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 significance of urinary 8-oxo-7, 8-dihydroguanosine(8-oxoGuo)in assessing the short-term prognosis of sepsis in frail elderly patients.Methods:We conducted a cross-sectional study involving 62 frail elderly patients diagnosed with sepsis who were admitted to the Emergency Intensive Care Unit(EICU)at Beijing Hospital between March 2021 and March 2022.Based on their 28-day prognosis, the patients were categorized into two groups: those who died and those who survived.Upon admission, we collected urine samples and clinical data from both groups.We employed isotope dilution high-performance liquid chromatography-mass spectrometry to measure the levels of the RNA oxidation marker 8-oxoGuo in the urine.Results:A total of 62 frail elderly patients[aged(85.1±6.3)years]diagnosed with sepsis were included in the study, comprising 36 patients in the 28-day mortality group and 26 patients in the survival group.Univariate analysis revealed that the survival group had significantly lower body temperature, blood calcitonin(PCT)levels, sequential organ failure assessment(SOFA)scores, and urinary 8-oxoGuo levels compared to the mortality group.Additionally, the survival group exhibited a higher mean arterial pressure(MAP)than the mortality group, with all differences reaching statistical significance(all P<0.05).Spearman correlation analysis indicated that urinary 8-oxoGuo levels were positively correlated with both PCT and SOFA scores in frail elderly sepsis patients( r=0.426, 0.768, both P<0.05).Furthermore, logistic regression analysis identified urinary 8-oxoGuo and SOFA as independent risk factors for 28-day mortality in this population( OR=1.936, 1.427; P=0.006, 0.002).The area under the receiver operating characteristic curve(AUC)for urinary 8-oxoGuo and SOFA in predicting the 28-day prognosis of frail elderly sepsis patients was 0.761 and 0.741, respectively, both demonstrating statistical significance(both P<0.001). Conclusions:Our findings suggest that urinary 8-oxoGuo possesses strong predictive value for the short-term prognosis of sepsis in this vulnerable population.

Objective:To synthesize a hydrolysis-resistant urushiol-modified monomer(UMM)to improve the hydrolysis resistance of light-cured composite resin,while reducing the volume shrinkage rate(VS),increasing the double bond conversion rate(DC),and mitigating the potential biosafety concerns of bisphenol A glycidyl methacrylate(Bis-GMA)monomer.Methods:UMM was synthesized by modifying urushiol via an acyl chloride reaction,and its structure was analyzed and characterized using Fourier transform infrared spectroscopy(FT-IR).Control group was consisted of Bis-GMA/triethylene glycol dimethacrylate(TEGDMA)without UMM,while 10%UMM,15%UMM,and 20%UMM groups were prepared by partially replacing Bis-GMA with UMM at mass fractions of 10%,15%,and 20%,respectively.The viscosity of UMM was measured using a rheometer.The DC of light-cured composite resin in various groups was detected by FT-IR spectroscopy,and the VS was calculated.The contact angle of light-cured composite resin in various groups was measured using the sessile drop method,and the water sorption and solubility values were calculated.The mechanical properties of light-cured composite resin in various groups were tested.The in vitro cytotoxicity of light-cured composite resin in various groups was evaluated using the cell counting kit-8(CCK-8)assay.Results:The FT-IR spectra results showed that the absorption peak of the hydroxyl group at 3 402 cm-1 disappeared,while characteristic absorption peaks of-C=O and-C=C appeared at 1 745 and 1 637 cm-1,indicating that urushiol successfully reacted with acryloyl chloride to form UMM.The viscosity of UMM ranged from 25.14 to 29.43 Pa·s.Compared with control group,the DC of light-cured composite resin in 10%UMM,15%UMM,and 20%UMM groups was significantly increased(P<0.05),while the VS was significantly decreased(P<0.05),both in a dose-dependent manner.Compared with control group,the contact angle of light-cured composite resin in 10%UMM,15%UMM,and 20%UMM groups was significantly increased(P<0.05).Compared with 10%UMM group,the contact angle of light-cured composite resin in 15%UMM and 20%UMM groups was further increased(P<0.05).Compared with control group,the water sorption and solubility values of light-cured composite resin in 10%UMM,15%UMM,and 20%UMM groups were significantly decreased(P<0.05),showing a dose-dependent trend.After 24 h of water immersion,compared with control group,the flexural strength(FS)and elastic modulus(EM)of light-cured composite resin in 10%UMM,15%UMM,and 20%UMM groups were significantly decreased(P<0.05),also in a dose-dependent manner.After 7 d of water immersion,compared with control group,the FS of light-cured composite resin in 10%UMM group was significantly increased(P<0.05),while that in 20%UMM group was significantly decreased(P<0.05).Compared with 10%UMM group,the FS of light-cured composite resin in 15%UMM and 20%UMM groups was significantly decreased(P<0.05),exhibiting a dose-dependent trend.Compared with control group,the EM of light-cured composite resin in 15%UMM and 20%UMM groups was significantly decreased(P<0.05),also in a dose-dependent manner.The relative growth rate(RGR)of the L929 cells in control,10%UMM,15%UMM,and 20%UMM groups was above 90%,with no statistically significant differences among groups(P>0.05),and all cytotoxicity results were qualified.Conclusion:A novel low-viscosity monomer UMM is successfully synthesized in this study.All UMM-containing light-cured composite resin formulations exhibit higher DC,lower VS,reduced water sorption and solubility values,improved hydrolysis resistance,and low cytotoxicity.UMM can serve as a potential resin monomer to enhance the hydrolysis resistance of light-cured composite resin.

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 significance of urinary 8-oxo-7, 8-dihydroguanosine(8-oxoGuo)in assessing the short-term prognosis of sepsis in frail elderly patients.Methods:We conducted a cross-sectional study involving 62 frail elderly patients diagnosed with sepsis who were admitted to the Emergency Intensive Care Unit(EICU)at Beijing Hospital between March 2021 and March 2022.Based on their 28-day prognosis, the patients were categorized into two groups: those who died and those who survived.Upon admission, we collected urine samples and clinical data from both groups.We employed isotope dilution high-performance liquid chromatography-mass spectrometry to measure the levels of the RNA oxidation marker 8-oxoGuo in the urine.Results:A total of 62 frail elderly patients[aged(85.1±6.3)years]diagnosed with sepsis were included in the study, comprising 36 patients in the 28-day mortality group and 26 patients in the survival group.Univariate analysis revealed that the survival group had significantly lower body temperature, blood calcitonin(PCT)levels, sequential organ failure assessment(SOFA)scores, and urinary 8-oxoGuo levels compared to the mortality group.Additionally, the survival group exhibited a higher mean arterial pressure(MAP)than the mortality group, with all differences reaching statistical significance(all P<0.05).Spearman correlation analysis indicated that urinary 8-oxoGuo levels were positively correlated with both PCT and SOFA scores in frail elderly sepsis patients( r=0.426, 0.768, both P<0.05).Furthermore, logistic regression analysis identified urinary 8-oxoGuo and SOFA as independent risk factors for 28-day mortality in this population( OR=1.936, 1.427; P=0.006, 0.002).The area under the receiver operating characteristic curve(AUC)for urinary 8-oxoGuo and SOFA in predicting the 28-day prognosis of frail elderly sepsis patients was 0.761 and 0.741, respectively, both demonstrating statistical significance(both P<0.001). Conclusions:Our findings suggest that urinary 8-oxoGuo possesses strong predictive value for the short-term prognosis of sepsis in this vulnerable population.

Objective:To explore the nurses' willingness to stay at work in ClassⅢ hospitals of Chinese medicine, and provide a reference for developing retention strategies.Methods:From May to September 2017, a total of 1 080 nurses from 3 ClassⅢ hospitals of Chinese medicine in Western, Central and Eastern China were selected by cluster sampling and investigated with the demographic data questionnaire, Nurses' Willingness to Stay at Work Questionnaire, Job Satisfaction Scale, Job Control Scale and Transformational Leadership Questionnaire. Multiple hierarchical regression analysis was used to analyze the influencing factors of nurses' willingness to stay at work. A total of 1 080 questionnaires were distributed, and 918 valid questionnaires were collected, accounting for an effective recovery rate of 85.00%.Results:The average score of the Nurses' Willingness to Stay at Work Questionnaire in the 918 Chinese medicine nurses was (3.45±0.63) . There were statistically significant differences in the scores of the Nurses' Willingness to Stay at Work Questionnaire between nurses with different positions, job titles, forms of employment, number of night shifts per month, and monthly income ( P< 0.05) . The results of multiple hierarchical regression analysis revealed that job titles, number of night shifts per month, job satisfaction, work efficiency control, and vision incentives were the main factors affecting nurses' willingness to stay at work ( P < 0.05) , accounting for 28.0% of the total variation. Conclusions:The nurses' willingness to stay at work in hospitals of Chinese medicine is at a medium level, and hospital managers should appropriately adjust night shifts, give nurses appropriate incentives, and strengthen their job autonomy, so as to improve their job satisfaction and stabilize their willingness to stay at work.

Objective To establish a polymerase spiral reaction (PSR) method for rapid detection of influenza A/H1N1 virus.Methods Six sets of primers were designed for influenza A/H1N1 virus HA gene, and the results were determined with real time kinetic turbidimetric assay and colorimetry method.Results and Conclusion The best primers were selected from six sets of primers, and the best temperature was determined as 65 degrees Celsius.Further experiments showed that the best primer had good specificity for detection of influenza A/H1N1 virus,without cross reactions with 14 other respiratory tract pathogenic nucleic acids.The sensitivity was up to 100 copies,and consistent with that of PCR.So a PSR method is established for rapid detection of the influenza A/H1N1 virus, which is simple, quick, highly specific and sensitive,and especially applicable to field and grass-roots units.

Objective To evaluate the efficacy of emergency endoscopic intervention in acute obstructive suppurative cholangitis (AOSC) complicated with septic shock.Methods A total of 54 patients with AOSC and septic shock who underwent therapeutic emergency ERCP were included in this retrospective study,and were evaluated by the shock index (SI).Results ERCP was performed for all patients in 24hours after hospitalization,and the average ERCP operation time was 23.8 ± 12.5 min.All 54 patients underwent EST,46 of whom received ENBD,7 biliary stenting and 1 transferred to surgery due to bleeding.The post-ERCP mortality rate was 0,and the complications included 1 case of pancreatitis and 2 cases of pneumonia.The positive rate ofGram-Negative bacillus before ERCP was 46.9％ (15/32).The SI before ERCP was 1.250 ±0.200,which decreased to 0.950 ±0.119 at 2hr after the procedure (P ＜0.001 ),and decreased further to 0.598 ± 0.099 ( P ＜ 0.001 ) at 24 hours after ERCP.Conclusion Therapeutic emergency ERCP is of great importance in the treatments for AOSC complicated with septic shock.