Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

OBJECTIVE:Nowadays,machine learning algorithms are gradually being applied to predict stroke and cardiovascular disease.Compared with traditional regression models,machine learning can learn from data to achieve high prediction accuracy by exploring the flexible relationship between a large number of predictive features and outcome variables,providing a new method for the formulation of individualized treatment and rehabilitation programs.This study aims to systematically evaluate stroke functional recovery and prognosis prediction models based on machine learning,comprehensively assessing their predictive performance and clinical application potential to provide references for the development,application,and promotion of related predictive models.METHODS:This review was conducted following the PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses)guidelines.Relevant literature on stroke prognosis prediction using machine learning methods was selected by searching PubMed,EMbase,Web of Science Core Collection,CNKI,WanFang,and the China Biomedical Literature Database,with the search period from January 1,2014,to July 1,2024.Two researchers independently screened the literature and extracted data based on inclusion and exclusion criteria,using the Prediction model Risk Of Bias ASsessment Tool(PROBAST)to assess model quality.RESULTS:(1)A total of 3 126 articles were obtained in the preliminary search.After screening and exclusion,18 articles were finally included.150 prediction models were constructed using 13 machine learning methods.The three most frequently used methods are Logistic Regression,Random Forest,and Extreme Gradient Boosting(XGBoost).Only one study was externally validated.Eight studies reported how the missing data were handled.(2)In terms of outcome indicators,8 studies used the combination of clinical data and imaging data to build models,9 studies only used clinical data to build models,and 1 study only used imaging data to build models.(3)Each of the 18 studies gave the most important characteristics of the study,with the most mentioned being the National Institute of Health Stroke Scale and age.All studies reported area under curve values ranging from 0.74 to 0.96,with the highest area under curve being 0.96.The overall risk of bias in all models was high.The high risk of bias in the field of model analysis was the main reason for the high risk of overall bias in all models.(4)The results of meta-analysis showed that age and National Institute of Health Stroke Scale score had significant influence on stroke prognosis,with age[MD=8.49,95％CI(6.24,10.75),P＜0.01]and National Institute of Health Stroke Scale score[MD=4.78,95％CI(2.56,7.00),P＜0.01].CONCLUSION:This study systematically evaluated the predictive model of functional recovery and prognosis of stroke based on machine learning,and all the models have good predictive potential.However,future studies should increase the sample size of the included model,adopt prospective studies,and add external validation of the model to improve the stability and prediction accuracy of the model,control the risk of bias,and contribute to the validation and promotion of the model in practical clinical applications.At the same time,the interpolation of missing values is more transparent and accurate.Although existing machine learning models show good predictive performance,it is also important to focus on the functionality and usability of the model,and the inclusion of features will reduce ease of use.We should develop easy to use model interfaces and user-friendly clinical tools to enable medical staff to better apply the model for clinical decision.

Spine fracture and dislocation are common traumatic spinal conditions that often require surgical intervention due to compromised spinal stability. Surgical approaches include anterior, posterior, and combined anterior-posterior spinal procedures. According to the specific surgical requirements, patients may be placed in the prone position or repositioned between prone and supine positions during surgery. Intraoperative repositioning has become an essential step in patient positioning. However, during repositioning, patients with spinal fracture and dislocation are at increased risk for complications such as hemodynamic instability, nerve injury, and pressure injuries to the skin and soft tissue. Notably, due to the instability of the spinal cord, even minor manipulations can further exacerbate the damage, potentially leading to severe outcomes like paraplegia. Although the current clinical guidelines provide instructive recommendations for standard position, there remains no specific protocols for intraoperative repositioning in patients with spine fracture and dislocation. With a concern for the lack of clinical studies on positioning techniques, risk prevention, and operational norms for special patients, no applicable guidelines or standards are available. A consensus was required to provide clinical reference, meet the requirements of surgical treatment, and minimize the safety risks of patients caused by improper placement of positions. Professional Committee of Operating Room Nursing of Shaanxi Nursing Association organized experts in nursing management and operating room nursing from major hospitals across China to formulate Expert consensus on intraoperative repositioning for patients with spinal fracture and dislocation ( version 2025). The consensus provides 11 recommendations covering pre-repositioning preparation, intraoperative maneuvers, and post-repositioning observation, aiming to provide references for clinical standardization of the intraoperative repositioning process and protection of patients′ safety.

Objective:To investigate the prevalence of enterovirus 71 (EV71) and the genetic characteristics of VP1 region in common hand, foot and mouth disease (HFMD) cases in Shenzhen from 2016 to 2022.Methods:Throat swabs from mild HFMD in Shenzhen sentinel hospitals were collected from 2016 to 2022. A total of 38 EV71-positive samples were screened from these throat swabs and were sequenced. Then, the VP1 sequence of these EV71-positive samples were analyzed for their phylogenetic evolution by bioimformatics software DNAStar and MEGA 6.Results:From 2016 to 2022, the number of EV71 infections among HFMD patients in Shenzhen sentinel hospitals decreased from 136 in 2016 to 0 in 2022. The mumber of EV71 infections in 2018 and 2019 decreased by 96.3%(257/267) compared to that in 2016 and 2017. From 2020 to 2022, the number of EV71 infections decreased to 0. During this period, the EV71 vaccination rate among HFMD patients increased from 6.4% to 39.6%; Evolutionary analysis showed that the nucleotide homology and amino acid homology between 38 EV71 sample strains in Shenzhen from 2016 to 2022 were 91.8%-99.9% and 98.3%-100.0%, all belonging to the C4a subgenotype; Among them, 26 strains wene local epidemic strains, and 11 strains were imported from other provinces, with a close genetic relationship with epidemic strains in Hainan, Yunnan, Sichuan, Tianjin, Henan, Jilin, and other places. One strain from 2017 had the closest genetic relationship with the US epidemic strain OP207969-USA-2017. Further comparing the EV71 epidemic strains in Shenzhen from 2016 to 2022 and EV71 severe strains, it was found that the EV71 strains in Shenzhen carried four amino acid mutation sites related to severe condition, named R22H, K43R, I249V and T289A.Conclusions:The EV71 epidemic strains in Shenzhen from 2016 to 2022 all belong to the C4a subgenotype, and the number of EV71 infection shows a downward trend with the increase of vaccine coverage rate. At the same time, the distribution of EV71 virus strains in Shenzhen shows a significant decrease in local strains and a predominance of imported strains. There are a total of four amino acid mutation sites associated with severe cases in the EV71 sample strains in Shenzhen from 2016 to 2022. Among them, 22R and 289T are located at the N and C ends of VP1, which are related to EV71 adsorption and targeting cells. The 43R site is associated with binding ability to Annexin2 protein, which enhances cell binding ability.

Objective To investigate the genetic characteristics and recombination of human-infected sapoviruses(SaVs)worldwide using bioinformatics.Methods The complete genome sequences of SaVs were downloaded from the National Center for Biotechnology Information(NCBI)while high-quality complete genomes were retained for analysis.Molecular phylogenetic trees of SaVs were constructed to analyze their genetic characteristics,followed by recombination analysis of human-infected SaV strains genetype Ⅰ,Ⅱ,Ⅳ,and V(G Ⅰ,G Ⅱ,GⅣ,and GⅤ)with recombination analysis software.Results SaVs exhibited substantial genetic diversity worldwide and infected a wide range of hosts.Human-associated SaVs included G Ⅰ,G Ⅱ,GⅣ,and GⅤ,with GⅤ shared between human and swine hosts.Genetype recombination analysis of SaVs revealed a high frequency of recombination in SaV G Ⅱ strains that involved diverse hosts in the field of SaV G V strains.Recombination breakpoints of the virus were concentrated in the major viral proteins 1(VP1)and minor viral proteins 2(VP2).Conclusion Based on systematic analysis of the genetic characteristics of human-infected SaVs,the genotype distribution and prevalence of SaVs are investigated,the recombination patterns of SaV revealed,and its genetic dynamics highlighted.These findings can offer insights into epidemiological trends of viruses and help devise effective prevention and control strategies.

Diabetes mellitus is a chronic metabolic disease characterized by elevated blood glucose due to insufficient insulin secretion or insulin resistance.According to traditional Chinese medicine,diabetes mellitus is classified as"Xiaoke disease"in Chinese medicine,and its basic pathogenesis is yin deficiency and fluids.With the continuous improvement and development of traditional Chinese medicine theory,more and more doctors generally believe that"hot"runs through the occurrence and development of diabetes and the heat-clearing method is the key to the treatment of diabetes.The theory of the efficacy of traditional Chinese medicine believes that heat-clearing Chinese medicine has the effect of clearing heat and reducing fire,and its own effect of reducing heat and preserving Yin,which is in line with the principle of traditional Chinese medicine treatment of diabetes.Therefore,in this paper,we summarize the research progress on the role and mechanism of heat clearing herbs in the prevention and treatment of diabetes,mainly related to protection of pancreatic β-cell function,improvement of insulin resistance,inhibition of glucosidase activity,reduce the inflammatory response,relieve oxidative stress and regulation of intestinal flora,and analyze the problems and development trend of the current research,in order to provide a scientific and theoretical basis for the drug development and clinical application of heat clearing traditional Chinese medicine in the prevention and treatment of diabetes.

Diabetes mellitus is a chronic metabolic disease characterized by elevated blood glucose due to insufficient insulin secretion or insulin resistance.According to traditional Chinese medicine,diabetes mellitus is classified as"Xiaoke disease"in Chinese medicine,and its basic pathogenesis is yin deficiency and fluids.With the continuous improvement and development of traditional Chinese medicine theory,more and more doctors generally believe that"hot"runs through the occurrence and development of diabetes and the heat-clearing method is the key to the treatment of diabetes.The theory of the efficacy of traditional Chinese medicine believes that heat-clearing Chinese medicine has the effect of clearing heat and reducing fire,and its own effect of reducing heat and preserving Yin,which is in line with the principle of traditional Chinese medicine treatment of diabetes.Therefore,in this paper,we summarize the research progress on the role and mechanism of heat clearing herbs in the prevention and treatment of diabetes,mainly related to protection of pancreatic β-cell function,improvement of insulin resistance,inhibition of glucosidase activity,reduce the inflammatory response,relieve oxidative stress and regulation of intestinal flora,and analyze the problems and development trend of the current research,in order to provide a scientific and theoretical basis for the drug development and clinical application of heat clearing traditional Chinese medicine in the prevention and treatment of diabetes.

Objective:To explore the effects of mothers' exposure to polycyclic aromatic hydrocarbons during pregnancy on their children's neurobehavioral development.Methods:In November 2009 to April 2010, a total of 221 pairs of mother-newborn pairs were recruited from two cooperative hospitals in Taiyuan, and their children were followed up at age two. High performance liquid chromatography was used to determine the level of BPDE-DNA in cord blood leukocytes. The Neonatal behavioral neurological assessment (NBNA) was used to assess the neurodevelopment of newborns, and the Gesell Development Scale was used to measure neurodevelopmental indexes of 2-year-old children. NBNA includes behavior, active and passive tone, primitive reflexes and general assessment, with a total score of 40 points. The Gesell Developmental Schedules consisted of four sub-scales: motor development, adaptive behavior development, language development and personal-social behavior development. We used mean and standard deviation to describe continuous variables with normal distribution, median (interquartile range) to describe continuous variables with skewed distribution, and frequency and proportion to describe categorical variables. Restricted cubic spline models were applied to assess the dose-response relationships between maternal prenatal polycyclic aromatic hydrocarbons exposure and children's neurobehavioral development at two years old. Generalized linear models were applied to evaluate the effect of exposure to maternal prenatal polycyclic aromatic hydrocarbons exposure on children's neurobehavioral development at 0 and two years old.Results:The NBNA score was 38.0±0.8, and the scores of 2-year-old children's motor, adaptive, language and personal-social were 111.6±15.0, 110.5±14.6, 108.8±17.2 and 111.7±14.5, respectively. After adjusting for confounding factors, there is no dose-response association between the cord blood BPDE of pregnant women and neonatal NBNA scores, but there were dose-response associations between BPDE and scores of 2-year-old children's motor, adaptive, language and personal-social. A unit increase in cord blood ln (BPDE-DNA), the score of motor, adaptive, language and personal-social of 2-year-old children decreased on average by 4.54、6.29、8.41 and 7.02 points.Conclusion:Maternal exposure to polycyclic aromatic hydrocarbons during pregnancy is associated with decreased children's neurobehavioral development at two years old.

PET/CT imaging can reflect the physiological metabolic process in living body which is the model experiment incapable to simulate. Animal experiment may be considered for systematic validation of PET/CT products. The obtained research data can be used to evaluate the feasibility, effectiveness and safety of PET/CT products, and be submitted as supporting documents for research data or clinical evaluation data when doing product registration or alteration registration. In this study, the functions and advantages of animal experiments were expounded, and relevant research cases were given as well as the issues that should be paid attention to. It can be a reference for the validation and review of PET/CT products.
Animal Experimentation ; Animals ; Positron Emission Tomography Computed Tomography ; Positron-Emission Tomography ; Tomography, X-Ray Computed