Objective To explore the effect of remote ischemic preconditioning (RIPC) on preoperative heart rate variability in patients with heart valves. Methods Patients scheduled to undergo on-pump cardiac valve surgery in the Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, between January and July 2022 were initially enrolled. Eligible patients were randomly assigned at a 1 : 1 ratio to either the RIPC group or the control group. Relevant indicators of heart rate variability [standard deviation of NN interval (SDNN), standard deviation of mean value of NN interval in every five minutes (SDANN), mean square root of difference between consecutive NN intervals (RMSSD), percentage of adjacent RR interval>50 ms (PNN50), low frequency (LF) component, high frequency (HF) component and LF/HF] at 8 hours in the morning on the surgical day between two groups were compared. Results A total of 118 patients were initially assessed. After screening, 58 patients were excluded, and 60 patients provided written informed consent and were enrolled in the trial, with 30 allocated to the RIPC group and 30 to the control group. Seven patients in the control group and 5 patients in the RIPC group were subsequently excluded due to missing heart rate variability data resulting from cancelled operations. Finally, 23 patients in the control group and 25 patients in the RIPC group were included in the analysis. There was no statistical difference in baseline characteristics between the two groups, and there was no significant difference in heart rate variability 24 hours before intervention (P>0.05). After the intervention measures were taken, the comparison of the results of heart rate variability at 8 hours on the day of operation showed that SDNN and SDANN of patients in the RIPC group were higher than those in the control group, with statistical differences (P<0.05). Conclusion RIPC can stabilize the preoperative heart rate variability of patients undergoing cardiac valve surgery.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Objective To compare the mid- and long-term clinical results of mitral valve plasty (MVP) and mitral valve replacement (MVR) in the treatment of functional mitral regurgitation (FMR). Methods Patients with FMR who underwent surgical treatment in the Department of Cardiovascular Surgery of the General Hospital of Northern Theater Command from 2012 to 2021 were collected. The patients who underwent MVP were divided into a MVP group, and those who underwent MVR into a MVR group. The clinical data and mid-term follow-up efficacy of two groups were compared. Results Finally 236 patients were included. There were 100 patients in the MVP group, including 53 males and 47 females, with an average age of (61.80±8.03) years. There were 136 patients in the MVR group, including 72 males and 64 females, with an average age of (61.29±8.97) years. There was no statistical difference in baseline data between the two groups (P＞0.05). There was no statistical difference between the two groups in the extracorporeal circulation time, aortic occlusion time, postoperative hospital and ICU stay, intraoperative blood loss, or hospitalization death (P＞0.05), but the time of mechanical ventilation in the MVP group was significantly shorter than that in the MVR group (P=0.022). The total follow-up rate was 100.0%, the longest follow-up was 10 years, and the average follow-up time was (3.60±2.55) years. There were statistical differences in the left atrial diameter, left ventricular end-diastolic diameter, left ventricular end-systolic diameter and cardiac function between the two groups compared with those before surgery (P<0.05). The postoperative left ventricular ejection fraction in the MVP group was statistically higher than that before surgery (P=0.002), but there was no statistical difference in the MVR group before and after surgery (P=0.658). The left atrial diameter in the MVP group was reduced compared with the MVR group (P=0.026). The recurrence rate of mitral regurgitation in the MVP group was higher than that in the MVR group, and the difference was statistically significant (10.0% vs. 1.5%, P=0.003). There were 14 deaths in the MVP group and 19 in the MVR group. The cumulative survival rate (P=0.605) and cardiovascular events-free survival rate (P=0.875) were not statistically significant between the two groups by Kaplan-Meier survival analysis. Conclusion The safety, and mid- and long-term clinical efficacy of MVP in the treatment of FMR patients are better than MVR, and the left atrial and left ventricular diameters are statistically reduced, and cardiac function is statistically improved. However, the surgeon needs to be well aware of the indications for the MVP procedure to reduce the rate of mitral regurgitation recurrence.

Objective To analyze the current status and influencing factors of occupational stress, job burnout and sleep quality among workers in the secondary industry in Jinshan District, Shanghai City. Methods A total of 1 418 workers from six key industries in Jinshan District, Shanghai City were selected as the study subjects by the stratified cluster sampling method. The Occupational Stress Core Scale, Maslash Burnout Inventory General Survey and Pittsburgh Sleep Quality Index were used to investigate occupational stress, job burnout and sleep quality of the workers. Results The detection rates of occupational stress, job burnout and sleep disturbance among the study subjects were 33.6%, 65.4% and 23.3%, respectively. Multivariate logistic regression analysis showed that the workers with a monthly income <5 000 yuan had a higher risk of occupational stress than those with a monthly income ≥5 000 yuan (P<0.01). The workers with ≥5.0 years of service had a higher risk than those with <1.0 year (P<0.05). Lack of physical exercise, employment in medium- and large-sized enterprises, and shift work were risk factors of occupational stress in the workers (all P<0.01). The workers aged 18-<30 years had a higher risk of job burnout than those aged 45-<60 years (P<0.05). The workers monthly income <5 000 yuan was associated with a higher risk of job burnout than those with ≥9 000 yuan (P<0.05). The workers with 1.0-<10.0 years or ≥15.0 years of service had higher job burnout risks than those with <1.0 year (all P<0.05). Being unmarried, lack of physical exercise, and employment in medium- and large-sized enterprises were risk factor of job burnout in the workers (all P<0.05). The workers with an educational level of high school or above had a higher risk of sleep disturbance than those with junior school or below (P<0.05). The workers who work >56 hours per week had a higher risk than those working ≤40 hours per week (P<0.01). Conclusion There is a high detection rate of occupational stress, job burnout, and sleep disturbance in the secondary industry workers in Jinshan District, Shanghai City. Special attention should be given to workers with low income, lack of physical exercise, employment in medium- and large-sized enterprises, shift work, long service duration, and long weekly working hours to protect their physical and mental health.

Objective To identify the most significantly compressed areas and the areas with the best recovery effects by analyzing the changes in vertebral height after percutaneous vertebroplasty(PVP)in patients with osteoporotic vertebral compression fractures(OVCF)through lateral radiographs.Methods A retrospective analysis was conducted on the lateral X-rays of 186 injured vertebrae from 142 patients hospitalized in our hospital's intervertebral disc center.The sagittal height of the vertebrae was measured at five different points before and after surgery,and the collected data were statistically analyzed using SPSS software.Results There were statistically significant differences in the heights of the five measured points before and after surgery within OVCF injured vertebrae(P＜0.05),in the ascending order:central＜mid-anterior＜mid-posterior＜anterior edge＜posterior edge.Comparison of the height parameters of the five measured points before and after surgery showed statistically significant differences(P＜0.01).In comparing the height restoration differences of the five measured points after PVP,the differences between central and mid-anterior,central and anterior edge,and mid-posterior and anterior edge were found not to be statistically significant(P＞0.05).The differences in height restoration for the remaining groups were statistically significant(P＜0.05),with the height restoration differences from highest to lowest being:mid-anterior,central,anterior edge,mid-posterior,posterior edge.Conclusion In patients with OVCF,the compression of the injured vertebra is most pronounced in the central part,followed by the mid-anterior part.PVP surgery can effectively restore the height of various parts of the injured vertebra,especially in the mid-anterior and central parts of the vertebral body,where the recovery effect is particularly significant.

Objective Exploring the performance of a sensitivity recognition model for neoadjuvant therapy based on ensemble learning algorithms for predicting neoadjuvant therapy sensitive patients.Methods In this study,255 rectal cancer patients who underwent standard neoadjuvant therapy and surgery at the Affiliated Cancer Hospital of Harbin Medical University were collected,of which 139 patients were sensitive to neoadjuvant therapy and 116 patients were not.The recursive feature elimination method was used to screen deep learning features and imaging histology features to construct an integrated learning model.The generalization performance of the model was verified using the leave-out method,and the sensitivity,specificity,positive predictive value,negative predictive value,accuracy,G-mean,F-measure,Mathews correlation coefficient(MCC),and area under the receiver operating charactpristic curve(AUC)were used to evaluate and compare the model performance.Results The performance of the integrated model on the external validation set was:an AUC value of 0.916(95％CI:0.899 to 0.935),a sensitivity of 1.000,a specificity of 0.833,a positive predictive value of 0.875,a negative predictive value of 1.000,an accuracy of 0.923,a G-mean of 0.913,an F-measure of 0.933,a MCC was 0.854.Conclusion The integration model of depth features and imaging omics features is superior to the individual depth feature model and imaging omics feature model,and has good practicality and reliability in identifying sensitive patients with neoadjuvant therapy,which can be used as a reference for clinical practice.

Objective:To analyze the three-dimensional radiographic characteristics of solid variant of odontogenic keratocyst(SOKC)using multi-slice spiral tomography(MSCT).Methods:Clinical records,histopathological reports and MSCT images of 8 patients were retrospectively acquired,radiographic features,including lesion site,size,borders,jaw expansion,internal structures and relationship with surrounding tissues were analyzed.Results:8 cases(4 males and 4 females)aged 37-65 years were included.Among them,4 lesions were located in the maxilla and the other 4 in mandible.Clinically,the lesions manifested as an enlargement of the affected area in the jaw bone,with pain present in 6 cases.On MSCT scans,all lesions showed evident jaw expansion.The boundaries of the lesions were clear in 3 cases,and unclear in 5 cases.4 cases exhibited multilocular radiolucent lesions,while the other 4 cases showed mixed radiolucent/opaque lesions resembling fibro-osseous lesion.The maxilla lesions involved the palate,na-sal cavity,maxillary sinus and orbital floor.All mandible SOKCs were recurrent and infectious,with involvement of the surrounding soft tissue.Conclusion:SOKC exhibits imaging characteristics that differentiate from conventional odontogenic keratocysts.Radio-graphically,it presents as a benign or low-grade malignant solid jaw mass.

Retroperitoneal sarcoma is a rare but highly malignant type of soft tissue tumor,and its diagnosis and treatment have long been focal points in clinical research.In December 2021,the Japanese Society for Sarcoma Research,together with several other medical organizations,published the Clinical practice guidelines for the management of retroperitoneal sarcoma,which were revised in April 2023.The guidelines provide recommendations on three key aspects:the diagnosis of retroperitoneal tumors,treatment of primary retroperitoneal sarcomas,and management of recurrent or unresectable cases.They also address 11 clinical questions derived from these topics and,for the first time,present a systematic diagnostic and treatment algorithm for this disease—offering important reference value for standardizing the management of retroperitoneal sarcoma in China.The diagnostic process includes assessment of clinical features,imaging evaluation,pathological diagnosis,and biopsy.Despite the technical challenges,surgical resection remains the mainstay of treatment,with a particular emphasis on achieving R0 resection.In addition,chemotherapy,radiotherapy,particle therapy,and targeted therapy also play crucial roles.This article focuses on analyzing and discussing the guideline's recommendations on imaging,pathological diagnosis,and surgical resection,in comparison with other domestic and international guidelines.It further explores the effectiveness of current non-surgical treatment strategies based on recent advances in particle and immunotherapy,and looks ahead to the prospects of improving patient outcomes through personalized treatment,multimodal therapy,and multidisciplinary collaboration.

Objective To analyze the disease burden of early-onset colorectal cancer (EOCRC) at the global, regional, and national levels from 1990 to 2021, and to predict the disease burden trend from 2022 to 2026. Methods Based on the Global Burden of Disease (GBD) database, the incidence, mortality, and disability-adjusted life year (DALY) rate of EOCRC across 204 countries and regions from 1990 to 2021 were obtained. The time trends of these indicators were assessed by calculating the estimated annual percentage change (EAPC), and the contributions of ten risk factors to the EOCRC burden were analyzed. The autoregressive integrated moving average (ARIMA) model was used to predict the disease burden from 2022 to 2026. Results From 1990 to 2021, the number of new global EOCRC cases increased from 107 310 to 211 890, with the incidence rising from 3.96 to 5.37 per 100 000 people. In 2021, global EOCRC incidence, mortality, and DALY rate increased with age; males had higher rates than females in terms of incidence, mortality, and DALY rate in all age groups. In 2021, East Asia had the highest number of new cases, deaths, and DALY. From 1990 to 2021, the global EAPC for incidence rate was 0.96%, and death rate was –0.38%. ARIMA model indicated that from 2022 to 2026, the global incidence of EOCRC would continue to rise, while mortality and DALY rate would be expected to decline. Conclusions The disease burden of EOCRC has significantly increased globally from 1990 to 2021, with notable regional, age, and sex differences. By 2026, the mortality and DALY rate of EOCRC will decline, while the incidence is expected to further increase, highlighting the urgency of taking active measures to address the growing trend of EOCRC.