Objective:To observe the effects of hyperbaric oxygen combined with huperzine A treatment on cognitive function and neuroinflammation in patients with vascular cognitive impairment(VCI)after cerebral infarction.Methods:This study was a retrospective study.34 patients with VCI after cerebral infarction who were treated with hyperbaric oxygen combined with huperzine A in our hospital from January 2023 to October 2024 were selected as the observation group.Additionally using 1∶1 matched case-control study method,34 patients who were treated with hyperbaric oxygen alone during the same period were selected as the control group 1,and 34 patients who were treated with huperzine A alone were selected as the control group 2.Compared the clinical efficacy,cognitive function[mini mental state examination(MMSE)score,montreal cognitive assessment(MoCA)score],activities of daily living(Barthel index),neurological function[national institutes of health stroke scale(NIHSS),neuron specific enolase(NSE),brain-derived neurotrophic factor(BDNF)],inflammatory mediators[high-sensitivity C-reactive protein(hs-CRP),tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1 β)]and incidence of adverse reactions among three groups.Results:The total clinical effective rate of the observation group was higher than that of control group 1 and control group 2(P＜0.05).After the treatment,the MMSE score,MoCA score and Barthel index of the observation group were higher than those of control group 1 and control group 2(P＜0.05).After the treatment,the NIHSS and NSE in the observation group were lower than those in control group 1 and control group 2,and the BDNF was higher than that in control group 1 and control group 2(P＜0.05).After the treatment,the levels of hs-CRP,IL-1β and TNF-α in the observation group were lower than those in control group 1 and control group 2(P＜0.05).There was no difference in the incidence of adverse reactions among the three groups(P＞0.05).Conclusion:Hyperbaric oxygen combined with huperzine A in the treatment of VCI after cerebral infarction can increase the total clinical effective rate,improve patients' cognitive function and reduce levels of neuroinflammation.

Objective To investigate the failure distribution characteristics of semiconductor coolers for medical devices and to analyze the failure mechanisms at different time periods.Methods Firstly,a sample of a pilot semiconductor cooler used in some medical device was used as the object,and all the failure data within the last 4 years were collected,screened and organized.Secondly,the failure data were fitted based on the Weibull distribution,and the failure distribution characteristics were analyzed by shape parameters.Finally,based on the failure distribution characteristics,mechanism analysis was carried out with the typical failure samples selected from three time periods of early failure,random failure and wear-out failure,and the causes of failure were found out.Results The failures of semiconductor coolers occurring in the three time periods presented different types of mechanisms,and the failure distribution conformed to the Weibull distribution.The causes included the mechanical stress damage for early failures,the process defect for random failures and water vapour penetration due to sealant aging after long-term operation of the semiconductor cooler.Conclusion The failure patterns of semiconductor coolers for medical devices are revealed effectively,providing a scientific basis for the design,improvement and reliability assessment of semiconductor coolers used in medical equipment.[Chinese Medical Equipment Journal,2025,46(5):34-38]

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective To investigate the failure distribution characteristics of semiconductor coolers for medical devices and to analyze the failure mechanisms at different time periods.Methods Firstly,a sample of a pilot semiconductor cooler used in some medical device was used as the object,and all the failure data within the last 4 years were collected,screened and organized.Secondly,the failure data were fitted based on the Weibull distribution,and the failure distribution characteristics were analyzed by shape parameters.Finally,based on the failure distribution characteristics,mechanism analysis was carried out with the typical failure samples selected from three time periods of early failure,random failure and wear-out failure,and the causes of failure were found out.Results The failures of semiconductor coolers occurring in the three time periods presented different types of mechanisms,and the failure distribution conformed to the Weibull distribution.The causes included the mechanical stress damage for early failures,the process defect for random failures and water vapour penetration due to sealant aging after long-term operation of the semiconductor cooler.Conclusion The failure patterns of semiconductor coolers for medical devices are revealed effectively,providing a scientific basis for the design,improvement and reliability assessment of semiconductor coolers used in medical equipment.[Chinese Medical Equipment Journal,2025,46(5):34-38]

Objective To propose a dynamic early warning model based on machine learning methods and validate its predi-ctive efficacy so as to achieve precise assessment and early warning of mortality risk in patients with traumatic hypothermia.Methods Firstly,a total of 480 patients who met inclusion criteria were retrospectively selected from the eICU database and randomly divided into training and test sets at an 8∶2 ratio.Secondly,physiological parameters were extracted from these patients,and five machine learning algorithms including XGBoost,AdaBoost,LightGBM,logistic regression(LR)and random forest(RF)were employed respectively to develop dynamic mortality risk warning models for traumatic hypothermia patients,utilizing a 1-hour observation window.Thirdly,receiver operating characteristic curves(ROC)were plotted using the test set data and the effects of different warning windows on the model performance were analyzed by calculating the AUC.Finally,the interpretability of the models was analyzed using the SHapley Additive exPlanations(SHAP)algorithm to elucidate the contribution of each feature to predictive performance.Results The optimal warning window for the dynamic warning model constructed using the eICU database was 12 hours,and in case of 12-hour warning window the logistic regression model achieved the highest AUC of 0.935 and showed optimal predictive performance.The results of the interpretability analysis by the SHAP algorithm showed that body temperature was the feature that had the greatest impact on the model results,and its reduction was positively correlated with the increased risk of death.Conclusion The machine learning-based dynamic warning model for mortality risk in traumatic hypothermia patients enables real-time dynamic risk assessment,providing robust support for clinicians to identify the patient's condition changes at an early stage and references for the adjustment of clinical treatment programs.[Chinese Medical Equipment Journal,2025,46(3):9-14]

Objective:To observe the effects of hyperbaric oxygen combined with huperzine A treatment on cognitive function and neuroinflammation in patients with vascular cognitive impairment(VCI)after cerebral infarction.Methods:This study was a retrospective study.34 patients with VCI after cerebral infarction who were treated with hyperbaric oxygen combined with huperzine A in our hospital from January 2023 to October 2024 were selected as the observation group.Additionally using 1∶1 matched case-control study method,34 patients who were treated with hyperbaric oxygen alone during the same period were selected as the control group 1,and 34 patients who were treated with huperzine A alone were selected as the control group 2.Compared the clinical efficacy,cognitive function[mini mental state examination(MMSE)score,montreal cognitive assessment(MoCA)score],activities of daily living(Barthel index),neurological function[national institutes of health stroke scale(NIHSS),neuron specific enolase(NSE),brain-derived neurotrophic factor(BDNF)],inflammatory mediators[high-sensitivity C-reactive protein(hs-CRP),tumor necrosis factor-α(TNF-α),interleukin-1β(IL-1 β)]and incidence of adverse reactions among three groups.Results:The total clinical effective rate of the observation group was higher than that of control group 1 and control group 2(P＜0.05).After the treatment,the MMSE score,MoCA score and Barthel index of the observation group were higher than those of control group 1 and control group 2(P＜0.05).After the treatment,the NIHSS and NSE in the observation group were lower than those in control group 1 and control group 2,and the BDNF was higher than that in control group 1 and control group 2(P＜0.05).After the treatment,the levels of hs-CRP,IL-1β and TNF-α in the observation group were lower than those in control group 1 and control group 2(P＜0.05).There was no difference in the incidence of adverse reactions among the three groups(P＞0.05).Conclusion:Hyperbaric oxygen combined with huperzine A in the treatment of VCI after cerebral infarction can increase the total clinical effective rate,improve patients' cognitive function and reduce levels of neuroinflammation.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

PURPOSE: To analyze risk factors for severe trauma and establish a nomogram for early risk prediction, to improve the early identification of severe trauma. METHODS: This study was conducted on the patients treated in 81 trauma treatment institutions in Gansu province from 2020 to 2022. Patients were grouped by year, with 5364 patients from 2020 to 2021 as the training set and 1094 newly admitted patients in 2020 as the external validation set. Based on the injury severity score (ISS), patients in the training set were classified into 2 subgroups of the severe trauma group (n = 478, ISS scores ≥25) and the non-severe trauma group (n = 4886, ISS scores <25). Univariate and binary logistic regression analyses were employed to identify independent risk factors for severe trauma. Subsequently, a predictive model was developed using the R software environment. Furthermore, the model was subjected to internal and external validation via the Hosmer-Lemeshow test and receiver operating characteristic curve analysis. RESULTS: In total, 6458 trauma patients were included in this study. Initially, this study identified several independent risk factors for severe trauma, including multiple traumatic injuries (polytrauma), external hemorrhage, elevated shock index, elevated respiratory rate, decreased peripheral oxygen saturation, and decreased Glasgow coma scale score (all p < 0.05). For internal validation, the area under the receiver operating characteristic curve was 0.914, with the sensitivity and specificity of 88.4% and 87.6%, respectively; while for external validation, the area under the receiver operating characteristic curve was 0.936, with the sensitivity and specificity of 84.6% and 93.7%, respectively. In addition, a good model fitting was observed through the Hosmer-Lemeshow test and calibration curve analysis (p > 0.05). CONCLUSION This study establishes a nomogram for early risk prediction of severe trauma, which is suitable for primary healthcare institutions in underdeveloped western China. It facilitates early triage and quantitative assessment of trauma severity by clinicians prior to clinical interventions.
Humans ; Nomograms ; Male ; Female ; Wounds and Injuries/diagnosis* ; Risk Factors ; Middle Aged ; Adult ; Injury Severity Score ; Risk Assessment ; ROC Curve ; Aged ; Logistic Models ; China ; Glasgow Coma Scale

Single-cell metabolite analysis at the small molecule level reveals intercellular heterogeneity and molecular diversity,especially living cell metabolite analysis which can provide more accurate biochemical information.In this study,a comprehensive single-cell metabolomics mass spectrometry analysis platform was constructed based on continuous ultrasonic sample introduction,aiming to improve the utilization rate of single cells and the efficiency of mass spectrometry detection.This platform utilized mechanical motion generated by a miniaturized ultrasound module,which minimally affected cell integrity and viability,enabling cell suspension and dispersion for up to 60 min,with cell viability exceeding 70%.By comparing cell suspension densities and the cell number of mass spectrometry detections between static and ultrasound groups,the results showed that the ultrasound treatment significantly reduced cell sedimentation rate and increased single-cell mass spectrometry detection efficiency.Applying this platform to single-cell analysis of cell line of mouse cerebellar astrocytes(C8D1A)and mouse glioma(GL261)cells achieved clustering and differential analysis of different cell types,demonstrating the method's potential in analyzing cellular heterogeneity and identifying cells.This approach promised to provide new insights and solutions for single-cell analysis.