The major obstacle to optimizing the design of rare disease coverage is the fragmented decision-making process among medical services, pharmaceuticals, and medical insurance departments. There is an urgent need to realize data sharing and digital empowerment, as well as to adopt top-level design and systematic decision-making. It is also crucial to establish mechanisms, facilitated by digital intelligence, for sharing power and responsibilities, and assessing rewards and punishments. Furthermore, there is an urgent need to incorporate the theories of collaborative governance, digital governance, and the full life cycle into the entire process, which includes patient classification, diagnosis and treatment, medical assistance, medication protection, and health insurance fund management for rare diseases. This integration aims to provide theoretical reference for the effective linkage of medical services, pharmaceuticals, and medical insurance, and to improve the efficiency and equity of resource allocation in the public sector.

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.

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.

Traditional evidence-based medicine has been essential in medical practice and health decision-making. However, it has also continuously exposed shortcomings such as low efficiency in evidence generation, narrow scope of coverage, and imperfect integration strategies, making it challenging to serve clinical diagnosis and treatment and regulatory decision-making. Therefore, it is urgent to adapt to the development of cutting-edge technology and to expand and improve the concept of evidence-based medicine. Deep evidence-based medicine proposed in 2023 aims to advocate the innovative use of the latest artificial intelligence and natural language processing technologies, comprehensively expanding the breadth, depth, and integrability of evidence and improving the efficiency of evidence generation and integration. Building a new generation of evidence-based decision-making ecosystems based on deep evidence-based medicine has broad prospects for practical application. It can promote the development of evidence retrieval, generation, integration, dissemination, transformation, and application, deeply explore imaging, multi-omics, and real-world data to increase the utilization potential of real-world evidence, establish dynamic literature management platforms and decision support tools, reduce resource waste, and promote evidence flow. Utilizing this system can help obtain individual-centered comprehensive clinical evidence and play a significant role in talent training, reforming evidence-based teaching, popularizing science, and ultimately promoting the goal of "One Health".

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.

Objective:Diabetic retinopathy (DR) has been reported as the leading cause of blindness among diabetic adults, which is closely related to poor quality of life and increased burden of disability. This study aimed to aggregate the optimally available evidence on modifiable risks of DR.Methods:Until June 2023, PubMed, Cochrane Library, CNKI, and Wanfang databases were used to retrieve Meta-analysis about various risk factors for DR, and Meta-analysis were analyzed and summarized. R 4.3.2 software was used for each Meta-analytic association to calculate the effect size, 95% CI, heterogeneity, small-study effects, excess significance bias, and 95% prediction intervals. The credibility of significant evidence was graded. Results:We captured 23 eligible papers (72 associations) covering a wide range of medication use, concomitant diseases, daily intervention, biomarkers, lifestyle, and physical measurement index. Among them, higher HbA1c variability ( RR=1.45, 95% CI: 1.26-1.66) and urine microalbumin positive ( OR=2.44, 95% CI: 1.99-2.97) were convincing (grade Ⅰ) evidence, and insulin use ( RR=3.48, 95% CI: 2.14-5.67) was highly suggestive (grade Ⅱ) evidence. Moreover, hypertension ( OR=2.03, 95% CI: 1.06-3.97), poor glycemic control ( OR=4.35, 95% CI: 1.47-12.85), positive macroalbuminuria ( OR=8.42, 95% CI: 3.52-20.15), long sleep duration ( OR=2.05, 95% CI: 1.37-3.05), vitamin D deficiency ( OR=2.02, 95% CI: 1.17-3.50), periodontitis ( OR=4.51, 95% CI: 1.76-11.55) were the main risk factors for DR. Intensive blood pressure intervention ( RR=0.78, 95% CI: 0.65-0.94), dietary control ( OR=0.64, 95% CI: 0.47-0.89) and moderate intensity physical activity ( RR=0.76, 95% CI: 0.59-0.97) yielded significant protective associations with DR. Conclusions:Intensive blood pressure glycemic control, and a healthy lifestyle pattern could reduce the risk of DR. This study provides the evidence to identify high-risk populations and recommends rational treatment options and healthy living interventions.

Objective To explore the effect of transcranial direct current stimulation(tDCS)based on music therapy on insomnia. Methods From July,2023 to April,2024,70 patients with insomnia in the Fourth Affiliated Hospital of Soochow Univer-sity were randomly divided into control group(n=35)and observation group(n=35).Both groups accepted mu-sic therapy;moreover,the observation group accepted tDCS,and the control group accepted sham tDCS,for four weeks.They were assessed with Pittsburgh Sleep Quality Index(PSQI)total score and sub-score,Hamilton De-pression Scale 17-item(HAMD-17),Hamilton Anxiety Scale(HAMA),Chinese version of Stress Perception Scale(CPSS);and the relative power of resting-state electroencephalography(EEG)and mean blood flow veloci-ty(Vm)of each cerebral artery with transcranial Doppler were measured before and after treatment. Results Five cases dropped down in the control group,and four in the observation group.PSQI total score and sub-score,HAMD-17 score,HAMA score and CPSS score(|t|>3.503,P<0.01)in the observation group decreased after treatment,and were less in the observation group than in the control group(|t|>2.304,P<0.05),except sleep duration,sleep efficiency and CPSS scores.The relative power of δ and θ increased in the observation group,and decreased in α,β and γ(|t|>6.468,P<0.001),and were better in the observation group than in the control group(|t|>2.395,P<0.05).The Vm of each artery increased in the observation group(|t|>4.624,P<0.001),and were more in the observation group than in the control group(|t|>2.147,P<0.05). Conclusion tDCS based on music therapy may further improve sleep quality and EEG activity,increase cerebral blood flow velocity,and reduce adverse emotions in insomnia patients.

Objective:To analyze the epidemiological characteristics and economic burden of palmoplantar pustulosis (PPP) in China.Methods:A population-based retrospective study was conducted using the data from China′s Urban Basic Medical Insurance data from January 1, 2012, to December 31, 2016. International Classification of Diseases code and diagnoses in Chinese for PPP were used to identify cases and estimate the prevalence, incidence, and cost. Subgroup analyses were performed according to age and sex, and sensitivity analyses were conducted to evaluate the robustness of the results. Age-adjusted prevalence rates were calculated based on the 2010 national census data.Results:The crude prevalence and incidence rate of PPP in 2016 were 2.730/100 000 (95% CI: 2.218/100 000-3.242/100 000) and 1.556/100 000 (95% CI: 1.154/100 000-1.958/100 000), and the prevalence rate of females (2.910/100 000) was higher than that of males (2.490/100 000, χ2=97.48, P=0.001). The incidence rate of females (1.745/100 000) was also higher than that of males (1.418/100 000, χ2=85.02, P=0.001). The age peak of incidence and prevalence of patients with PPP was in the 30-39-year age group and a small peak existed in the 0-3-year age group among people under 20 years old. From 2012 to 2016, the average number of visits was (2.44±0.04) per patient, and the total per-capita cost per year was (982.40±39.19) yuan. Conclusion:In 2016, the prevalence and incidence rate of PPP in China were higher in females than in males, and the highest age peak was in the 30-39-year age group.

Objective:To explore the relationship among sleep phenotypes, attention deficit and hyperactivity impulsivity (ADHD) symptoms and cognitive information processing in children with ADHD.Methods:A total of 244 children with ADHD aged 6-12 were selected from December 2021 to December 2022.Swanson, Nolan and Pelham rating scale Ⅳ(SNAP-Ⅳ) was used to evaluate the core symptoms of ADHD.Sleep disturbance scale for children (SDSC) was used to evaluate six sleep phenotypes((disorders in initiating and maintaining sleep (DIMS), sleep breathing disorders(SDB), sleep-wake transition disorders(SWTD), disorders of arousal(DA), disorders of excessive somnolence (DOES), and Nocturnal hyperhidrosis(SHY)). Das-Naglieri cognitive function assessment system (DN-CAS) was used to evaluate the cognitive information processing (planning, simultaneous processing, attention and successive processing). Descriptive statistical analysis, Spearman correlation analysis, and mediation analysis were conducted by SPSSAU 23.0 and Zstats software, respectively.Results:Correlation analysis showed that DIMS, SDB, SWTD, DA and DOES were significantly and positively correlated with attention deficit ( r=0.190-0.349, all P<0.01).DIMS(2.14(1.71, 2.57)), SWTD(1.67(1.33, 2.00)) and SHY(2.00(1.50, 3.00)) were significantly positively correlated with hyperactive impulsivity (1.44(1.00, 2.00))( r=0.193, 0.242, 0.133, P<0.05). Attention deficit(1.78(1.44, 2.33)) was significantly and negatively correlated with successive processing(105.00(96.00, 112.00)) ( r=-0.127, P<0.05). The results of multiple linear regression analysis showed that after controlling for sex, age, verbal IQ and operational IQ, DIMS ( β=0.152, P<0.05) and SWTD ( β=0.178, P<0.05) had significant positive predictive effects on hyperactive impulsivity symptoms. DOES ( β=0.259, P<0.01) had significant positive predictive effects on attention deficit symptoms. Attention deficit ( β=-0.183, P<0.05) had a significant negative predictive effect on successive processing. Mediation effect analysis showed that attention deficit played a complete mediating role between DOES and successive processing(effect=-0.179, Bootstrap 95% CI=-0.196--0.110). Conclusion:Different sleep phenotypes are associated with ADHD core symptoms and different dimensions of cognitive information processing processes. DOES indirectly affects successive processing capability by attention deficit symptoms.