Objective To compare the predictive effectiveness of the Glasgow coma scale(GCS),GCS-pupils scale(GCS-P),Glasgow-Pittsburgh coma scale(GPCS),full outline of unresponsiveness scale(FOUR),and coma recovery scale-revised(CRS-R)in forecasting the prognosis of severe stroke patients.Methods A prospective,consecutive cohort of severe stroke patients admitted to the Department of Neurology,First Medical Center of Chinese PLA General Hospital from September 2021 to April 2024 was enrolled.Demographic and clinical data were collected,including age,sex,length of hospital stay,diagnosis(severe ischemic stroke,severe cerebral hemorrhage,aneurysmal subarachnoid hemorrhage),medical history(hypertension,diabetes,coronary artery disease),smoking and drinking habits,vital signs upon admission(temperature,pulse,respiration,blood pressure),neurological examination findings(including speech and brainstem reflexes)at admission,head imaging results(CT,MRI)within 24 h of admission to assess the presence of brain herniation,and whether intubation occurred within 24 h of admission.Patients underwent GCS,GCS-P,GPCS,FOUR,and CRS-R scoring within 8h of admission.Telephone follow-up was conducted at 6 months post-stroke to assess outcomes using the modified Rankin scale(mRS),with mRS scores of 0-2 classified as the good prognosis group and 3-6 as the poor prognosis group.The receiver operating characteristic(ROC)curve was used to assess the prognostic prediction value of the five scales for poor outcomes at 6 months.The area under the ROC curve(AUC)was calculated,and pairwise comparisons of AUC were performed using the Delong test.Results A total of 179 severe stroke patients were enrolled,including 116 males and 63 females.The group consisted of 132 patients with severe ischemic stroke,30 with severe intracerebral hemorrhage,and 17 with aneurysmal subarachnoid hemorrhage.At 6months,126patients had a poor prognosis and 53 had a good prognosis.(1)There were statistically significant differences in age,temperature,pulse,history of coronary artery disease,smoking and drinking habits,presence of speech impairment,abnormal brainstem reflexes,brain herniation,intubation within 24 h of admission,and GCS,GCS-P,GPCS,FOUR,and CRS-R scores between the poor and good prognosis groups(all P＜0.05).(2)ROC analysis revealed that the AUC(95％CI)for predicting poor outcomes at 6 months in severe stroke patients for GCS,GCS-P,GPCS,FOUR,and CRS-R were 0.808(0.742-0.863),0.815(0.750-0.869),0.828(0.765-0.880),0.841(0.780-0.892),and 0.831(0.768-0.883),respectively.Sensitivities were 76.98％,78.57％,82.54％,84.13％,and 82.54％,and specificities were 73.58％,73.58％,67.92％,71.70％,and 73.58％,respectively.The FOUR had the highest AUC,with an optimal cutoff value of 13.(3)Pairwise comparisons of AUC showed a statistically significant difference between the FOUR and GCS(the difference value of AUC is 0.034,95％CI 0.004-0.064,Z=2.194,P=0.028),but no significant differences were observed between other scales(all P＞0.05).Conclusion Compared to GCS,GCS-P,GPCS,and CRS-R,FOUR may provide more valuable prognostic information for severe stroke patients.

Notum, a negative feedback regulator of the Wnt signaling, has emerged as a promising target for treating glucocorticoid-induced osteoporosis (GIOP). This study showcases an efficient strategy for discovering the anti-Notum constituents from herbal medicines (HMs) as novel anti-GIOP agents. Firstly, a rapid-responding near-infrared fluorogenic substrate for Notum was rationally engineered for high-throughput identifying the anti-Notum HMs. The results showed that Bu-Gu-Zhi (BGZ), a known anti-osteoporosis herb, potently inhibited Notum in a competitive-inhibition manner. To uncover the key anti-Notum constituents in BGZ, an efficient strategy was adapted via integrating biochemical, phytochemical, computational, and pharmacological assays. Among all identified BGZ constituents, three furanocoumarins were validated as strong Notum inhibitors, while 5-methoxypsoralen (5-MP) showed the most potent anti-Notum activity and favorable safety profiles. Mechanistically, 5-MP acted as a competitive inhibitor of Notum via creating strong hydrophobic interactions with Trp128 and Phe268 in the catalytic cavity of Notum. Cellular assays showed that 5-MP remarkably promoted osteoblast differentiation and activated Wnt signaling in dexamethasone (DXMS)-challenged MC3T3-E1 osteoblasts. In dexamethasone-induced osteoporotic mice, 5-MP strongly elevated bone mineral density (BMD) and improved cancellous and cortical bone thickness. Collectively, this study constructs a high-efficient platform for discovering key anti-Notum constituents from HMs, while 5-MP emerges as a promising anti-GIOP agent.

Objective:To construct a risk classification model based on logistic regression for medical equipment,so as to improve the application efficiency of medical equipment in the department of cardiovascular medicine. Methods:The logistic regression algorithm was used to construct the risk grade of adverse event of medical equipment of the department of cardiovascular medicine,and data collation and analysis were used to realize monitoring and management control for medical equipment. The 31 sets used medical equipment in the Department of Cardiovascular equipment of Xi'an No.3 Hospital from October 2021 to October 2022 were selected. Equipment management was conducted using conventional methods for risk management from October 2021 to October 2022,while equipment management from November 2022 to November 2023 adopted a risk classification model based on logistic regression was used to conduct risk management. A total of 204 logs of equipment application of the two kinds of management methods were selected,and each method selected 102 logs. The error rate of clinical operation,the occurrence of equipment failure,the timeliness score of risk management of equipment and the rate of hidden danger of safety risk of equipment were compared between the two kinds of management methods. Results:The number of error use of equipment,operational error and man-made mistake were respectively 3,2 and 2 in 102 logs that were managed by using risk classification model,and the incidence rates of them were respectively 2.94％,1.97％ and 1.97％,which were lower than those by using conventional management method,and the differences were statistically significant (x2=11.613,13.058,14.191,P<0.05). The average failure rate,self-maintenance rate of failure and average scrap rate of the medical equipment of the department of cardiovascular medicine of the management with risk classification model were respectively (0.56±0.22)％,(0.79±0.19)％ and (0.90±0.22)％,all of which were lower than those of conventional management method,and the differences were statistically significant (x2=16.971,15.531,15.809,P<0.05). The risk early warning,risk identification,and the average timeliness scores of risk prevention and control of using the management with risk classification model were respectively (90.29±8.69),(89.69±7.69),and (94.58±6.69),all of which were higher than those of using the management with conventional management method,and the differences were statistically significant (t=13.325,11.003,11.676,P<0.05). The number of mechanical injury,associated infection,and abnormal operation of equipment were respectively 1,1 and 2 in 31 medical equipment that were managed by risk classification model,and the incidence rates of them were respectively 3.23％,3.23％ and 6.45％,all of which were lower than those of conventional management methods,and the differences were statistical significances (x2=5.167,7.631,5.413,P<0.05),respectively. Conclusion:The application of a risk classification model based on logistic regression for medical equipment of the department of cardiovascular medicine can improve the utilization rate of equipment,and reduce the potential risk hidden danger of safety,and enhance the operation quality of equipment.

Objective:To construct a risk classification model based on logistic regression for medical equipment,so as to improve the application efficiency of medical equipment in the department of cardiovascular medicine. Methods:The logistic regression algorithm was used to construct the risk grade of adverse event of medical equipment of the department of cardiovascular medicine,and data collation and analysis were used to realize monitoring and management control for medical equipment. The 31 sets used medical equipment in the Department of Cardiovascular equipment of Xi'an No.3 Hospital from October 2021 to October 2022 were selected. Equipment management was conducted using conventional methods for risk management from October 2021 to October 2022,while equipment management from November 2022 to November 2023 adopted a risk classification model based on logistic regression was used to conduct risk management. A total of 204 logs of equipment application of the two kinds of management methods were selected,and each method selected 102 logs. The error rate of clinical operation,the occurrence of equipment failure,the timeliness score of risk management of equipment and the rate of hidden danger of safety risk of equipment were compared between the two kinds of management methods. Results:The number of error use of equipment,operational error and man-made mistake were respectively 3,2 and 2 in 102 logs that were managed by using risk classification model,and the incidence rates of them were respectively 2.94％,1.97％ and 1.97％,which were lower than those by using conventional management method,and the differences were statistically significant (x2=11.613,13.058,14.191,P<0.05). The average failure rate,self-maintenance rate of failure and average scrap rate of the medical equipment of the department of cardiovascular medicine of the management with risk classification model were respectively (0.56±0.22)％,(0.79±0.19)％ and (0.90±0.22)％,all of which were lower than those of conventional management method,and the differences were statistically significant (x2=16.971,15.531,15.809,P<0.05). The risk early warning,risk identification,and the average timeliness scores of risk prevention and control of using the management with risk classification model were respectively (90.29±8.69),(89.69±7.69),and (94.58±6.69),all of which were higher than those of using the management with conventional management method,and the differences were statistically significant (t=13.325,11.003,11.676,P<0.05). The number of mechanical injury,associated infection,and abnormal operation of equipment were respectively 1,1 and 2 in 31 medical equipment that were managed by risk classification model,and the incidence rates of them were respectively 3.23％,3.23％ and 6.45％,all of which were lower than those of conventional management methods,and the differences were statistical significances (x2=5.167,7.631,5.413,P<0.05),respectively. Conclusion:The application of a risk classification model based on logistic regression for medical equipment of the department of cardiovascular medicine can improve the utilization rate of equipment,and reduce the potential risk hidden danger of safety,and enhance the operation quality of equipment.

Objective To compare the predictive effectiveness of the Glasgow coma scale(GCS),GCS-pupils scale(GCS-P),Glasgow-Pittsburgh coma scale(GPCS),full outline of unresponsiveness scale(FOUR),and coma recovery scale-revised(CRS-R)in forecasting the prognosis of severe stroke patients.Methods A prospective,consecutive cohort of severe stroke patients admitted to the Department of Neurology,First Medical Center of Chinese PLA General Hospital from September 2021 to April 2024 was enrolled.Demographic and clinical data were collected,including age,sex,length of hospital stay,diagnosis(severe ischemic stroke,severe cerebral hemorrhage,aneurysmal subarachnoid hemorrhage),medical history(hypertension,diabetes,coronary artery disease),smoking and drinking habits,vital signs upon admission(temperature,pulse,respiration,blood pressure),neurological examination findings(including speech and brainstem reflexes)at admission,head imaging results(CT,MRI)within 24 h of admission to assess the presence of brain herniation,and whether intubation occurred within 24 h of admission.Patients underwent GCS,GCS-P,GPCS,FOUR,and CRS-R scoring within 8h of admission.Telephone follow-up was conducted at 6 months post-stroke to assess outcomes using the modified Rankin scale(mRS),with mRS scores of 0-2 classified as the good prognosis group and 3-6 as the poor prognosis group.The receiver operating characteristic(ROC)curve was used to assess the prognostic prediction value of the five scales for poor outcomes at 6 months.The area under the ROC curve(AUC)was calculated,and pairwise comparisons of AUC were performed using the Delong test.Results A total of 179 severe stroke patients were enrolled,including 116 males and 63 females.The group consisted of 132 patients with severe ischemic stroke,30 with severe intracerebral hemorrhage,and 17 with aneurysmal subarachnoid hemorrhage.At 6months,126patients had a poor prognosis and 53 had a good prognosis.(1)There were statistically significant differences in age,temperature,pulse,history of coronary artery disease,smoking and drinking habits,presence of speech impairment,abnormal brainstem reflexes,brain herniation,intubation within 24 h of admission,and GCS,GCS-P,GPCS,FOUR,and CRS-R scores between the poor and good prognosis groups(all P＜0.05).(2)ROC analysis revealed that the AUC(95％CI)for predicting poor outcomes at 6 months in severe stroke patients for GCS,GCS-P,GPCS,FOUR,and CRS-R were 0.808(0.742-0.863),0.815(0.750-0.869),0.828(0.765-0.880),0.841(0.780-0.892),and 0.831(0.768-0.883),respectively.Sensitivities were 76.98％,78.57％,82.54％,84.13％,and 82.54％,and specificities were 73.58％,73.58％,67.92％,71.70％,and 73.58％,respectively.The FOUR had the highest AUC,with an optimal cutoff value of 13.(3)Pairwise comparisons of AUC showed a statistically significant difference between the FOUR and GCS(the difference value of AUC is 0.034,95％CI 0.004-0.064,Z=2.194,P=0.028),but no significant differences were observed between other scales(all P＞0.05).Conclusion Compared to GCS,GCS-P,GPCS,and CRS-R,FOUR may provide more valuable prognostic information for severe stroke patients.

Objective: To understand the nutritional literacy level and associated factors of primary and secondary school students in Beijing, so as to provide a scientific basis for improving student nutrition. Methods: From October 2022 to May 2023, a multi stage cluster random sampling method was employed to select a total of 14 568 primary, junior and senior high school students from 16 districts (ecluding the Economic Technological Development area) in Beijing. Through a survey questionnaire on nutritional literacy and dietary hehavior of school age children, basic information as well as data on nutritional literacy levels across four dimensions:nutrition related knowledge concepts, food selection, food preparation, and food intake dimensions were obtained. The Wilcoxon rank sum test, Kruskal-Wallis test, Spearman correlation analysis, Chi square test and binary Logistic regression were used for the analysis. Results: The median total score of nutritional literacy among primary and secondary school students in Beijing was 68.8. Approximately 26.0% of primary and secondary school students achieved nutritional literacy standards. The median scores and rates of meeting the standards for nutrition related knowledge concepts, food selection, food preparation and food intake dimensions were 23.0, 42.1%; 17.0, 27.4%; 6.5, 33.5%; 23.0, 33.3%, respectively. There were positive correlations between all pairs of the four dimensions ( r=0.33-0.49, P <0.05). The results of multiple Logistic regression analysis showed that primary school students, junior high school students, female students, suburban students, caregivers with a college education level and a bachelor s degree or above were the positive arrelation factors that promoted the achievement of nutritional literacy standards ( OR =2.21, 1.39, 1.18, 1.27, 1.42, 1.66, P <0.05). Conclusion The literacy level of primary and secondary school students in Beijing needs to be significantly improved. School stage, gender, region and caregiver s education level are associated factors.

Objective:To explore the risk factors of neck work-related musculoskeletal disorders (WMSDs) among automobile manufacturing enterprise workers, and construct the risk prediction model.Methods:In May 2022, a cluster convenience sampling method was used to selet all front-line workers from an automobile manufacturing factory in Xiangyang City as the research objects. And a questionnaire survey was conducted using the modified Musculoskeletal Disorders Questionnaire to analyze the occurrence and exposure to risk factors of neck WMSDs. Logistic regression was used to analyze the influencing factors of workers' neck WMSDs symptoms, and Nomogram column charts was used to construct the risk prediction model. The accuracy of the model was evaluated by the receiver operating characteristic (ROC) curve, the Bootstrap resampling method was used to verify the model, Hosmer-Lemeshow goodness of fit test was used to evaluate the model, and the Calibration curve was drawn.Results:A total of 1783 workers were surveyed, and the incidence of neck WMSDs symptoms was 24.8% (442/1783). Univariate logistic regression showed that age, female, smoking, working in uncomfortable postures, repetitive head movement, feeling constantly stressed at work, and completing conflicting tasks in work could increase the risk of neck WMSDs symptoms in automobile manufacturing enterprise workers ( OR=1.37, 95% CI: 1.16-1.62; OR=2.85, 95% CI: 1.56-5.20; OR=1.50, 95% CI: 1.18-1.91; OR=1.18, 95% CI: 1.02-1.37; OR=1.34, 95% CI: 1.04-1.72; OR=1.62, 95% CI: 1.21-2.17; OR=1.48, 95% CI: 1.13-1.92; P<0.05). While adequate rest time could reduce the risk of neck WMSDs symptoms ( OR=0.56, 95% CI: 0.52-0.86, P<0.05). The risk prediction model of neck WMSDs of workers in automobile manutacturing factory had good prediction efficiency, and the area under the ROC curve was 0.72 (95% CI: 0.70-0.75, P<0.001) . Conclusion:The occurrence of neck WMSDs symptoms of workers in automobile manufacturing factory is relatively high. The risk prediction model constructed in this study can play a certain auxiliary role in predicting neck WMSDs symptoms of workers in automobile manufacturing enterprise workers.

Objective:To explore the risk factors of neck work-related musculoskeletal disorders (WMSDs) among automobile manufacturing enterprise workers, and construct the risk prediction model.Methods:In May 2022, a cluster convenience sampling method was used to selet all front-line workers from an automobile manufacturing factory in Xiangyang City as the research objects. And a questionnaire survey was conducted using the modified Musculoskeletal Disorders Questionnaire to analyze the occurrence and exposure to risk factors of neck WMSDs. Logistic regression was used to analyze the influencing factors of workers' neck WMSDs symptoms, and Nomogram column charts was used to construct the risk prediction model. The accuracy of the model was evaluated by the receiver operating characteristic (ROC) curve, the Bootstrap resampling method was used to verify the model, Hosmer-Lemeshow goodness of fit test was used to evaluate the model, and the Calibration curve was drawn.Results:A total of 1783 workers were surveyed, and the incidence of neck WMSDs symptoms was 24.8% (442/1783). Univariate logistic regression showed that age, female, smoking, working in uncomfortable postures, repetitive head movement, feeling constantly stressed at work, and completing conflicting tasks in work could increase the risk of neck WMSDs symptoms in automobile manufacturing enterprise workers ( OR=1.37, 95% CI: 1.16-1.62; OR=2.85, 95% CI: 1.56-5.20; OR=1.50, 95% CI: 1.18-1.91; OR=1.18, 95% CI: 1.02-1.37; OR=1.34, 95% CI: 1.04-1.72; OR=1.62, 95% CI: 1.21-2.17; OR=1.48, 95% CI: 1.13-1.92; P<0.05). While adequate rest time could reduce the risk of neck WMSDs symptoms ( OR=0.56, 95% CI: 0.52-0.86, P<0.05). The risk prediction model of neck WMSDs of workers in automobile manutacturing factory had good prediction efficiency, and the area under the ROC curve was 0.72 (95% CI: 0.70-0.75, P<0.001) . Conclusion:The occurrence of neck WMSDs symptoms of workers in automobile manufacturing factory is relatively high. The risk prediction model constructed in this study can play a certain auxiliary role in predicting neck WMSDs symptoms of workers in automobile manufacturing enterprise workers.

A 58-year-old female patient underwent ovarian cancer tumor cell reduction surgery for advanced ovarian serous carcinoma in stage ⅣB for more than 3 years and received chemotherapy with paclitaxel and carboplatin regimen for a total of 6 cycles and chemotherapy with doxorubicin liposome and carboplatin regimen for a total of 6 cycles successively. After that, olaparib 300 mg was administered twice daily orally for maintenance treatment. Twenty-five days later, due to the occurrence of grade Ⅱ bone marrow suppression in the patient, the dose of olaparib was reduced to 150 mg in the morning and 300 mg in the evening. After 13 months of olaparib treatment, the patient developed pancytopenia, with the lowest platelet count of 2×10 9/L. Olaparib was stopped immediately.The symptomatic and supportive treatments such as infusion of suspended red blood cells and fresh platelets, elevation of white blood cells,iron replenishment, and platelet elevation were given, but the efficacy was not obvious. Bone marrow flow cytometry detection suggested a high possibility of myelodysplastic syndrome. After discontinuing olaparib for 47 days, the patient died of circulatory failure due to massive abdominal and pelvic bleeding and hemorrhagic shock.

A 58-year-old female patient underwent ovarian cancer tumor cell reduction surgery for advanced ovarian serous carcinoma in stage ⅣB for more than 3 years and received chemotherapy with paclitaxel and carboplatin regimen for a total of 6 cycles and chemotherapy with doxorubicin liposome and carboplatin regimen for a total of 6 cycles successively. After that, olaparib 300 mg was administered twice daily orally for maintenance treatment. Twenty-five days later, due to the occurrence of grade Ⅱ bone marrow suppression in the patient, the dose of olaparib was reduced to 150 mg in the morning and 300 mg in the evening. After 13 months of olaparib treatment, the patient developed pancytopenia, with the lowest platelet count of 2×10 9/L. Olaparib was stopped immediately.The symptomatic and supportive treatments such as infusion of suspended red blood cells and fresh platelets, elevation of white blood cells,iron replenishment, and platelet elevation were given, but the efficacy was not obvious. Bone marrow flow cytometry detection suggested a high possibility of myelodysplastic syndrome. After discontinuing olaparib for 47 days, the patient died of circulatory failure due to massive abdominal and pelvic bleeding and hemorrhagic shock.