Objective To explore the effectiveness of the peripheral arterial calcification scoring system(PACSS)based on computed tomography angiography(CTA)in assessing renal function in patients with peripheral arterial disease(PAD).Methods The clinical data,CTA imaging data,and laboratory results from PAD patients who underwent lower limb artery CTA examination were retrospectively collected.The PACSS was utilized to score the calcification in both lower limb arteries.Participants were categorized into three groups based on their estimated glomerular filtration rate(eGFR)(normal group:eGFR≥90 mL/min;mild renal dysfunction group:eGFR 60-89 mL/min,and moderate to severe renal dysfunction group:eGFR＜60 mL/min).The demographic data,clinical symptoms,and comorbidities among the three groups were compared by analysis of variance(ANOVA).The Spearman correlation coefficient was employed to evaluate the relationship between eGFR,cystatin C,and PACSS score.Results The age(P＜0.001)and PACSS score(P＜0.05)of patients with renal dysfunction were significantly higher than those of patients with normal renal function.However,there were no significant differences in gender,prevalence of diabetes,hypertension,or severe limb ischemia.Spearman correlation analysis showed that eGFR was negatively correlated with PACSS score(r=-0.18 in the right lower limb,P=0.037,r=-0.24 in the left lower limb,P=0.006).In contrast,cystatin C was positively correlated with PACSS score(r=0.26 in the right lower limb,P＜0.001,r=0.22 in the left lower limb,P=0.002).Conclusion The PACSS score of lower limb artery in PAD patients is corre-lated with the severity of renal dysfunction.This finding may facilitate early warning and clinical intervention for PAD patients with renal dysfunction.

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 To explore the effectiveness of the peripheral arterial calcification scoring system(PACSS)based on computed tomography angiography(CTA)in assessing renal function in patients with peripheral arterial disease(PAD).Methods The clinical data,CTA imaging data,and laboratory results from PAD patients who underwent lower limb artery CTA examination were retrospectively collected.The PACSS was utilized to score the calcification in both lower limb arteries.Participants were categorized into three groups based on their estimated glomerular filtration rate(eGFR)(normal group:eGFR≥90 mL/min;mild renal dysfunction group:eGFR 60-89 mL/min,and moderate to severe renal dysfunction group:eGFR＜60 mL/min).The demographic data,clinical symptoms,and comorbidities among the three groups were compared by analysis of variance(ANOVA).The Spearman correlation coefficient was employed to evaluate the relationship between eGFR,cystatin C,and PACSS score.Results The age(P＜0.001)and PACSS score(P＜0.05)of patients with renal dysfunction were significantly higher than those of patients with normal renal function.However,there were no significant differences in gender,prevalence of diabetes,hypertension,or severe limb ischemia.Spearman correlation analysis showed that eGFR was negatively correlated with PACSS score(r=-0.18 in the right lower limb,P=0.037,r=-0.24 in the left lower limb,P=0.006).In contrast,cystatin C was positively correlated with PACSS score(r=0.26 in the right lower limb,P＜0.001,r=0.22 in the left lower limb,P=0.002).Conclusion The PACSS score of lower limb artery in PAD patients is corre-lated with the severity of renal dysfunction.This finding may facilitate early warning and clinical intervention for PAD patients with renal dysfunction.

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 analyze the consistency between computer tomography angiography(CTA)and digital subtraction angiography(DSA)in evaluating the global limb anatomic staging system(GLASS)stage of patients with chronic limb-threatening ischemia(CLTI).Methods The clinical data of patients with CLTI,who were admitted to the First Affiliated Hospital of Xi'an Jiaotong University of China to receive treatment between January 2017 and December 2020,were retrospectively analyzed.Taking the DSA assessment as the gold standard,the consistency of CTA and DSA in evaluating the GLASS stage of patients with CLTI was analyzed.Results In the assessment of GLASS stage of CLTI,CTA showed strong agreement with DSA.The weighted Kappa coefficient of CTA and DSA for the staging of femoropopliteal segment was 0.798(95％CI=0.722-0.873,P＜0.01),and the weighted Kappa coefficient of CTA and DSA for the staging of infrapopliteal artery segment was 0.785(95％ CI=0.725-0.845,P＜0.0l).For the overall staging of GLASS,the weighted Kappa coefficient of CTA and DSA was 0.832(95％ CI=0.752-0.91 1,P＜0.01).All the above results indicated that a very strong consistency existed between CTA and DSA in evaluating the GLASS stage of patients with CLTI.Conclusion CTA examination of lower limb can accurately evaluate GLASS score and stage of CLTI patient's target lesions,which is helpful in diagnosing lower extremity arteriosclerosis occlusion disease as well as in assessing the technical difficulty degree of its revascularization operation.(J Intervent Radiol,2024,33:300-303)

Objective To investigate the relationship between the triglyceride glucose product(TyG)in-dex,plasma arteriosclerosis index(AIP),systemic immune inflammation index(SII),and the severity of acute coronary syndrome(ACS)and coronary artery stenosis degree in patients.Methods A retrospective analysis was conducted on patients who underwent coronary angiography at the hospital from November 2021 to De-cember 2023 due to suspected coronary heart disease,and they were divided into an ACS group(1 446 cases)and a control group(690 cases).General baseline and laboratory data of the patients were collected,and TyG index,AIP and SII in were calculated.Gensini score was used to evaluate the degree of coronary artery steno-sis.The risk factors of ACS were determined by binary multivariate Logistic regression analysis.Receiver op-erating characteristic(ROC)curve was drawn to analyze the diagnostic value of various indicators for the de-gree of coronary artery stenosis in ACS patients.Results TyG index,AIP and SII in ACS group were signifi-cantly higher than those in control group(P＜0.05).Binary multivariate Logistic regression analysis results showed that TyG index,AIP and SII were risk factors for ACS(P＜0.05).The ROC curve results showed that TyG index,AIP and SII had certain diagnostic value for the degree of coronary artery stenosis in ACS pa-tients.According to the cut off values of TyG index,AIP,and SII,the patients were further divided into differ-ent subgroups,the Gensini scores in the high TyG index,AIP,and SII groups were significantly higher than those in the low TyG index,AIP,and SII groups(P＜0.05).Conclusion TyG index,AIP and SII are closely related to ACS,which could be used as simple laboratory indexes for diagnosis of ACS,and also have good di-agnostic value for the coronary artery stenosis degree.

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: PS128 is a novel psycho biotic strain, it has been reported to play an important role in neuropsychiatric disorders. This study investigated the clinical effect of PS128 supplementation on patients with anxiety. Methods: A total of 200 patients with anxiety were recruited, and divided into two groups (n = 100/group). The control group received oral treatment with citalopram, and the PS128 group received PS128 capsules based on citalopram treatment. Hamilton Anxiety Scale (HAMA) and Self-Rating Anxiety Scale (SAS) were used to evaluate the anxiety levels.After 2 months of continuous administration, clinical efficacy was evaluated according to HAMA score. Results: There was no significant difference in HAMA and SAS scores between the two groups before treatment. With the treatment prolonged, the HAMA and SAS score decreased gradually in both control and PS128 groups, and the decrease rate of PS128 group was significantly greater than that of the control group. The clinical effective rates of PS128 group were higher than those in the control group, high levels of clinical cure rate were also detected in the PS128 group. Compared with the control group (22%), the incidence of adverse reactions was significantly reduced for patients in the PS128 group (4%). Conclusion The treatment effect of citalopram combined with PS128 against anxiety is satisfactory clinically. It can greatly improve the anxiety symptoms of patients, increase the cure rate, reduce adverse reactions.