Primary biliary cholangitis （PBC） is a chronic progressive autoimmune liver disease that is often comorbid with other connective tissue diseases （CTDs）， and such comorbidity can significantly alter the natural course or clinical phenotype of PBC or CTDs， limiting available therapeutic drugs and complicating clinical decision-making. Due to the involvement of the interdisciplinary subjects of hepatology， rheumatology， and clinical immunology and a paucity of large-scale cohort data and in-depth basic research， there is a limited understanding of such comorbidity in clinical practice， which increases the complexity of clinical diagnosis and treatment. This article summarizes the comorbidity of PBC with common CTDs such as Sjögren’‍s syndrome， systemic sclerosis， systemic lupus erythematosus， and idiopathic inflammatory myopathies， and analyzes related immune mechanisms， clinical manifestations， diagnostic challenges， treatment strategies， and prognosis. It is expected to establish PBC-CTD comorbidity cohorts through future multidisciplinary collaborations， focus on genetic background， immune mechanisms， and multi-omics approaches， elucidate pathogenesis and novel therapeutic targets， and improve the prognosis of patients by optimizing treatment strategies through precision medicine and artificial intelligence.

Over the past few decades, tumor immunotherapy has revolutionized the landscape of cancer clinical treatment. There is a flourishing development of combination strategies to improve the anti-tumor efficacy of mono-immunotherapy. However, instead of a straightforward combination of multiple therapeutics, it is more preferable to pursue a synergistic effect by designing rational combinations as well as administration strategies, which are based on a comprehensive understanding of the physiological and pathological features. In this case, the timing and spatial distribution of the combination drugs become essential factors in achieving improved therapeutic outcomes. Therefore, the concept of Sequential Drug Delivery System (SDDS) is proposed to define the spatiotemporally programmed drug delivery/release through triggers of internal conditions and/or external interventions, thus complying with the dynamic disease evolution and the human immunity. This review summarizes the recent advancements in biomaterial-based SDDSs used for spatiotemporally-tuned combination tumor immunotherapy. Furthermore, the rationales behind various engineering strategies are discussed. Finally, an overview of potential synergistic mechanisms as well as their prospects for combination immunotherapy is presented.

Objective:To explore the association between cognitive function and the level of gamma-amino-butyric acid(GABA)in anterior cingulate cortex(ACC)before and after treatment in patients with major depres-sion disorder.Methods:Totally 31 medication-naive patients with major depression disorder meeting the criteria of the Diagnostic and Statistical Manual of Mental Disorders,Fifth Edition(DSM-5)and 33 normal controls were col-lected.Each eligible patient received treatment with selective serotonin reuptake inhibitor agents for 8 weeks.The MATRICS Consensus Cognitive Battery(MCCB)was used to evaluate the cognitive function.By means of 1H magnetic resonance spectroscopy,anterior cingulate cortex GABA concentrations were measured.Results:At base-line,the concentration of ACC GABA relative to water(GABA+/W)was lower in the patient group than in the control group(P＜0.05)and increased after treatment(P＜0.05).ACC GABA+/W was negatively associated with verbal learning and visual memory score in patient group at baseline(correlation coefficient and P value were r=-0.40,P＜0.05;r=-0.42,P＜0.05,respectively).The ACC GABA+/W difference resulted of treatment in patient group was positively associated with the difference of working memory score and the difference of reasoning and problem-solving score(correlation coefficient and P value were r=0.58,P＜0.05;r=0.66,P＜0.05,respec-tively).Conclusion:The cognitive dysfunction of patients with major depression disorder may not be related to the degree of depression and anxiety.And improvement of cognitive function may be associated with increase of ACC GABA concentrations.

Objective:To evaluate the effects of different exercises on executive function, moter skills and core symptoms in children with attention deficit hyperactivity disorder (ADHD) using network Meta-analysis, and to provide evidence-based basis for the treatment of children with ADHD.Methods:The randomized controlled trails(RCTs)of exercises on children with ADHD were searched in Web of Science, PubMed, Cochrane Library, Embase, PsycINFO, CNKI, Wanfang Data, VIP and CBM databases.The retrieval time limit was from the establishment of each database to May 2023.The Cochrane quality review manual was used to evaluate the quality of the included literatures.Stata 17.0 software was used to perform network Meta-analysis.Results:Finally, twenty-four studies were enrolled including five types of exercise intervention, such as aerobic exercise, multicomponent exercise, exergaming, high intensity interval training (HIIT) and traditional low-to-moderate intensity exercise therapy (TRAD).The results of network Meta-analysis showed that: in terms of improving executive function(working memory, inhibitory control, and cognitive flexibility), motor skills and core symptoms of children with ADHD, the optimal exercise intervention was aerobic exercises (surface under the cumulative ranking were 78.9%, 73.0%, 88.7%, 77.9% and 69.9% respectively).Conclusion:Exercises can improve executive function, improve motor skills, and alleviate ADHD core symptoms.Aerobic exercise has a better effect on improving executive function, motor skills and core symptoms of ADHD children.

As a chronic metabolic disease,type 2 diabetes poses a significant threat to human health with increasing incidence.An increasing number of studies confirm that the pathogenesis of diabetes is closely related with alterations in multiple cellular signaling pathways.Although numerous studies have reported that traditional Chinese medicine compounds prevent diabetes by modulating cell signaling pathways,asystematic review of the mechanism of action of traditional Chinese medicine compounds in modulating cell signaling pathways is still lacking.Therefore,this paper summarizes the research of type 2 diabetes prevention and treatment,which was found mainly related to the signaling pathways such as PI3K/AKT,AMPK,MAPK,NF-κB,PPAR,TGF-β.This family of signaling pathways can treat type 2 diabetes by inhibiting pancreatic islet cell apoptosis,protecting pancreatic β-cell function,ameliorating insulin resistance,inhibiting hepatic gluconeogenesis,promoting glycogen synthesis,attenuating inflammation,and resisting oxidative stress.At the same time,we analyze the problems in current research and the future development trend,in order to provide a scientific theoretical basis for the drug development and clinical application of traditional Chinese medicine compound in the prevention and treatment of diabetes.

Objective:To explore the application status, research hotspots and frontiers of blended teaching mode in undergraduate nursing education at home and abroad, so as to provide reference for related research.Methods:In Chinese, CNKI, Wanfang Database and VIP, and in English, Web of Science database were used as search sources. CiteSpace was used to conduct a quantitative analysis of the retrieved literatures published from 2008 to 2022.Results:A total of 43 literatures in Chinese and 83 in English were retrieved, and the number of publications showed an overall increasing trend. The research hotspots in China focused on education reform, online learning and curriculum system evaluation, and the research frontiers were network teaching, moral education and humanistic care. Foreign research focused on the choice of education model and emphasizes interaction, and the research frontier were to carry out innovative blended teaching and pay attention to the learning experience and learning environment construction of nursing students.Conclusions:In the future, blended teaching should pay attention to carrying out innovative research and dynamic evaluation and feedback of students' satisfaction. In particular, it is necessary to strengthen the cultivation of blended learning competence among nursing teachers, aiming to promote the knowledge and skills of nursing students, and continuously optimize and innovate the application of blended learning in the field of undergraduate nursing education.

Objective:To construct a diabetes foot prediction model for adult patients with type 2 diabetes based on retrospective cohort study using data from a regional health data platform.Methods:Using Yinzhou Health Information Platform of Ningbo, adult patients with newly diagnosed type 2 diabetes from January 1, 2015 to December 31, 2022 were included in this study and divided randomly the train and test sets according to the ratio of 7∶3. LASSO regression model and bidirectional stepwise regression model were used to identify risk factors, and model comparisons were conducted with net reclassification index, integrated discrimination improvement and concordance index. Univariate and multivariate Cox proportional hazard regression models were constructed, and a nomogram plot was drawn. Area under the curve (AUC) was calculated as a discriminant evaluation indicator for model validation test its calibration ability, and calibration curves were drawn to test its calibration ability.Results:No significant difference existed between LASSO regression model and bidirectional stepwise regression model, but the better bidirectional stepwise regression model was selected as the final model. The risk factors included age of onset, gender, hemoglobin A1c, estimated glomerular filtration rate, taking angiotensin receptor blocker and smoking history. AUC values (95% CI) of risk outcome prediction at year 5 and 7 were 0.700 (0.650-0.749) and 0.715(0.668-0.762) for the train set and 0.738 (0.667-0.801) and 0.723 (0.663-0.783) for the test set, respectively. The calibration curves were close to the ideal curve, and the model discrimination and calibration powers were both good. Conclusions:This study established a convenient prediction model for diabetic foot and classified the risk levels. The model has strong interpretability, good discrimination power, and satisfactory calibration and can be used to predict the incidence of diabetes foot in adult patients with type 2 diabetes to provide a basis for self-assessment and clinical prediction of diabetic foot disease risk.

Objective:To systematically review the progress, advantages, disadvantages, precautions and future trends of virtual simulation technology used in epidemiology teaching.Methods:A systematical literature retrieval was conducted by using PubMed, Web of Science, Embase, China National Knowledge Infrastructure, Wanfang Data and VIP Paper Check System with key words of epidemiology, teaching and virtual simulation, and the literatures included were screened and classified with narrative integration method. Chinese virtual simulation teaching platforms were used to select the literatures about existing epidemiology virtual simulation teaching projects for integration and analysis.Results:A total of 22 articles were included (7 in Chinese and 15 in English), most of which were teaching projects for students majoring in Public Health. We also found 24 national first-class courses and 21 provincial first-class courses in virtual simulation of epidemiology in China. The application of virtual simulation technology in epidemiology education is still in its infancy, and the interaction degree is mostly moderate. It is mainly used in three scenarios: improving the visualization degree of complex concepts and structures, training the operational skills through low-risk and low-cost virtual environment, serving as an effective supplement to the teaching of epidemiological field investigation and response to public health emergencies. In terms of effect, it is conducive to students' understanding of epidemiology related phenomena and principles, and has the advantages of breaking through time and space constraints, reducing teaching costs and risks, improving students' attention and satisfaction and so on. However, it also faces the lack of foundation in the initial stage, and some students have problems such as psychological and physiological discomfort. In the future, we need to optimize the development process, program design and so on. At the same time, we should conduct more in-depth research on cost calculation, effect evaluation and curriculum integration.Conclusions:The application of virtual simulation technology in epidemiology education is an important part of training high-level applied public health talents. We encourage to actively carry out virtual simulation teaching in epidemiology, and train public health talents with Chinese characteristics.

Objective:To develop a prediction model for the risk of diabetic retinopathy (DR) in patients with newly diagnosed type 2 diabetes mellitus (T2DM).Methods:Patients with new diagnosis of T2DM recorded in Yinzhou Regional Health Information Platform between January 1, 2015 and December 31, 2022 were included in the study. The predictor variables were selected by using Lasso-Cox proportional hazards regression model. Cox proportional hazards regression models were used to establish the prediction model for the risk of DR. Bootstrap method (500 resamples) was used for internal validation, and the performance of the model was assessed by C-index, the receiver operating characteristic curve and area under the curve (AUC), and calibration curve.Results:The predictor variables included in the final model were age of T2DM onset, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, estimated glomerular filtration rate, and history of lipid-lowering agent and angiotensin converting enzyme inhibitor uses. The C-index of the final model was 0.622, and the mean corrected C-index was 0.623 (95% CI: 0.607-0.634). The AUC values for predicting the risk of DR after 3, 5, and 7 years were 0.631, 0.620, and 0.624, respectively, with a high degree of overlap of the calibration curves with the ideal curves. Conclusion:In this study, a simple and practical risk prediction model for DR risk prediction was developed, which could be used as a reference for individualized DR screening and intervention in newly diagnosed T2DM patients.

Objective:To construct a risk prediction model for diabetes kidney disease (DKD).Methods:Patients newly diagnosed with type 2 diabetes mellitus (T2DM) between January 1, 2015, and December 31, 2022, were selected as study subjects from the Yinzhou Regional Health Information Platform in Ningbo City. The Lasso method was used to screen the risk factors, and the DKD risk prediction model was established using Cox proportional hazard regression models. Bootstrap 500 resampling was applied for internal validation.Results:The study included 49 706 subjects, with an median ( Q1, Q3) age of 60.00 (50.00, 68.00) years old, and 55% were male. A total of 4 405 subjects eventually developed DKD. Age at first diagnosis of T2DM, BMI, education level, fasting plasma glucose, glycated hemoglobin A1c, urinary albumin, past medical history (hyperuricemia, rheumatic diseases), triglycerides, and estimated glomerular filtration rate were included in the final model. The final model's C-index was 0.653, with an average of 0.654 after Bootstrap correction. The final model's area under the receiver operating characteristic curve for predicting 4-year, 5-year, and 6-year was 0.657, 0.659, and 0.664, respectively. The calibration curve was closely aligned with the ideal curve. Conclusions:This study constructed a DKD risk prediction model for newly diagnosed T2DM patients based on real-world data that is simple, easy to use, and highly practical. It provides a reliable basis for screening high-risk groups for DKD.