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 construct an intelligent segmentation and T-stage diagnostic model for esophageal cancer based on the DAEUnet and ConvNeXt networks using transfer learning.Methods Dicom raw data from 126 patients diagnosed with esophageal cancer between January 2018 and April 2022 were collected,including 100 cases from Department of Thoracic Surgery at the First Affiliated Hospital of Army Medical University and 26 cases from the Department of Thoracic Surgery at Shanxi Cancer Hospital.After data augmentation,a total of 60 275 images were obtained.The DAEUnet esophageal cancer intelligent segmentation network was built,and on this basis,3 classification networks,ConvNeXt,Swin Transformer,and ResNet were constructed for T-stage diagnosis of esophageal cancer.Results The Dice similarity coefficient(DSC)for esophageal cancer intelligent segmentation using the DAEUnet network was 0.82,and the DSC value of the esophagus,aorta,normal esophagus,mediastinal lymph nodes,and heart was 72.4％,87.5％,79.3％,60.5％ and 96.8％,respectively.Among the 3 T-stage diagnosis models for esophageal cancer,the ConvNeXt model performed the best,with a precision value for T1～T4 stages of 0.65,0.727,0.889 and 0.92,respectively,and an AUC value of 0.892,which were superior to the ResNet and Swin Transformer networks.Conclusion The proposed DAEUnet and ConvNeXt-based intelligent segmentation and T-stage diagnosis model for esophageal cancer improves T-stage accuracy and treatment efficiency.

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 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 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: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 analyze the distribution characteristics of 13 common respiratory pathogens in children in Zibo area from 2020 to 2022.Methods A total of 3091 hospitalized children with respiratory infections admitted to Zibo Maternal and Child Health Hospital from January 2020 to December 2022 were selected as the subjects.Throat swabs or bronchoalveolar lavage fluid samples were collected from the patients,and 13 common respiratory pathogens were tested to analyze the distribution differences among different genders,ages,and seasons.Results Among 3091 pediatric patients,1794 were found to be infected with pathogens.The top three pathogens were Mycoplasma pneumoniae,rhinovirus,and respiratory syncytial virus(RSV).The single infection rate was 47.75％,while the mixed infection rate was 10.28％,with the most common scenario being a mixed infection of two pathogens.There were statistically significant differences in the pathogen profiles across different age groups(P＜0.001):infants had the highest detection rate of RSV,young children were primarily infected with rhinovirus,preschool and school-age children were predominantly infected with Mycoplasma pneumoniae.Seasonal distribution showed that the highest positive rate was in autumn,while the lowest was in spring(P＜0.05).In spring,the main pathogens were rhinovirus and Mycoplasma pneumoniae;in summer,they were rhinovirus and parainfluenza virus;in autumn,they were Mycoplasma pneumoniae and RSV;and in winter,the detection rates of Mycoplasma pneumoniae and influenza B virus were higher.Conclusion From 2020 to 2022,Mycoplasma pneumoniae,rhinovirus and RSV were the main pathogens of children's respiratory tract infection in Zibo area,and there were significant differences in the distribution of pathogens among different ages and seasons.

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.

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.

Objective To analyze the distribution characteristics of 13 common respiratory pathogens in children in Zibo area from 2020 to 2022.Methods A total of 3091 hospitalized children with respiratory infections admitted to Zibo Maternal and Child Health Hospital from January 2020 to December 2022 were selected as the subjects.Throat swabs or bronchoalveolar lavage fluid samples were collected from the patients,and 13 common respiratory pathogens were tested to analyze the distribution differences among different genders,ages,and seasons.Results Among 3091 pediatric patients,1794 were found to be infected with pathogens.The top three pathogens were Mycoplasma pneumoniae,rhinovirus,and respiratory syncytial virus(RSV).The single infection rate was 47.75％,while the mixed infection rate was 10.28％,with the most common scenario being a mixed infection of two pathogens.There were statistically significant differences in the pathogen profiles across different age groups(P＜0.001):infants had the highest detection rate of RSV,young children were primarily infected with rhinovirus,preschool and school-age children were predominantly infected with Mycoplasma pneumoniae.Seasonal distribution showed that the highest positive rate was in autumn,while the lowest was in spring(P＜0.05).In spring,the main pathogens were rhinovirus and Mycoplasma pneumoniae;in summer,they were rhinovirus and parainfluenza virus;in autumn,they were Mycoplasma pneumoniae and RSV;and in winter,the detection rates of Mycoplasma pneumoniae and influenza B virus were higher.Conclusion From 2020 to 2022,Mycoplasma pneumoniae,rhinovirus and RSV were the main pathogens of children's respiratory tract infection in Zibo area,and there were significant differences in the distribution of pathogens among different ages and seasons.