Patients with rare disease are a vulnerable group in healthcare who urgently need more attention from the government and society. Despite their overall number, they face challenges such as limited diagnosis and treatment options, poor access to medications, and heavy financial burdens. Providing better protection for this group is not only essential for public well-being, but also an important indicator of societal progress. This paper analyzes the current status and challenges of rare disease protection in China, with a focus on building diagnostic and treatment networks, optimizing the multitiered social security system, and improving pharmaceutical access mechanisms. It highlights issues such as incomplete health insurance coverage, unequal resource allocation, and insufficient incentives for drug development. To address these challenges, we suggest strengthening policies, improving the multitiered social security system, encouraging social support, and promoting medical and pharmaceutical innovation to provide comprehensive care for rare disease patients.

Objective To investigate the identification of octreotide(OCT)modified chitosan(CS)miR-155 molecular beacon nanoparticles(CS-miR-155-MB-OCT)and imaging of lung cancer cells for the early screening of lung cancer.Methods A nude mouse model of lung transplantation tumor was established by injecting A549 lung cancer cells into tail veins to establish lung xenograft models.Cre adenovirus was injected through nasal cavity,and mice were killed at 4,6,8 and 12 weeks after adenovirus injection to establish lung cancer models of atypical hyperplasia,adenoma,carcinoma in situ and adenocarcinoma of lung in LSL K-ras G12D transgenic mice at different pathological stages.Lung tissue samples were taken and observed by HE staining.Immunohistochemistry were used to detect the expression of somatostatin receptor 2(SSTR2).Real-time fluorescence quantitative PCR was used to detect miR-155 expression levels in lung xenograft models and transgenic mice at different stages of lung cancer.Then CS-miR-155-MB and CS-miR-155-MB-OCT were injected via tail vein in lung xenograft models.CS-miR-155-MB-OCT was injected via tail vein in transgenic mice models.The fluorescence signals of lung in nude mice and transgenic mice at different disease stages were imaged by living imaging system.Frozen slices of lung tissue were made.The source of fluorescence signal was detected by laser confocal scanning microscope(CLSM).Results HE staining showed that lung transplantation tumor models and lung cancer models of atypical hyperplasia,adenoma,carcinoma in situ and lung adenocarcinoma at different pathological stages were successfully constructed.Immunohistochemical analysis showed somatostatin receptor 2(SSTR2)was expressed in transplanted lung tumor and tissue at different pathological stages.In transgenic mouse models,the expression of miR-155 was gradually increased as the disease progressed(P＜0.05).In lung xenograft models,the fluorescence signals were significantly higher in the CS-miR-155-MB-OCT group than those of the CS-miR-155-MB group(P＜0.05).In transgenic mouse models,the fluorescence signals gradually increased with the gradual progression of lesions(P＜0.05).After re-imaging the lung tissue,it was found that the fluorescence signal came from lung,and CLSM showed that the fluorescence signal came from cancer cells and some normal alveolar epithelial cells.Conclusion CS-miR-155-MB-OCT can dynamically reflect the occurrence and development of lung cancer according to changes of different fluorescence intensity,thus providing a new technology for the early diagnosis of lung cancer.

Objective:To explore the latent classes of health behavior and explore the predictive factors among stroke patients.Methods:A total of 1 250 participants were recruited using cluster random sampling in September 2022. The general information, the modified Rankin scale(mRS), stroke prevention knowledge questionnaire(SPKQ), health behavior scale for stroke patients (HBS-SP), and short form-health belief model scale (SF-HBMS) were administered in the cross-sectional survey. Mplus 8.3 software was used to conduct a latent class analysis (LCA) on the health behavior of stroke patients, and SPSS 27.0 software was used to carry out multinomial Logistic regression to analyze the predictive factors of different latent classes of health behavior of stroke patients.Results:The health behavior of stroke patients obtained three latent classes: low health behaviors-lack of health responsibility group (66.9%, n=794), moderate health behaviors-poor compliance group (11.9%, n=141), and good health behaviors-insufficient exercise group (21.2%, n=251). Compared with good health behaviors-insufficient exercise group, stroke patients with shorter duration education time ( B=-0.589, OR=0.555, P=0.036), hemorrhagic stroke ( B=0.082, OR=1.086, P<0.001), fewer comorbidities ( B=-0.022, OR=0.978, P=0.026), higher mRS score ( B=-0.046, OR=1.047, P=0.004), lower SPKQ score ( B=-0.055, OR=0.947, P=0.016), and lower SF-HBMS score ( B=-0.085, OR=0.919, P<0.001) were more likely to be included in moderate health behaviors-poor compliance group. However, stroke patients with shorter duration education time ( B=-0.026, OR=0.974, P=0.003), rural areas dwelling ( B=0.800, OR=2.225, P=0.004), fewer comorbidities ( B=-0.056, OR=0.945, P<0.001), lower SPKQ score ( B=-0.101, OR=0.904, P<0.001), and lower SF-HBMS score ( B=-0.071, OR=0.931, P<0.001) were more likely to be included in low health behaviors-lack of health responsibility group. Conclusion:The health behavior of stroke patients has three latent classes. A targeted intervention should be carried out according to the characteristics of different classes to improve their health behavior levels.

Objective:To explore the potential facilitators and barriers to healthy behavior among stroke patients.Methods:Semi-structured interviews were conducted among 16 stroke patients from September 2022 to March 2023 using an objective sampling method.The interview guide was developed using the theoretical domain framework(TDF). Interviews were transcribed and refined the theme using directed content and induction analysis.Using the TDF as the initial coding framework, the themes were then merged into the most relevant domains.Finally, the correspondence between theoretical domains and the healthy behavior of stroke patients was determined based on the frequency and relationship between themes.Results:This study identified nine theoretical domains that affected the healthy behavior of stroke patients: knowledge, skills, motivation and goals, social influences, social/professional role and identity, environment context and resources, belief about capability, consequence belief and behavioral regulation.Conclusion:The healthy behavior of stroke patients is complex and influenced by several factors.The nine theoretical domains identified in this study will provide recommendations for future healthy behavior interventions for stroke patients.

Objective:To investigate the current comorbidity status among hypertension, diabetes, and dyslipidemia in residents aged 35-75 years in Tianjin and to explore the main influencing factors to provide a scientific basis for the prevention and treatment of chronic disease comorbidity.Methods:From June 2019 to November 2023, 10 districts (Hedong, Hexi, Dongli, Beichen, Nankai, Xiqing, Wuqing, Baodi, Jizhou, and Binhai New District) in Tianjin were selected as the project sites. The community and natural village was used as the primary sampling unit, and each project site selected the screening sites by cluster random sampling method. Residents aged 35-75 who lived in the screening sites for 6 months and above were surveyed by questionnaire, physical examination, and biochemical tests. The chi-square test, analysis of variance, and multivariate unconditional logistic regression analysis were used for statistical analysis. Age-standardized prevalence was based on the data of the sixth national census.Results:A total of 146 832 participants were included in this study, including 61 994 males (42.22%) and 84 838 females (57.78%), with an age of (56.83±8.84) years. The number of people with only one disease was 55 485 (37.79%), the number of people with two diseases was 36 942 (25.16%), and the number of people with three diseases was 9 683 (6.59%). The prevalence of hypertension combined with dyslipidemia was the highest (17.23%), and the standardized prevalence were 14.44%. The prevalence rates of three diseases and hypertension combined with diabetes was 6.59% and 4.98%, respectively, and the standardized prevalence was 5.42% and 4.11%, respectively. The prevalence of diabetes combined with dyslipidemia was 2.95%, and the standardized prevalence was 2.45%. Multivariate unconditional logistic regression analysis showed that advanced age (65- 75 years old: OR=2.69, 95% CI: 2.28-3.18), overweight/obesity (overweight: OR=2.21, 95% CI: 2.02-2.41; obesity: OR=4.50, 95% CI: 4.03-5.02), daily smoking ( OR=1.96, 95% CI: 1.72-2.24), regular and heavy drinking ( OR=1.63, 95% CI: 1.18-2.27), family history of hypertension/diabetes/hyperlipidemia (family history of hypertension: OR=81.17, 95% CI: 74.68-88.22; family history of diabetes: OR=15.26, 95% CI: 13.71-16.99; family history of hyperlipidemia: OR=7.13, 95% CI: 5.92-8.59), tea drinking (occasional tea drinking group: OR=1.74, 95% CI: 1.52-2.00; frequent tea drinking group: OR=2.23, 95% CI: 1.92-2.59) were risk factors for the comorbidity of hypertension, diabetes and dyslipidemia (all P<0.05), while higher education level was a protective factor (senior high school/technical secondary school: OR=0.79, 95% CI: 0.72-0.86; college/bachelor's degree and above: OR=0.60, 95% CI: 0.53-0.68, all P<0.001). Conclusions:The comorbidity rate of hypertension, diabetes, and dyslipidemia is high in residents aged 35-75 years in Tianjin. It is necessary to strengthen the co-management of blood pressure, blood glucose, and blood lipid in key populations with old age, overweight/obesity, junior high school education or below, daily smoking, daily drinking, occasional or frequent tea drinking, and family history of hypertension/diabetes/dyslipidemia, and promote a healthy lifestyle.

Objective:To understand the interaction effect of general obesity, central obesity, and dyslipidemia on the risk of hypertension to provide scientific evidence for the early prevention and control of hypertension.Methods:From 2019 to 2023, 10 of the 16 districts in Tianjin were selected as project sites. A community and a natural village were selected as monitoring sites in each project site using a multi-stage cluster random sampling method. A questionnaire, physical, and biochemical examination were conducted on permanent residents aged 35-75 who had lived in the surveillance sites for more than half a year. The chi-square test univariate and multivariate logistic regression were used for statistical analysis. The multiplicative and additive models were used to calculate the interaction between general obesity and dyslipidemia, as well as central obesity and dyslipidemia, respectively.Results:A total of 177 160 subjects were included in the study, with an age of (56.44±8.62) years old. There were 29 535 (16.67%) patients with general obesity, 67 338 (38.01%) patients with central obesity, 64 906 (36.64%) patients with dyslipidemia, and 90 266 (50.95%) patients with hypertension. Multiplication interaction analysis results showed that, after adjusting for gender, age, culture level, marriage status, drinking, smoking, and diabetes, the multiplicative interactions between general obesity and dyslipidemia, and central obesity and dyslipidemia on hypertension were statistically significant (all P<0.001), and the adjusted OR and 95% CI were 2.57 (2.47-2.68) and 2.14 (2.08-2.20), respectively. The results of the additive interaction analysis demonstrated that after adjusting for relevant variables, the relative excess risk of interaction ( RERI), the attributable proportion of interaction ( API), and the synergy index ( SI) of the interaction between generalized obesity and dyslipidemia were 0.48 (95% CI: 0.33-0.63), 0.15 (95% CI: 0.11-0.19), and 1.27 (95% CI: 1.18-1.36), respectively; the RERI, API, and SI of the interaction between central obesity and dyslipidemia were 0.37 (95% CI: 0.28-0.46), 0.13 (95% CI: 0.10-0.16), and 1.25 (95% CI: 1.18-1.32), respectively. Conclusions:There might be multiplicative and additive interactions between general obesity, central obesity, and dyslipidemia on the risk of hypertension. Simultaneous control of BMI, waist circumference, and blood lipid levels may effectively reduce the risk of hypertension.

Objective:To explore the latent profile types of capability-opportunity-motivation-behavior in stroke patients and analyze the influencing factors of different latent profiles.Methods:From January to October 2023, totally 596 stroke patients from the Neurology Department of five ClassⅢ Grade A hospitals in Henan Province were selected by stratified random sampling. The patients were surveyed using a general information questionnaire, the Stroke Prevention Knowledge Questionnaire (SPKQ), the Social Support Rating Scale (SSRS), the WHO's Quality of Life Questionnaire- Brief Version (WHOQOL-BREF), the Short Form Health Belief Model Scale (SF-HBMS), and the Health Promoting Lifestyle ProfileⅡ (HPLPⅡ). Latent profile analysis was used to classify the capability-opportunity-motivation-behavior characteristics of stroke patients, and multiple logistic regression was conducted to explore the influencing factors of different latent profiles.Results:Three latent profiles of capability-opportunity-motivation-behavior in stroke patients were identified, including low capability-opportunity-motivation-behavior with high health beliefs (32.4%, 193/596), moderate capability-opportunity-motivation-behavior with insufficient health beliefs (47.5%, 283/596), and high capability-opportunity-motivation-behavior with lack of social support (20.1%, 120/596). Multiple logistic regression analysis showed that educational level, smoking history, family history, body mass index, and Charlson Comorbidity Index score were influencing factors of different latent profiles ( P<0.05) . Conclusions:Stroke patients exhibit distinct classifications of capability-opportunity-motivation-behavior. Targeted interventions should be conducted based on the characteristics of each category to improve health behavior management outcomes in patients.

Objective:To explore the mediating effect of rumination between self-perceived burden (SPB) and stigma in stroke patients, so as to provide theoretical basis for the development of targeted nursing interventions in clinical practice.Methods:In September 2022, cluster sampling was used to select 1 126 stroke patients admitted to Department of Neurology of five ClassⅢ Grade A hospitals in Henan Province as subjects. General Information Questionnaire, Self-Perceived Burden Scale (SPBS), Stroke Stigma Scale (SSS), and Chinese Version of Event Related Rumination Inventory (C-ERRI) were used to investigate stroke patients. Pearson correlation analysis was used to explore the correlation between SPB, rumination, and stigma. AMOS 28.0 software was used to establish the structural equation model, and Bootstrap method was used to test the mediating effect.Results:A total of 1 126 questionnaires were distributed, and 1 026 valid questionnaires were collected, with a valid response rate of 91.12% (1 026/1 126). SPBS score of 1 026 stroke patients was (28.68±8.32), the SSS score was (40.53±9.48) and the C-ERRI score was (25.43±12.62). Pearson correlation analysis showed that SPB in stroke patients was positively correlated with stigma and rumination ( P<0.01), and rumination was positively correlated with stigma ( P<0.01). Bootstrap mediating effect test showed that rumination partially mediated the relationship between SPB and stigma in stroke patients, accounting for 55.15% of the total effect. Conclusions:SPB of stroke patients both directly affect stigma and indirectly affect stigma through rumination. Clinical nursing workers should promptly evaluate patients' SPB, pay attention to the mediating role of rumination, develop effective psychological intervention programs, implement personalized and targeted nursing measures, relieve patients' stigma, and improve treatment and rehabilitation compliance.

Objective:To understand the research status and hotspots in the field of stroke health management at home and abroad, and to provide insights for stroke health management research in China.Methods:Relevant literature on stroke health management published between 2013 and 2023 was retrieved from the Web of Science Core Collection and China National Knowledge Infrastructure databases. CiteSpace 6.1.R6 was used for the visual analysis of the number of publications, authors, institutions, countries, and keywords.Results:A total of 382 relevant articles were included, with 169 in English and 213 in Chinese. The number of publications on stroke health management showed a fluctuating upward trend. Research hotspots and frontiers in stroke health management mainly focused on telemedicine, big data and "Internet+", primary and secondary prevention, risk prediction models, quality of life, and swallowing disorders. Future research trends may focus on management models for post-stroke swallowing disorders, risk identification, and the role of caregivers in remote rehabilitation interventions.Conclusions:Researchers can refer to the research hotspots and trends shown by the visual analysis, with particular attention to health management models for patients with post-stroke swallowing disorders and issues related to remote intervention rehabilitation.

Objective:To explore the effects of depressive symptoms on sleep quality in adolescents with depressive disorder, and the mediating roles of cognitive fusion and sleep belief.Methods:A sample of 210 adolescents with first episode depressive disorder aged 12-18 years were recruited to complete 17-item Hamilton depression scale (HAMD-17), Pittsburgh sleep quality index (PSQI), cognitive fusion questionnaire (CFQ), and dysfunctional beliefs and attitudes about sleep scale (DBAS-16) from November 2021 to July 2022. SPSS 26.0 software was used to perform descriptive analysis and correlation analysis. The mediating effect was tested by Bootstrap analysis using PROCESS V 3.4 Macro program.Results:The incidence of low sleep quality in adolescents with depressive disorder was 69.0%(145/210). HAMD-17 score was (22.4±7.9), PSQI score was (9.7±3.7), CFQ score was (51.6±7.8), DBAS-16 score was (43.5±8.4).PSQI was positively correlated with the scores of HAMD-17 and CFQ( r=0.613, 0.463, both P<0.001).HAMD-17 was positively correlated with CFQ score ( r=0.488, P<0.001).DBAS-16 was negatively correlated with scores of PSQI, HAMD-17 and CFQ( r=-0.326, -0.284, -0.354, all P<0.001). The direct effect of depression on sleep quality was 0.230(95% CI=0.169-0.293). The indirect effect of depression on sleep quality through two pathways, the separate mediating effect value of cognitive fusion was 0.041 (95% CI=0.011-0.074), and the chain mediating effect value of cognitive fusion and sleep beliefs was 0.008(95% CI=0.001-0.020). Conclusion:Depressive symptoms can directly affect sleep quality of depressive disorder adolescents and indirectly through cognitive fusion and sleep beliefs.