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.

Mitochondrial dysfunction in chondrocytes is a key pathogenic factor in osteoarthritis (OA), but directly modulating mitochondria in vivo remains a significant challenge. This study is the first to verify a correlation between mitochondrial dysfunction and the downregulation of the FOXO3 gene in the cartilage of OA patients, highlighting the potential for regulating mitophagy via FOXO3 gene modulation to alleviate OA. Consequently, we developed a chondrocyte-targeting CRISPR/Cas9-based FOXO3 gene-editing tool (FoxO3) and integrated it within a nanoengineered 'truck' (NETT, FoxO3-NETT). This was further encapsulated in injectable hydrogel microspheres (FoxO3-NETT@SMs) to harness the antioxidant properties of sodium alginate and the enhanced lubrication of hybrid exosomes. Collectively, these FoxO3-NETT@SMs successfully activate mitophagy and rebalance mitochondrial function in OA chondrocytes through the Foxo3 gene-modulated PINK1/Parkin pathway. As a result, FoxO3-NETT@SMs stimulate chondrocytes proliferation, migration, and ECM production in vitro, and effectively alleviate OA progression in vivo, demonstrating significant potential for clinical applications.

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 effects of the core symptoms of attention deficit hyperactivity disorder (ADHD) and behavior problems on the sleep of children with ADHD and their correlation.Methods:In this cross-sectional study, a total of 289 children aged 6-12 years with ADHD who visited the Outpatient Department of the Center for Cognition and Sleep at the People′s Hospital of Guangxi Zhuang Autonomous Region from June 2021 to December 2022 were selected.The Swanson, Nolan and Pelham Rating Scale Ⅳ was used to assess ADHD core symptoms (attention deficit and hyperactivity-impulsivity), the Conners Parent Symptom Questionnaire was used to assess behavior problems (conduct, learning, psychosomatic symptoms, and anxiety), and the Sleep Disturbance Scale for Children (SDSC) was used to assess sleep.Multivariate Logistic regression, multivariate linear regression, and path analysis were performed for statistical analysis of the data.Results:Multivariate Logistic regression analysis showed that after controlling age, gender, verbal intelligence quotient and performance intelligence quotient, attention deficit ( P=0.008, OR=2.60, 95% CI: 1.28-5.28), psychosomatic symptoms ( P=0.027, OR=6.77, 95% CI: 1.24-36.93) and anxiety ( P=0.013, OR=3.96, 95% CI: 1.43-11.67) increased the risk of sleep disturbances in children with ADHD (SDSC score >39 points).Path analysis showed that attention deficit ( β=0.244, P=0.001), psychosomatic symptoms ( β=0.114, P=0.046) and anxiety ( β=0.216, P<0.001) directly affected the SDSC score.Hyperactivity-impulsivity indirectly affected the SDSC score through psychosomatic symptoms ( β=0.142, P=0.039), and attention deficit indirectly affected the SDSC score through anxiety ( β=0.321, P<0.001). Conclusions:The severer the core symptoms of ADHD and behavior problems are, the higher the degree of sleep disturbances in children with ADHD is.Psychosomatic symptoms and anxiety are directly associated with the sleep disturbances of ADHD children.Attention deficit has direct or anxiety-mediated indirect impact on the sleep of ADHD children.Hyperactivity-impulsivity indirectly affects the sleep of children with ADHD through psychosomatic symptoms.

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 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.