Objective To systematically review international research on health communication, and to provide valuable insights and reference for China's health communication research and practice. Methods This study included 693 articles published from January 2023 to April 2024 in two authoritative academic journals in the field of health communication, ^“Health Communication^” and the ^“Journal of Health Communication^”. A systematic review was conducted on the themes, theoretical foundations, research methods, and populations of international health communication research. Results The findings in this study revealed that international health communication research topics were diverse, with hotspots including social media, health information behavior, health misinformation, stigmatization, trust, and risk perception. The results showed that 34% of the articles were based on theoretical foundations, and 93.3% employed research methods, focusing on adolescents, parents, women, and other key populations. Conclusion Domestic health communication research can expand its perspective from ^“information transmission^” to ^“social interaction^”, innovate theories and methods from ^“single paradigm" to ^“multi-integration^” and shift focus from a ^“mass perspective^” to ^“targeted care^” for the health of all populations. Domestic health communication practice can delve into the localization of social media health communication practices, the comprehensive management of health misinformation, and the critical application of new technologies.

OBJECTIVE: To investigate the therapeutic potential of octaarginine (R8)-modified essential oil from Fructus Alpiniae zerumbet (EOFAZ) lipid microspheres (EOFAZ@^R8LM) for cardiovascular therapy. METHODS: EOFAZ@^R8LM was developed by leveraging the volatilization of EOFAZ and integrating it with the oil phase of LM, followed by surface modification with cell-penetrating peptide R8 to target the site of vascular endothelial injury. The therapeutic effects of this formulation in alleviating lipopolysaccharide-induced vascular endothelial inflammation were evaluated by assessing mitochondrial membrane potential (MMP), intracellular reactive oxygen species (ROS) levels, as well as inflammatory factors interleukin-6 (IL-6) and interleukin-1β (IL-1β) levels. RESULTS: EOFAZ@^R8LM effectively delivered EOFAZ to the site of injury and specifically targeted the mitochondria in vascular endothelial cells, thereby ameliorating mitochondrial dysfunction through regulation of MMP and reduction of intracellular ROS levels. Moreover, it attenuated the expression levels of IL-6 and IL-1β, exerting protective effects on the vascular endothelium. CONCLUSION Our findings highlight the significant therapeutic potential of EOFAZ@^R8LM in cardiovascular therapy, providing valuable insights for developing novel dosage forms utilizing EOFAZ for effective treatment against cardiovascular diseases.

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Objective:Chineselization of patient empowerment scale suitable for adult rehabilitation patients with long-term diseases-the Gothenburg empowerment scale(GES).To evaluate the reliability,validity and cultural applicability of the Chinese version GES in limb disabilities.Method:The Chinese version of the GES was produced by translating,back translating,and adjusting the GES according to the process of Beaton,DE.Convenience sampling method was used to select patients with limb disabilities over 18 years old from Shanghai YangZhi rehabilitation hospital as research object.The factor validi-ty,discriminant validity,convergent validity,confirmatory factor analysis(CFA)and exploratory structural equa-tion model(ESEM)were evaluated by Mplus 8.3.Adopt Cronbach's α coefficient and McDonald's ω coeffi-cient were used for reliability test of the scale.The floor and ceiling effects were used to evaluate the respon-siveness of the scale.Result:Total 274 valid samples with limb disabilities were obtained.The Chinese version GES consisted of 15 items,with 3 items connected to each dimension:Knowledge and understanding,personal control,identity,shared decision-making,and Enabling others.The Cronbach's α of total scale as well as the five dimensions ranged from 0.759-0.896.The McDonald's ω ranged from 0.737-0.953 in CFA and ESEM.CFA,ESEM,and second-order confirmatory factor analysis model all fitted well.The ESEM's fit indices(χ2/df=1.113,RMSEA=0.020,TLI=0.994,CFI=0.998,SRMR=0.014)were significantly better than the CFA's fit indices(χ2/df=1.516,RM-SEA=0.043,TLI=0.972,CFI=0.978,SRMR=0.042).The Chinese version GES had good discriminate and con-vergent validity,and responsiveness was good.Conclusion:The various indicators of the Chinese version GES meet the requirements of psychometrics and can be used in the mental study of adult limb disability rehabilitation patients.

Objective:To investigate disease characteristics of patients with psoriatic arthritis (PsA) based on the PsA cohort in West China Hospital, so as to provide a reference for clinicians in its diagnosis, treatment, and evaluation strategy formulation.Methods:A cross-sectional study was carried out, and a descriptive analysis was conducted on clinical data from PsA patients who were treated at the Department of Dermatology, West China Hospital, Sichuan University between April 2, 2020, and January 21, 2025. Demographic characteristics, clinical manifestations, laboratory and imaging findings, and treatment modalities were analyzed.Results:A total of 530 PsA patients were included, of whom 332 (62.6%) were males and 198 (37.4%) were females, with ages of 44.1 ± 12.4 years. Skin lesions preceded joint symptoms in 452 patients (85.3%), with time intervals ( M [ Q1, Q3]) of 8.0 (3.0, 15.0) years. Overweight or obesity was observed in 319 patients (60.2%), and 188 (35.5%) had comorbid fatty liver. Peripheral joint involvement was common (485 cases, 91.5%), with the proximal interphalangeal joints being most frequently affected by tenderness (172 cases, 35.5%) and swelling (119 cases, 24.5%) ; the number of enthesitis cases identified by ultrasonography (116 cases, 23.9%) was significantly higher than that by clinical examination (82 cases, 15.5%) ; axial joint involvement was observed in 258 patients (48.7%), with the sacroiliac joints most commonly affected (201 cases, 77.9%). Regarding treatment, conventional systemic drugs were predominant in the treatment of psoriasis prior to the diagnosis of PsA; after the diagnosis of PsA, the number of patients receiving targeted therapies increased to 334 (63.0%), with interleukin-17 inhibitors being the most common (140 cases, 26.4%), followed by tumor necrosis factor-α inhibitors (106 cases, 20.0%) and Janus kinase inhibitors (39 cases, 7.4%) . Conclusions:PsA predominantly affects males over 40 years old and is characterized by preceding skin lesions, delayed diagnosis, and multiple comorbidities. High-frequency ultrasound has advantages in the early detection of peripheral enthesitis. Further attention is needed for managing comorbidities such as fatty liver and obesity-related metabolic conditions.

Many female patients with inflammatory bowel disease (IBD) are of reproductive age, and addressing fertility-related issues is an important part of comprehensive treatment. Although the fertility of female IBD patients does not differ significantly from that of the general population, many women with IBD opt for childlessness. This voluntary childlessness may stem from concerns about the adverse effects of IBD and its treatment on fertility, pregnancy, and newborns. A lack of in depth understanding of fertility, socioeconomic status, demographic factors, and insufficient professional medical advice may also contribute to patients' decision to choose voluntary childlessness. This article analyzes the factors influencing voluntary childlessness in female IBD patients and summarizes recommendations aimed at reducing trend, to enhance fertility awareness among female IBD patients and providing useful references for developing reasonable fertility plans.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective To understand the current situation of nutrition literacy of primary and secondary school students in Shijingshan District of Beijing, and analyze its influencing factors, and to put forward targeted suggestions for improving the students’ nutrition literacy and promoting their healthy growth. Methods A multi-stage stratified cluster sampling method was used to select 2480 primary and secondary school students and their parents from 5 primary schools, 3 middle schools and 1 high school in Shijingshan District. The multivariate logistic regression model was used to analyze the factors influencing the attainment rate of nutrition literacy. Results The median score of nutrition literacy of 2480 primary and secondary school students from grades 1 to 12 was 77.86 (in hundred-mark system), the quartile range (IQR) was 16.96, and the attainment rate of nutrition literacy was 42.46%. The cognitive level (45.12%) was higher than the skill level (41.20%) among students from grades 3 to 12. In terms of skills, the attainment rate of food preparation was the lowest, at 30.38%. The scores of nutrition literacy of girls were higher than those of boys, and the scores of primary school students were higher than those of secondary school students. Students with different levels of caregiver’s education, family income, and family food environment had different scores of nutrition literacy, and the differences were statistically significant (P<0.05). Multivariate logistic regression analysis showed that the attainment rate of nutrition literacy was closely related to student’s gender and study stage, caregiver’s education level, and family food environment. Conclusion The nutrition literacy of primary and secondary school students in Shijingshan District still needs to be improved, especially in the aspect of skills. Targeted nutrition education should be carried out.

Objective To report the diagnosis of a canine distemper virus outbreak among a colony of cynomolgus monkeys at an experimental monkey farm in 2019. MethodsA total of 46 samples were collected from 21 diseased cynomolgus monkeys (exhibiting symptoms such as facial rash, skin scurf, runny nose, and diarrhea) and from one deceased monkey at an experimental monkey breeding farm in South China in late 2019, including serum, skin rash swabs, and anticoagulated whole blood, liver, lung, and skin tissues were submitted for testing. All submitted samples were tested for canine distemper virus gene fragments using real-time quantitative PCR, while immunohistochemical staining was performed to detect canine distemper virus nucleoprotein in lung tissues. The skin tissue of the deceased monkey was ground and sieved. The filtrate was inoculated into a monolayer MDCK cell line for virus isolation. Then, whole-genome sequencing was performed to identify the isolated virus. The Clustal Omega tool was used to align and analyze the homology of different Asian canine distemper virus isolates. A phylogenetic tree was constructed, followed by genetic evolutionary analysis. ResultsClinical retrospective analysis revealed that the diseased cynomolgus monkeys exhibited symptoms similar to those observed in cynomolgus monkeys infected with measles virus. Necropsy findings showed red lesions in the lungs and significant hemorrhage in the colonic mucosa. Real-time quantitative PCR detected canine distemper virus nucleic acid in the serum, skin rash swabs of the infected monkeys, and various tissue samples of the deceased monkey, all of which tested positive. Calculation based on the standard curve formula indicated the viral load was highest in the skin tissue. Immunohistochemical staining of the deceased monkey's lung tissue demonstrated aggregation of CDV nucleoprotein in alveolar epithelial cells, bronchi, and bronchioles. A CDV strain was isolated from the skin tissue of the deceased monkey. Phylogenetic analysis indicated that this strain shares the closest relationship (98.86%) with the Asian-1 type canine distemper virus strain CDV/dog/HCM/33/140816, previously identified in dogs in Vietnam. ConclusionBased on comprehensive analysis of clinical symptoms, nucleic acid detection, viral protein immunohistochemistry, and whole-genome sequencing results, the diagnosis confirms that the cynomolgus monkeys in this facility are infected with canine distemper virus. It is recommended to include canine distemper virus as a routine surveillance target in captive monkey populations. Additionally, this study provides a foundation for further research on the molecular biological characteristics of canine distemper virus.