal and two external validation sets,respectively.Clinical models yielded AUCs of 0.731 0(0.660 2～0.802 1),0.746 0(0.666 4～0.824 9),and 0.813 4(0.743 1～0.883 6);radiomics models showed AUCs of 0.762 4(0.692 4～0.825 1),0.745 4(0.671 1～0.814 3),and 0.724 7(0.639 7～0.801 6).The integrated model demonstrated optimal performance with AUCs of 0.905 5(0.857 0～0.945 4),0.832 7(0.763 3～0.896 4),and 0.889 0(0.834 4～0.934 3).DCA indicated significant net benefit for EGFR prediction at threshold probabilities of 0.15～0.85 using the integrated model.Conclusion Deep learning features from CT images effectively predict EGFR mutation status in NSCLC.The integrated model combining deep learning,clinical,and radiomics features further enhances predictive performance.

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 explore the value of artificial intelligence(AI)based multi-parameter coronary CT angiography(CCTA)features combined with clinical indicators for predicting coronary plaque progression.Methods Totally 143 coronary atherosclerosis(AS)patients were retrospectively enrolled and divided into progression group(arithmetic average annual growth rate of plaque load＞1％,n=73)and non-progression group(arithmetic average annual growth rate of plaque load＜1％,n=70).The baseline clinical data,CT-derived fractional flow reserve(CT-FFR),perivascular fat attenuation index(FAI),and quantitative plaque features were collected and compared between groups.For variables being statistically different between groups,those had collinearity with others were excluded,and then multivariable logistic regression was used to screen independent predictors of plaque progression from the retained variables,and a combined model was constructed.Receiver operating characteristic(ROC)curve was drawn,and the area under the curve(AUC)was calculated to evaluate the predictive efficacy of this model.Results Progression group had higher proportions of hypertension and diabetes,higher apolipoprotein A1(ApoA1)and high-sensitivity C-reactive protein(hs-CRP)levels but lower high-density lipoprotein cholesterol(HDL-C)levels than non-progression group(all P＜0.05).Progression group showed smaller minimum lumen area and lower CT-FFR,but greater degree of lumen stenosis,total plaque volume,plaque load,non-calcified plaque volume,lipid-rich plaque volume,fibrolipid plaque volume and FAI values than non-progression group(all P＜0.05).Plaque types were different between groups(P＜0.05).Diabetes,low HDL-C,small minimum lumen area and large lipid-rich plaque volume were all independent predictors of plaque progression in patients with coronary AS(all P＜0.05),and the AUC of the combined model for predicting plaque progression was 0.859.Conclusion Multi-parameter CCTA features based on AI combined with clinical indicators could be used to effectively predict progression of coronary AS plaque.

In order to effectively prevent and control the occurrence of catheter-associated urinary tract infection and ensure the safety of both patients and medical personnel,the National Health Commission of the People's Re-public of China officially released the recommended health industry standard"Standard for prevention and control of catheter-associated urinary tract infection"(WS/T862-2025)in Aug.2025.This paper provides an interpreta-tion of the standard,covering its drafting background,basis and content,to assist relevant medical personnel in healthcare institutions in enhancing their understanding and recognition of the standard,and to further promote its implementation and enforcement.