Objective To develop a multi-parameter gait kinematics analysis and measurement system using artificial intelligence key point detection technology(AI system),and verify the effectiveness of the measurement.Methods A total of ten inpatients with abnormal gait were selected in Peking University Third Hospital from April to Oc-tober,2022,while ten normal subjects were recruited.Their gait data were measured using GaitWatch and AI sys-tem simultaneously.Results Compared with those from GaitWatch,the mean absolute error(MAE)of stride frequency,gait period,stride length and stride speed were little from AI system,with high accuracy(>95%)and good consistency(ICC>0.75);however,MAE of hip,knee and ankle angles was larger,and correlation was satisfactory(r>0.60).Conclusion The novel gait analysis system is accurate and consistent in measuring spatiotemporal gait parameters,but not well in measuring joint angles,which need to be modified.

Objective:To investigate the relationship between lipid levels and cognitive impairment in the elderly Chinese population using prospective cohort data.Methods:Based on the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) cohort, this study included 24 380 individuals aged ≥60 years who participated in the cognitive function follow-up survey from 2018 to 2019. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), with cognitive impairment defined according to different educational levels: MMSE ≤17 for illiterate individuals, MMSE ≤20 for those with primary education and MMSE ≤24 for those with secondary education or above. Multivariable linear regression and logistic regression models were employed to examine the associations between six baseline lipid indicators and cognitive scores, as well as cognitive impairment. Additionally, restricted cubic splines were used to explore the exposure-dose relationship between lipid levels and cognitive function.Results:The study population had a median follow-up time of 11.6 years, with a baseline age of (59.7±6.8) years. Among the participants, 9 510 (39.0%) were males, and the mean MMSE score was 24.7±6.8. A total of 3 887 individuals (15.9%) were identified as cognitively impaired. The results of multivariable linear regression and logistic regression indicated that total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels were not only significantly positively associated with cognitive scores but also significantly associated with a lower risk of cognitive impairment. Each 1 mmol/L increase in these lipid levels corresponded to β values (95% CI) of 0.267 (0.173-0.361), 0.385(0.271-0.499) and 0.331(0.231-0.431), respectively. Each 1 mmol/L increase in these lipid levels corresponded to odds ratio ( OR) (95% CI) values of 0.915 (0.876-0.956), 0.875 (0.830-0.923) and 0.886 (0.848-0.927), respectively. The dose-response curve demonstrated that the negative association was primarily observed within the guideline-recommended optimal lipid level range. Specifically, when LDL-C was less than 3.4 mmol/L and non-HDL-C was less than 4.1 mmol/L, the corresponding OR (95% CI) values were 0.859 (0.796-0.926) and 0.876 (0.818-0.939). Conclusion:Lipid levels exhibit a certain linear negative association with cognitive impairment in elderly Chinese adults, with LDL-C and non-HDL-C demonstrating a stronger effect, particularly within the guideline-recommended optimal range.

Objective:To construct a biosafety evaluation index system for major emergency public health events in medical institutions.Methods:Based on previous laboratory biosafety evaluation work, relevant regulations and standards on biosafety in China were collected through literature research and expert consultations. Candidate indicators for constructing the biosafety evaluation system for major emergency public health events in medical institutions were selected, and a framework was established. Two rounds of expert questionnaires were conducted to determine the content of the index system based on experts′ evaluation, and each indicator′s relevance and importance were scored. Finally, two rounds of Delphi consultations were carried out, and the Analytic Hierarchy Process (AHP) was applied to calculate the weights of indicators.Results:The response rates for the total four rounds of questionnaire surveys were all 100%. The first two rounds focused on determining the framework, while the latter two focused on determining the weights for each indicator. The authority coefficients of the expert consultations for the two rounds of weights were 0.65 and 0.70, respectively, indicating the reliability of the research results. In the final round of survey, the Kendall′s coefficients of concordance at each level were all greater than 0.1. Through statistical testing, the P-values were all less than 0.05, indicating good coordination of expert opinions. Ultimately, we established an operational biosafety evaluation system for major emergency public health events in medical institutions, consisting of 4 primary indicators, 26 secondary indicators, and 119 tertiary indicators, with additional deduction items, bonus items, unacceptable items, and monitoring indicators.Conclusions:Based on scientific theory, a biosafety evaluation system for major emergency public health events in medical institutions was constructed, achieving the integration of routine and emergency measures. This system can be used for self-assessment of laboratory biosafety during emergency public health events, addressing the lack of unified standards in biosafety evaluation. Through regular self-assessment, it can enhance the level of biosafety management in medical institution laboratories, to realize the value of application and dissemination.

Objective:To construct a biosafety evaluation index system for major emergency public health events in medical institutions.Methods:Based on previous laboratory biosafety evaluation work, relevant regulations and standards on biosafety in China were collected through literature research and expert consultations. Candidate indicators for constructing the biosafety evaluation system for major emergency public health events in medical institutions were selected, and a framework was established. Two rounds of expert questionnaires were conducted to determine the content of the index system based on experts′ evaluation, and each indicator′s relevance and importance were scored. Finally, two rounds of Delphi consultations were carried out, and the Analytic Hierarchy Process (AHP) was applied to calculate the weights of indicators.Results:The response rates for the total four rounds of questionnaire surveys were all 100%. The first two rounds focused on determining the framework, while the latter two focused on determining the weights for each indicator. The authority coefficients of the expert consultations for the two rounds of weights were 0.65 and 0.70, respectively, indicating the reliability of the research results. In the final round of survey, the Kendall′s coefficients of concordance at each level were all greater than 0.1. Through statistical testing, the P-values were all less than 0.05, indicating good coordination of expert opinions. Ultimately, we established an operational biosafety evaluation system for major emergency public health events in medical institutions, consisting of 4 primary indicators, 26 secondary indicators, and 119 tertiary indicators, with additional deduction items, bonus items, unacceptable items, and monitoring indicators.Conclusions:Based on scientific theory, a biosafety evaluation system for major emergency public health events in medical institutions was constructed, achieving the integration of routine and emergency measures. This system can be used for self-assessment of laboratory biosafety during emergency public health events, addressing the lack of unified standards in biosafety evaluation. Through regular self-assessment, it can enhance the level of biosafety management in medical institution laboratories, to realize the value of application and dissemination.

Objective To develop a multi-parameter gait kinematics analysis and measurement system using artificial intelligence key point detection technology(AI system),and verify the effectiveness of the measurement.Methods A total of ten inpatients with abnormal gait were selected in Peking University Third Hospital from April to Oc-tober,2022,while ten normal subjects were recruited.Their gait data were measured using GaitWatch and AI sys-tem simultaneously.Results Compared with those from GaitWatch,the mean absolute error(MAE)of stride frequency,gait period,stride length and stride speed were little from AI system,with high accuracy(>95%)and good consistency(ICC>0.75);however,MAE of hip,knee and ankle angles was larger,and correlation was satisfactory(r>0.60).Conclusion The novel gait analysis system is accurate and consistent in measuring spatiotemporal gait parameters,but not well in measuring joint angles,which need to be modified.

Objective:To investigate the relationship between lipid levels and cognitive impairment in the elderly Chinese population using prospective cohort data.Methods:Based on the China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) cohort, this study included 24 380 individuals aged ≥60 years who participated in the cognitive function follow-up survey from 2018 to 2019. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), with cognitive impairment defined according to different educational levels: MMSE ≤17 for illiterate individuals, MMSE ≤20 for those with primary education and MMSE ≤24 for those with secondary education or above. Multivariable linear regression and logistic regression models were employed to examine the associations between six baseline lipid indicators and cognitive scores, as well as cognitive impairment. Additionally, restricted cubic splines were used to explore the exposure-dose relationship between lipid levels and cognitive function.Results:The study population had a median follow-up time of 11.6 years, with a baseline age of (59.7±6.8) years. Among the participants, 9 510 (39.0%) were males, and the mean MMSE score was 24.7±6.8. A total of 3 887 individuals (15.9%) were identified as cognitively impaired. The results of multivariable linear regression and logistic regression indicated that total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) levels were not only significantly positively associated with cognitive scores but also significantly associated with a lower risk of cognitive impairment. Each 1 mmol/L increase in these lipid levels corresponded to β values (95% CI) of 0.267 (0.173-0.361), 0.385(0.271-0.499) and 0.331(0.231-0.431), respectively. Each 1 mmol/L increase in these lipid levels corresponded to odds ratio ( OR) (95% CI) values of 0.915 (0.876-0.956), 0.875 (0.830-0.923) and 0.886 (0.848-0.927), respectively. The dose-response curve demonstrated that the negative association was primarily observed within the guideline-recommended optimal lipid level range. Specifically, when LDL-C was less than 3.4 mmol/L and non-HDL-C was less than 4.1 mmol/L, the corresponding OR (95% CI) values were 0.859 (0.796-0.926) and 0.876 (0.818-0.939). Conclusion:Lipid levels exhibit a certain linear negative association with cognitive impairment in elderly Chinese adults, with LDL-C and non-HDL-C demonstrating a stronger effect, particularly within the guideline-recommended optimal range.

Objective:To investigate the effect of myogenic differentiation protein 1(Myod1)on the proliferation inhibition and apoptosis of the SH-SY5Y cells induced by oxygen-glucose deprivation(OGD),and to elucidate its mechanism.Methods:Real-time quantitative fluorescence PCR(RT-qPCR)method was used to detect the mRNA levels of Myod1 and long non-coding RNA(lncRNA)small nucleolar RNA host gene 15(SNHG15)in peripheral blood of the subjects in normal group and the patients in ischemic cerebral infarction group as well as the normal cultured SH-SY5Y cells(control group)and the cells in OGD model(OGD group).After transfecting SH-SY5Y cells with si-Myod1,pcDNA3.0-Myod1,si-SNHG15,pcDNA3.0-SNHG15、si-NC,Vector,miR-NC,and miR-24-3p mimics,the cells were treated with OGD,and then the SH-SY5Y cells were divided into control group,OGD group,OGD+Vector group,OGD+Myod1 group,OGD+si-NC group,OGD+si-Myod1 group,OGD+si-SNHG15 group,OGD+si-SNHG15+Vector group,OGD+si-SNHG15+Myod1 group,OGD+miR-NC group,OGD+miR-mimics group,OGD+miR-mimics+Vector group,and OGD+miR-mimics+SNHG15 group.CCK-8 method was used to detect the cell activities in various groups;5-ethynyl-2'-deoxyuridine(EdU)staining was used to detect the rates of EDU positive cells in various groups;the rates of TdT-mediated dUTP nick end labeling(TUNEL)positive cells in various groups were detected by TUNEL staining;Western blotting method was used to detect the expression levels of cleaved caspase-3,cleaved caspase-9,B-cell lymphoma 2(Bcl-2)and Bcl-2 associated X protein(Bax)proteins in the cells in various groups;the association between Myod1 and SNHG15 was evaluated by chromatin immunoprecipitate(CHIP);dual luciferase reporter gene experiment was used to evaluate the targeting relationships between Myod1 and SNHG15 as well as SNHG15 and miR-24-3p.Results:Compared with normal control group,the expression levels of Myod1 and SNHG15 mRNA in peripheral blood of the patients in ischemic cerebral infarction group were significantly increased(P＜0.05).Compared with control group,the expression levels of Myod1 and SNHG15 mRNA in the SH-SY5Y cells in OGD group were significantly increased(P＜0.05).Compared with OGD group,the cell activities and rates of EdU positive cells in OGD+Myod1 group at 48 and 72 h were decreased(P＜0.01),and the rates of TUNEL positive cells were increased(P＜0.05);the cell activities and rates of EdU positive cells in OGD+si-Myod1 group were increased(P＜0.05),while the rates of TUNEL positive cells were decreased(P＜0.01).Myod1 binded to the promoter sequence of SNHG15.SNHG15 could absorb miR-24-3p,and there were target relatronships between Myod1 and SNHG15 as well as SNHG15 and miR-24-3p.After SNHG15 knockdown,compared with OGD group,the cell activities and rates of EdU positive cells in OGD+si-SNHG15 group at 48 and 72 h were increased(P＜0.01),and the rates of TUNEL positive cells were decreased(P＜0.01),the expression levels of Bax,cleaved caspase-3 and cleaved caspase-9 proteins were decreased(P＜0.01),and the expression levels of Bcl-2 protein were increased(P＜0.01).Compared with OGD+si-SNHG15 group,the cell activities and rates of EdU positive cells in OGD+si-SNHG15+Myod1 group at 48 and 72 h were decreased(P＜0.05),the rates of TUNEL positive cells were(P＜0.05),the expression levels of Bax,cleaved caspase-3,and cleaved caspase-9 proteins were increased(P＜0.05),and the expression levels of Bcl-2 were decreased(P＜0.05).After over-expression of miR-24-3p and SNHG15,compared with OGD group,the cell activities and rates of EdU positive cells in OGD+miR-mimics group at 48 and 72 h were increased(P＜0.01),the rates of TUNEL positive cells were significantly decreased(P＜0.01),the protein expression levels of Bax,cleaved caspase-3 and cleaved caspase-9 were decreased(P＜0.05),and the expression levels of Bcl-2 were increased(P＜0.01).Compared with OGD+miR-mimics group,the cell activities and rates of EdU positive cells in OGD+miR-mimics+SNHG15 group at 48 and 72 h were decreased(P＜0.05),and the rates of TUNEL positive cells were increased(P＜0.05),the expression levels of Bax,cleaved caspase-3 and cleaved caspase-9 proteins were increased(P＜0.05),and the expression levels of Bcl-2 protein were decreased(P＜0.05).Conclusion:Myod1 can promote the proliferation inhibition and apoptosis of OGD-induced SH-SY5Y cells by binding to the SNHG15 promoter region and then absorbing miRNA-24.

Objective::To determine whether deep learning algorithms are suitable for predicting preterm birth.Methods::A retrospective study was conducted at Peking University Third Hospital from January 2018 to June 2023. Birth data were divided into two parts based on the date of delivery: the first part was used for model training and validation, while real world viability was evaluated using the second part. Four machine learning algorithms (logistic regression, random forest, support vector machine, and transformer) were employed to predict preterm birth. Receiver operating characteristic curves were plotted, and the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated.Results::This research included data on 30,965 births, where 24,770 comprised the first part, and included 3164 (12.77%) in the preterm birth group, with 6195 in the second part, including 795 (12.83%) in the preterm birth group. Significant differences in various factors were observed between the preterm and full-term birth groups. The transformer model (AUC = 79.20%, sensitivity = 73.67%, specificity = 72.48%, PPV = 28.21%, NPV = 94.95%, and accuracy = 72.61 % in the test dataset) demonstrated superior performance relative to logistic regression (AUC = 77.96% in the test dataset), support vector machine (AUC = 71.70% in the test dataset), and random forest (AUC = 75.09% in the test dataset) approaches.Conclusion::This study highlights the promise of deep learning algorithms, specifically the transformer algorithm, for predicting preterm birth.

Objective::To determine whether deep learning algorithms are suitable for predicting preterm birth.Methods::A retrospective study was conducted at Peking University Third Hospital from January 2018 to June 2023. Birth data were divided into two parts based on the date of delivery: the first part was used for model training and validation, while real world viability was evaluated using the second part. Four machine learning algorithms (logistic regression, random forest, support vector machine, and transformer) were employed to predict preterm birth. Receiver operating characteristic curves were plotted, and the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated.Results::This research included data on 30,965 births, where 24,770 comprised the first part, and included 3164 (12.77%) in the preterm birth group, with 6195 in the second part, including 795 (12.83%) in the preterm birth group. Significant differences in various factors were observed between the preterm and full-term birth groups. The transformer model (AUC = 79.20%, sensitivity = 73.67%, specificity = 72.48%, PPV = 28.21%, NPV = 94.95%, and accuracy = 72.61 % in the test dataset) demonstrated superior performance relative to logistic regression (AUC = 77.96% in the test dataset), support vector machine (AUC = 71.70% in the test dataset), and random forest (AUC = 75.09% in the test dataset) approaches.Conclusion::This study highlights the promise of deep learning algorithms, specifically the transformer algorithm, for predicting preterm birth.

Objective:This stndy aims to analyze the influence of ChatGPT on medical research and give suggestions on GhatGPT.Methods:This paper expounded the background of ChatGPT, analyze the connection between ChatGPT and medical research management, application and threats of ChatGPT , and gave suggestions to avoid GhatGPT′s damage.Results:Technology will promote the evolution of medical research management. It is recommended to strengthen the supervision of the ChatGPT and research integrity education.Conclusions:It is urgent to establish a scientific management mechanism to take usage the advantages of GhatGPT.