Background: Visual impairment is increasingly prevalent, underscoring the need for effective prevention strategies. This study investigated the association between cardiovascular health and visual impairment using three progressively developed metrics: Life’s Simple 7 (LS7), a cardiovascular health score developed by the American Heart Association based on 7 components, including physical activity, smoking status, body mass index (BMI), diet, blood glucose, blood pressure, and total cholesterol; Life’s Essential 8(LE8), which adds sleep health as an additional component; and Life’s Crucial 9 (LC9), which further incorporates mental health. We aimed to evaluate whether these expanded metrics provide additional explanatory value and to inform future public health strategies. Objective: To investigate the associations among LS7, LE8, and LC9 cardiovascular health scores and visual impairment in National Health and Nutrition Examination Survey 2013 to 2018, assess the added value of sleep and mental health components, and identify key contributing subcomponents using regression and machine learning methods. Methods: We analyzed data from 9891 participants in the 2013 to 2018 National Health and Nutrition Examination Survey. Multivariable logistic regression, restricted cubic spline modeling, and subgroup analyses were conducted to evaluate the association between cardiovascular health and visual impairment. Machine learning methods, including Random Forest and SHapley Additive exPlanations, a model interpretation approach used to assess feature importance, were applied. The Boruta algorithm, a feature selection method, was used to identify key subcomponents of cardiovascular health. Results: Participants with visual impairment were older, had lower poverty-to-income ratios, and exhibited lower cardiovascular health scores (LS7, LE8, and LC9). Higher cardiovascular health scores were negatively associated with visual impairment. Each one standard deviation increase in LS7, LE8, and LC9 was associated with approximately 8%, 2%, and 3% lower odds of visual impairment, respectively. LS7 showed the strongest negative association, whereas LE8 and LC9 emerged as the most important predictors in the machine learning models. Key subcomponents such as blood glucose, blood pressure, and BMI, as well as mental health status, were strongly associated with visual impairment. Conclusion: Higher cardiovascular health scores were negatively associated with visual impairment. LS7 demonstrated the strongest statistical association, whereas LE8 and LC9 were the most influential features in the machine learning models. Blood glucose, blood pressure, BMI, and mental health status were important correlates of visual impairment. From the perspective of the “prevention and treatment of diseases” in traditional Chinese medicine and the holistic concept, the aforementioned research findings confirm that severe visual impairment is indeed an external manifestation of overall health imbalance (such as organ dysfunction and Qi deficiency and blood stasis). The prevention strategy lies in comprehensively adjusting lifestyle, emotional factors, and metabolic indicators (corresponding to the key factors in the study) to restore the body’s internal balance and achieve prevention before disease arises.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

At present,with the development of new diagnostic technologies,the detection rate of aortic dissection has been increasing year by year,but its mortality rate still remains high.Cardiovascular disease is a chronic inflammato-ry disease,and vascular inflammation plays a major role in the progression of aortic dissection.Therefore,this article sys-tematically describes the specific roles and mechanisms of inflammatory cells,inflammatory factors,and inflammasomes in the development of aortic dissection.

Objective Develop a prediction model for full-term neonatal mortality and conduct internal validation.Methods 86 full-term newborns who died in the pediatric department of Xuzhou Central Hospital from January 1,2015 to December 31,2023 were selected.According to the sample estimation method,135 full-term infants who were hospitalized during the same period were randomly selected as the control group.Clinical data such as maternal and child birth history,blood routine within 24hours after birth,coagulation function,arterial carbon dioxide pressure(PaCO2),and serum total bilirubin were collected.Using single factor analysis,stepwise regres-sion,and multiple Logistic regression analysis to screen possible predictive factors and establish a predictive model.The receiver operating characteristic(ROC)curve was used to evaluate the discriminative power of the model,and Hosmer Lemeshow was used to test the calibra-tion of the evaluation model.Bootstrap method was used for internal validation.Results Amniotic fluid contamination(OR=3.818,95％CI:1.009-14.447,P=0.048),coagulation dysfunction(OR=12.981,95％CI 3.732-45.152,P＜0.001),gynecological inflamma-tory diseases(OR=7.203,95％CI:1.216-42.659,P=0.03),mechanical ventilation(OR=54.451,95％CI:12.913-229.619,P＜0.001),PCO2(OR=1.131,95％CI:1.055-1.212,P=0.001),and serum lactate(OR=4.540,95％CI:2.561-8.046,P＜0.001)are independent influencing factors of full-term infant mortality,which can predict the occurrence of neonatal mortality in full-term infants(sensitivity 75.8％,specificity 87.0％,AUC=0.814).The Hosmer Lemeshow test showed that the model had good consis-tency with the actual occurrence probability of full-term neonatal mortality in clinical practice(x2=3.787,P=0.876).After internal validation by Bootstrap,the model had good discrimination(AUC=0.849).Conclusion A predictive model for mortality risk factors can be established based on maternal pregnancy history and clinical data within 24hours after full-term birth,which helps to make clinical decisions in advance.

At present,with the development of new diagnostic technologies,the detection rate of aortic dissection has been increasing year by year,but its mortality rate still remains high.Cardiovascular disease is a chronic inflammato-ry disease,and vascular inflammation plays a major role in the progression of aortic dissection.Therefore,this article sys-tematically describes the specific roles and mechanisms of inflammatory cells,inflammatory factors,and inflammasomes in the development of aortic dissection.

Objective Develop a prediction model for full-term neonatal mortality and conduct internal validation.Methods 86 full-term newborns who died in the pediatric department of Xuzhou Central Hospital from January 1,2015 to December 31,2023 were selected.According to the sample estimation method,135 full-term infants who were hospitalized during the same period were randomly selected as the control group.Clinical data such as maternal and child birth history,blood routine within 24hours after birth,coagulation function,arterial carbon dioxide pressure(PaCO2),and serum total bilirubin were collected.Using single factor analysis,stepwise regres-sion,and multiple Logistic regression analysis to screen possible predictive factors and establish a predictive model.The receiver operating characteristic(ROC)curve was used to evaluate the discriminative power of the model,and Hosmer Lemeshow was used to test the calibra-tion of the evaluation model.Bootstrap method was used for internal validation.Results Amniotic fluid contamination(OR=3.818,95％CI:1.009-14.447,P=0.048),coagulation dysfunction(OR=12.981,95％CI 3.732-45.152,P＜0.001),gynecological inflamma-tory diseases(OR=7.203,95％CI:1.216-42.659,P=0.03),mechanical ventilation(OR=54.451,95％CI:12.913-229.619,P＜0.001),PCO2(OR=1.131,95％CI:1.055-1.212,P=0.001),and serum lactate(OR=4.540,95％CI:2.561-8.046,P＜0.001)are independent influencing factors of full-term infant mortality,which can predict the occurrence of neonatal mortality in full-term infants(sensitivity 75.8％,specificity 87.0％,AUC=0.814).The Hosmer Lemeshow test showed that the model had good consis-tency with the actual occurrence probability of full-term neonatal mortality in clinical practice(x2=3.787,P=0.876).After internal validation by Bootstrap,the model had good discrimination(AUC=0.849).Conclusion A predictive model for mortality risk factors can be established based on maternal pregnancy history and clinical data within 24hours after full-term birth,which helps to make clinical decisions in advance.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective To observe the effect of intelligent flexible ankle stretching training on lower extremity spasm in patients with spinal cord injury. Methods From June,2021 to May,2024,28 patients with spinal cord injury were randomly divided into control group(n=14)and experimental group(n=14).Both groups received conventional rehabilitation treatment.On this ba-sis,the control group received manual extension treatment,and the experimental group received intelligent flexi-ble ankle stretching system training,for eight weeks.The modified Ashworth Scale(MAS),ankle dorsiflexion an-gle,Clinical Spasticity Index(CSI),max root mean square(RMSmax)of surface electromyography of gastrocne-mius medial head and vibration perception threshold(VPT)of great toe were compared. Results After treatment,MAS(χ2=10.378,P=0.035),ankle dorsiflexion angle(Z=-3.306,P<0.001),CSI(t=4.101,P=0.001)and RMSmax of gastrocnemius medial head(Z=-3.296,P<0.001)improved in the experimental group,while MAS(χ2=11.418,P=0.022),ankle dorsiflexion angle(Z=-1.986,P=0.047)and RMSmax of gas-trocnemius medial head(Z=-2.297,P=0.021)were better in the experimental group than in the control group.Although VPT was improved after treatment,no significant difference was found within and beteen groups(P>0.05). Conclusion The intelligent flexible ankle stretching training could improve the lower limb muscle spasticity in patients with spinal cord injury,and may be benefit for foot proprioception.

Objective: To investigate the influencing factors of lumbar disc herniation (LDH) among elderly patients with osteoporosis (OP), so as to provide the basis for early prevention of LDH in OP patients. Methods: The patients aged 60 years and above with OP who were treated in the clinic of Beijing Jishuitan Hospital from December 2022 to December 2023 were recruited as the study subjects. The data of demographic and clinical information were collected through the hospital medical record system. LDH was diagnosed according to the LDH Diagnosis and Treatment Guidelines. Factors affecting LDH among elderly patients with OP were identified using a multivariable logistic regression model. Results: A total of 500 patients with OP were included, with 188 males (37.60%) and 312 females (62.40%). The mean age was (69.05±3.28) years, and the course of OP was (6.37±2.11) years. There were 196 patients living alone, accounting for 39.20%. LDH among elderly patients with OP were detected in 78 cases, accourcing for 15.60%. Multivariable logistic regression analysis showed that the patients who were at ages of 70 years and above (OR=1.465, 95%CI: 1.197-1.793), lived alone (OR=1.777, 95%CI: 1.093-2.889), had a history of fracture (OR=1.799, 95%CI: 1.070-3.023) and scored higher in quality of life (OR=1.405, 95%CI: 1.230-1.605) were at higher risks of LDH, while the patients who had a history of taking calcium supplements (OR=0.562, 95%CI: 0.345-0.915) were at lower risks of LDH. Conclusion Age, living situation, history of fracture, history of taking calcium supplements and quality of life were influencing factors for LDH among elderly patients with OP.

Objective:To evaluate the effect of preemptive analgesia with ibuprofen on postoperative pain following single posterior tooth implantation, aiming to provide a clinical reference for its application.Methods:A multicenter, randomized, double-blind, placebo-controlled parallel-group trial was conducted. A total of 82 participants were included in the trial, meeting the eligibility criteria from April 2022 to April 2024 at the Capital Medical University School of Stomatology (40 cases), Beijing TianTan Hospital, Capital Medical University (22 cases), Beijing Chao-Yang Hospital, Capital Medical University (20 cases). Participants were randomly assigned in a 1∶1 ratio to either the ibuprofen group or the control group, with each group comprising 41 individuals. Participants in the ibuprofen group received 300 mg of sustained-release ibuprofen capsules orally 15 min before surgery, while the control group received a placebo. Both groups received the same postoperative analgesic regimen for 3 days. Pain scores were assessed using the numerical rating scale at 30 min, 4 h, 6 h, 8 h, 24 h, 48 h, and 72 h postoperatively, and the additional use of analgesic medication was recorded from days 4 to 6 postoperatively.Results:A total of 82 participants were initially enrolled in the study, with 7 dropouts (4 from the control group and 3 from the ibuprofen group), resulting in 75 participants (37 in the control group and 38 in the ibuprofen group) completing the trial. There were no reports of adverse events such as nausea or vomiting among the participants. The ibuprofen group exhibited significantly lower pain scores at 4 h, 6 h and 8 h [1.0 (0.0, 2.0), 1.0 (0.0, 2.0), 1.5 (0.0, 3.0) ] postoperatively compared to the control group 4 h, 6 h and 8 h [2.0 (1.0, 3.0), 3.0 (1.5, 4.0), 2.0 (1.0, 4.0)] ( Z=-1.99, P=0.047; Z=-3.01, P=0.003; Z=-2.10, P=0.036). The proportions of patients requiring additional analgesic medication between days 4 and 6 post-surgery were 18.4% (7/38) in the ibuprofen group and 27.0% (10/37) in the control group, with no significant difference (χ 2=0.79, P=0.373). The median additional medication usage postoperatively was [0.0 (0.0, 0.0) pills] in the ibuprofen group and [0.0 (0.0, 1.0) pills] in the control group, with no significant difference ( Z=-0.78, P=0.439). Conclusions:Preemptive analgesia with ibuprofen effectively reduces postoperative pain following tooth implantation, representing a safe and effective perioperative pain management strategy.