BackgroundEmotional disorders in adolescents have emerged as a prominent issue in recent years. Current mainstream clinical assessment approaches for such conditions predominantly rely on interviews and rating scales， which are limited by inherent drawbacks such as high subjectivity and recall bias. Accordingly， there exists an urgent clinical need for the development of objective， quantifiable auxiliary diagnostic tools.In previous studies， frontal electroencephalography （EEG） has demonstrated significant value in assessing depressive and anxiety. However， the lack of standardized quantitative metrics and intuitive visual analytical approaches has severely restricted clinical interpretability of EEG data and diminished patient engagement. To address these key limitations， the present study proposes an innovative analytical framework that converts frontal EEG signals into quantifiable visual metrics to enhance clinical comprehension and acceptance. ObjectiveTo explore the value of frontal EEG in assessing anxiety， depression， and sleep quality in adolescents with emotional disorders， with the aim of providing objective auxiliary tools for clinical diagnosis and assessment of adolescents with emotional disorders. MethodsThis cross-sectional study recruited 105 adolescents aged 12-18 years who visited the outpatient department of a specialized mental hospital in Beijing from April 2023 to April 2024. All participants met the diagnostic criteria for mood （affective） disorders or anxiety disorders in the International Classification of Diseases， tenth edition （ICD-10）. Frontal EEG signals were collected within a big data analytics-driven framework and further processed by EEG system to generate six quantitative cerebral function indices， namely brain load， tension and excitement， emotional stress， sleepiness index， cerebral vitality， and cerebral fatigue. In addition， validated standardized scales， including the Self-rating Anxiety Scale （SAS）， the Self-rating Depression Scale （SDS）， and the Pittsburgh Sleep Quality Index （PSQI）， were administered for anxiety， depressive symptoms， and sleep quality， respectively. ResultsIn adolescent patients with emotional disorders， the SAS score exhibited significant positive correlations with brain load （r_s=0.328， P<0.01）， emotional stress （r_s=0.341， P<0.01）， and cerebral fatigue （r_s=0.286， P<0.01）. The SDS score was positively correlated with brain load （r_s=0.275， P<0.01）， emotional stress （r_s=0.241， P<0.05）， and cerebral fatigue （r_s=0.311， P<0.01）， while showing a significant negative correlation with cerebral vitality （r_s=-0.212， P<0.05）. Additionally， the PSQI total score demonstrated positive correlations with brain load （r_s=0.340， P<0.01）， emotional stress （r_s=0.322， P<0.01）， and cerebral fatigue （r_s=0.229， P<0.05）. ConclusionFrontal EEG-derived indices， including brain load， emotional stress， cerebral fatigue and cerebral vitality， may serve as objective markers for reflecting anxiety， depression， and sleep quality in adolescents with emotional disorders. ［Funded by Beijing High level Innovation and Entrepreneurship Talent Support Program （number， 202504841041）； Horizontal Joint Project］

Home rehabilitation is the main rehabilitation model for elderly patients with hip fractures in China, and the application of digital health technology shows great potential in improving the quality of home rehabilitation for this population. This paper describes the concept of digital health technology, the current application status of different types of digital health technology in home rehabilitation for elderly patients with hip fractures, and discusses existing issues and future prospects, aiming to provide a reference for digital home rehabilitation nursing for elderly hip fracture patients.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Creutzfeldt-Jakob disease(CJD)is a rapidly progressive and fatal neurodegenerative disorder caused by prions,with certain infectivity and iatrogenic transmission risks.With the rapid progress and application of new dia-gnostic biomarkers and detection methods,as well as the construction and improvement of surveillance and reporting systems,the detection of CJD in patients domestically and internationally has shown an increasing trend year by year.Due to its long incubation period and heterogeneity of early symptoms,early identification and diagnosis of the disease is difficult,increasing the risk of transmission within medical institutions.Currently,there is a lack of con-sensus on the infection prevention and control of CJD.In order to timely identify and diagnose CJD as well as effec-tively block its transmission in medical institutions,this consensus summarizes 15 clinical concerns and formulates 24 specific recommendations based on the latest domestic and international research findings and clinical evidence,as well as combines with clinical practice,aiming to standardize healthcare-associated infection prevention and control measures for CJD and reduce its transmission risk in medical institutions.

Objective To develop and compare the predictive performance of five machine learning models for adverse postoperative outcomes in cardiac surgery patients, and to identify key decision factors through SHapley Additive exPlanations (SHAP) interpretability analysis. Methods A retrospective collection of perioperative data (including demographic information, preoperative, intraoperative, and postoperative indicators) with 88 variables was conducted from adult cardiac surgery patients at the First Affiliated Hospital of Xinjiang Medical University in 2023. Adverse postoperative outcomes were defined as the occurrence of acute kidney injury and/or in-hospital mortality during the postoperative hospitalization period following cardiac surgery. Patients were divided into an adverse outcome group and a favorable outcome group based on the presence of adverse postoperative outcomes. After screening feature variables using the least absolute shrinkage and selection operator (LASSO) regression method, five machine learning models were constructed: eXtreme gradient boosting (XGBoost), random forest (RF), gradient boosting machine (GBM), light gradient boosting machine (LightGBM), and generalized linear model (GLM). The dataset was randomly divided into a training set and a test set at a 7 : 3 ratio using stratified sampling, with postoperative outcome as the stratification factor. Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and F1 Score. The SHAP method was applied to analyze feature contribution. Results A total of 639 patients were included, comprising 395 males and 244 females, with a median age of 62 (55, 69) years. The adverse outcome group consisted of 191 patients, while the favorable outcome group included 448 patients, resulting in an adverse postoperative outcome incidence of 29.9%. Univariate analysis showed no significant differences between the two groups for any variables (P>0.05). Using LASSO regression, 16 feature variables were selected (including cardiopulmonary bypass support time, blood glucose on postoperative day 3, creatine kinase-MB isoenzyme, systemic inflammatory response index, etc.), and five machine learning models (GLM, RF, GBM, LightGBM, XGBoost) were constructed. Evaluation results demonstrated that the XGBoost model exhibited the best predictive performance on both the training set (n=447) and test set (n=192), with area under the curve values of 0.761 [95%CI (0.719, 0.800) ] and 0.759 [95%CI (0.692, 0.818) ], respectively. It also significantly outperformed other models in positive predictive value, and balanced accuracy in the test set. Decision curve analysis further confirmed its clinical utility across various risk thresholds. SHAP analysis indicated that variables such as cardiopulmonary bypass support time, blood glucose on postoperative day 3, creatine kinase-MB isoenzyme, and inflammatory markers (SIRI, NLR, CAR) had high contributions to the prediction. Conclusion The XGBoost model effectively predicts adverse postoperative outcomes in cardiac surgery patients. Clinically, attention should be focused on cardiopulmonary bypass support time, postoperative blood glucose control, and monitoring of inflammatory levels to improve patient prognosis.

HDAC7, a member of class IIa HDACs, plays a pivotal regulatory role in tumor, immune, fibrosis, and angiogenesis, rendering it a potential therapeutic target. Nevertheless, due to the high similarity in the enzyme active sites of class IIa HDACs, inhibitors encounter challenges in discerning differences among them. Furthermore, the substitution of key residue in the active pocket of class IIa HDACs renders them pseudo-enzymes, leading to a limited impact of enzymatic inhibitors on their function. In this study, proteolysis targeting chimera (PROTAC) technology was employed to develop HDAC7 drugs. We developed an exceedingly selective HDAC7 PROTAC degrader B14 which showcased superior inhibitory effects on cell proliferation compared to TMP269 in various diffuse large B cell lymphoma (DLBCL) and acute myeloid leukemia (AML) cells. Subsequent investigations unveiled that B14 disrupts BCL6 forming a transcriptional inhibition complex by degrading HDAC7, thereby exerting proliferative inhibition in DLBCL. Our study broadened the understanding of the non-enzymatic functions of HDAC7 and underscored the importance of HDAC7 in the treatment of hematologic malignancies, particularly in DLBCL and AML.

Global public health faces substantial challenges from malignant tumors and infectious diseases. Vaccination provides an approach for treating and preventing these diseases. Oral vaccinations are particularly advantageous in disease treatment and prevention due to their non-invasive nature, high patient compliance, convenience, cost-effectiveness, and capacity to stimulate comprehensive and adaptive immune responses. However, the overwhelming majority of oral vaccines remain in experimental development, struggling with clinical and commercial translation due to their suboptimal efficacy. Thus, enhancing scientists' understanding of the interaction between vaccines and gastrointestinal immune system, creating antigen delivery systems suitable for the gut mucosal environment, developing more potent antigenic epitopes, and using personalized combination therapies are critical for advancing the next generation of oral vaccines. This article explores the fundamental principles and applications of current oral anti-tumor and anti-infective vaccines and discusses considerations necessary for designing future oral vaccines.

OBJECTIVE To analysis the clinical and pathological characteristics of lymph nodes metastasis in pediatric patients with differentied thyroid carcinoma(pDTC),and to develop a nomogram for predicting lymph nodes metastasis in patients with pDTC.METHODS Four cohorts were integrated in this study,including internal training cohort(1419 cases)and validation cohort(609 cases)from the Surveillance,Epidemiology,and End Results(SEER)database of the National Cancer Institute,TCGA-pDTC cohort(20 cases from the Cancer Genome Atlas Program)and YHD-pDTC cohort(39 cases from true world).The least absolute shrinkage and selection operator(Lasso)and logistic regression were used to screen factors related to lymph nodes metastasis in the presence and exclusion of T stage in pDTCs based on internal training cohort and construct nomograms.Calibration curve,receiver operating characteristic(ROC)curve,area under the ROC curve(AUC)and decision curve analysis(DCA)were used to evaluate model performance in the other three cohorts.RESULTS Multivariate logistic regression results showed that pathology[OR(95%CI):0.020(0.010-0.060),P<0.001],M stage[OR(95%CI):29.550(3.190-273.490),P=0.003],and T stage[OR(95%CI):2.210(1.820-2.680),P<0.001]were independent factors affecting lymph nodes metastasis in pDTCs.Through the internal training set,internal validation set,YHD-pDTC cohort and TCGA-pDTC cohort,the AUC values were 0.734(0.709,0.758),0.752(0.716,0.788),0.969(0.920,1),and 0.600(0.342,0.858)in that order,and the calibration curves are close to the perfect reference line.T stage is a better predictor of lymph nodes metastasis than extraperitoneal intrusion and tumor size.CONCLUSION Pathology,M stage,and T stage are risk factors for predicting lymph nodes metastasis in pDTCs.The nomogram established on this basis can make individualized predictions of the probability of lymph nodes metastasis in pDTCs.

Home rehabilitation is the main rehabilitation model for elderly patients with hip fractures in China, and the application of digital health technology shows great potential in improving the quality of home rehabilitation for this population. This paper describes the concept of digital health technology, the current application status of different types of digital health technology in home rehabilitation for elderly patients with hip fractures, and discusses existing issues and future prospects, aiming to provide a reference for digital home rehabilitation nursing for elderly hip fracture patients.

A 66-year-old female patient with lung adenocarcinoma was treated with crizotinib [the first-generation anaplastic lymphoma kinase (ALK) inhibitors] 250 mg twice daily. Prior to treatment, the patient's liver and kidney functions were normal. One month after treatment, her serum creatinine (Scr) was 85 μmol/L, and alanine aminotransferase (ALT) was 115 U/L. After discontinuing crizotinib for 10 days, both Scr and ALT returned to normal. One month later, the patient underwent a right lower lobectomy. Crizotinib was restarted postoperatively, and she developed symptoms of lower limb edema and poor appetite. After more than 3 months of treatment, her Scr increased to 129 μmol/L, ALT was 96 U/L, and aspartate aminotransferase (AST) was 83 U/L. Crizotinib was then switched to alectinib (the second-generation ALK inhibitors) 600 mg orally twice daily, and the patient's gastrointestinal symptoms and liver function were rapidly improved. However, her Scr continued to increase gradually (140-150 μmol/L). Renal biopsy pathology indicated IgA nephropathy and acute tubular injury. After 4 months of alectinib treatment, Scr was 174 μmol/L, and the drug was promptly discontinued. One month after discontinuation, Scr decreased to 125 μmol/L. Due to tumor progression, the patient restarted alectinib at a reduced dose (300 mg twice daily). Three months later, Scr increased to 177 μmol/L. Subsequently, alectinib was replaced with lorlatinib (the third-generation ALK inhibitors) 100 mg once daily due to tumor progression. After 6 months of treatment, the tumor condition was controlled, and Scr decreased to 124 μmol/L.