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 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.

Crisaborole, a phosphodiesterase-4 inhibitor, has been approved for the treatment of mild to moderate atopic dermatitis in China. In addition, crisaborole ointment has been reported for the successful treatment of other inflammatory skin disorders. This review summarizes mechanisms of action of crisaborole and its application to the treatment of atopic dermatitis, psoriasis, vitiligo, seborrheic dermatitis, inflammatory linear verrucous epidermal nevus, lichen simplex chronicus, prurigo pigmentosa, alopecia areata, plasma cell balanitis, and lichen planus.

Objective To investigate the effect of day chemotherapy on medical expenses of breast cancer patients.Methods The medical expenses of breast cancer patients in the First Affiliated Hospital of Guangxi Medical Universtiy from January 2019 to September 2023 were retrospectively investigated.Interrupted time series analysis was used to analyze the changes and trend of medical expenses and expenses structure of breast cancer before and after the implementation of day chemotherapy.Results The number of 8066 cases with breast cancer were included in this study.After day chemotherapy,the total expenses and each expense detail decreased significantly(P＜0.001).The total expenses,medicine expense,examination expenses and other expenses showed an upward trend before implementation of day chemotherapy(P＜0.05),the laboratory expenses showed a downward trend before implementation of day chemotherapy(P＜0.001).The total medical expenses decreased by 7306.34 yuan immediately after the implementation of day chemotherapy.The expenses of laboratory test,treatment,examination,nursing,medicine,health materials and other expenses were decreased by 599.59 yuan,1674.34 yuan,1470.66 yuan,91.37 yuan,3473.91 yuan,231.51 yuan and 436.73 yuan,respectively.Conclusion The implementation of day chemotherapy for breast cancer has significantly reduced all kinds of costs,which is conductive to reducing disease burden of patients.

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.