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 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 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 investigate the effects of pulsed electromagnetic field(PEMF)on sevoflurane-induced cognitive dysfunction in elderly rats and also explore its related mechanism.Methods:Thirty elderly male rats were randomly divided into the control group,sevoflurane treatment group(SEV),and sevoflurane+PEMF treatment group(SEV+PEMF).Rats in the sevoflurane group and sevoflurane+PEMF group passively inhaled 2.5％sevoflurane for 4 h,while rats in the SEV+PEMF group were stimulated with 2 mT,15 Hz PEMF for 14 d(2 h/day).The cognitive function of rats was evaluated via the Morris water maze testing.The serum concentrations of tumor necrosis factor-α(TNF-α),interleukin-1 β(IL-1β),IL-6,neuron specific enolase(NSE),and β amyloid protein(Aβ),as well as the levels of nerve growth factor(NGF)and brain-derived neurotrophic factor(BDNF)in hippocampal tissue,were de-termined via ELISA.Western blot was used to detect the expression of autophagy-related biomarkers in rat hippocampal tissue.Secondly,30 elderly male rats were randomly divided into three groups:SEV group,SEV+PEMF group,and SEV+3-MA(the autophagy inhibitor)+PEMF group.The Morris water maze experiment was used to evaluate the change of PEMF-induced improvement of cognitive function sevoflurane-inhaled elderly rats following the autophagy inhi-bition.Results:PEMF inhibited sevoflurane-induced increase in escape latency and overall swimming distance,as well as the decrease in the number of crossing target quadrant(P＜0.05);PEMF decreased the levels of serum Aβ and NSE in elderly rats inhaled with sevoflurane(P＜0.05),decreased the levels of TNF-α,IL-1β,and IL-6(P＜0.05),increased the levels of NGF and BDNF in hippocampal tissue(P＜0.05),inhibited neuronal apoptosis in hip-pocampal tissue and increased its autophagy level(P＜0.05).Following inhibition of autophagy with 3-MA,the im-provement of PEMF on the decreased learning and memory ability induced by sevoflurane in elderly rats was significantly inhibited(P＜0.05).Conclusion:PEMF can effectively inhibit sevoflurane-induced cognitive dysfunction in elderly rats by regulating the autophagy of hippocampal neuronal cells.

Objective To investigate the ameliorative effect and the related mechanism of rotating magnetic fields(RMF)on sevoflurane(SEV,a commonly used inhalational anesthetics in clinics)-induced cognitive impairments in elderly rats.Methods The cognitive functions of 24 elderly male SD rats which were allocated into 3 experimental groups(Control group,SEV group,and SEV+RMF group;n=8 per group)were assessedviawater maze testing,and the damage and repair of hippocampal neurons were detected using ELISA and Western blotting assays.Water maze testing was also conducted on additional 24 elderly male SD rats which were randomized into 3 groups(SEV group,SEV+RMF group,and SEV+autophagy inhibitor+RMF group;n=8 per group)for assessing their cognitive functions.Results Compared with SEV group,RMF intervention significantly reduced the escape latency and swimming distance in water maze test(P<0.01),while increasing the number of platform crossovers(P<0.001).RMF downregulated the serum levels of tumor necrosis factor-α,interleukin-1β,and interleukin-6 in SEV-treated rats(P<0.001),and led to remarkable reductions in both serum neuron-specific enolase and β-amyloid protein levels(P<0.001).Moreover,RMF upregulated the expressions of nerve growth factor and brain-derived neurotrophic factor in hippocampal neurons(P<0.001),suppressed the expression of apoptotic protein caspase-3 and autophagy related protein p62 in the hippocampus,and upregulate the expression of autophagy related protein Beclin-1(P<0.001).However,the administration of autophagy inhibitor 3-MA abolished the ameliorative effects of RMF on SEV-induced cognitive impairments in elderly rats(P<0.001).Conclusion RMF can effectively improves SEV-induced cognitive dysfunction in elderly rats,and this neuroprotective effect is mediated by the autophagy activation in hippocampal neurons.

Objective To investigate the ameliorative effect and the related mechanism of rotating magnetic fields(RMF)on sevoflurane(SEV,a commonly used inhalational anesthetics in clinics)-induced cognitive impairments in elderly rats.Methods The cognitive functions of 24 elderly male SD rats which were allocated into 3 experimental groups(Control group,SEV group,and SEV+RMF group;n=8 per group)were assessedviawater maze testing,and the damage and repair of hippocampal neurons were detected using ELISA and Western blotting assays.Water maze testing was also conducted on additional 24 elderly male SD rats which were randomized into 3 groups(SEV group,SEV+RMF group,and SEV+autophagy inhibitor+RMF group;n=8 per group)for assessing their cognitive functions.Results Compared with SEV group,RMF intervention significantly reduced the escape latency and swimming distance in water maze test(P<0.01),while increasing the number of platform crossovers(P<0.001).RMF downregulated the serum levels of tumor necrosis factor-α,interleukin-1β,and interleukin-6 in SEV-treated rats(P<0.001),and led to remarkable reductions in both serum neuron-specific enolase and β-amyloid protein levels(P<0.001).Moreover,RMF upregulated the expressions of nerve growth factor and brain-derived neurotrophic factor in hippocampal neurons(P<0.001),suppressed the expression of apoptotic protein caspase-3 and autophagy related protein p62 in the hippocampus,and upregulate the expression of autophagy related protein Beclin-1(P<0.001).However,the administration of autophagy inhibitor 3-MA abolished the ameliorative effects of RMF on SEV-induced cognitive impairments in elderly rats(P<0.001).Conclusion RMF can effectively improves SEV-induced cognitive dysfunction in elderly rats,and this neuroprotective effect is mediated by the autophagy activation in hippocampal neurons.

Objective To investigate the effect of ubiquitin-specific peptidase(USP)44 and nuclear receptor co-inhibitor 1(NCOR1)expression on prognosis in non-small cell lung cancer.Methods A total of 98 pa-tients with non-small cell lung cancer admitted to a hospital from May 2019 to May 2021 were selected as the study objects,and non-small cell lung cancer tissues and adjacent tissues were collected to detect the expres-sion levels of USP44 and NCOR1 in these tissues by immunohistochemical staining.The relationship between USP44 and NCOR1 expression and pathological features of non-small cell lung cancer patients was analyzed,and the prognostic factors of non-small cell lung cancer patients were analyzed by multivariate Cox regression.Results The positive expression rates of USP44 and NCOR1 in non-small cell lung cancer tissues were higher than those in adjacent tissues,the difference was statistically significant(P<0.05).The positive expression rates of USP44 and NCOR1 in patients with medium-low differentiation,lymph node metastasis,clinical stageⅢ to Ⅳ,and pleural metastasis were higher than those in patients with highly differentiated,no lymph node metastasis,clinical stage Ⅰ to Ⅱ,and no pleural metastasis,and the difference was statistically significant(P<0.05).The 3-year overall survival rate of USP44 and NCOR1 negative non-small cell lung cancer patients was higher than that of USP44 and NCOR1 positive non-small cell lung cancer patients,and the difference was statistically significant(all P<0.05).Multivariate Cox regression analysis showed that pleural metastasis,USP44 positive and NCOR1 positive were prognostic factors in non-small cell lung cancer patients(P<0.05).Conclusion The expression of USP44 and NCOR1 in patients with non-small cell lung cancer can be used as biomarkers for prognosis assessment,and provide evidence for progression assessment and clinical de-cision making of non-small cell lung cancer.

Objective:To investigate the impact of postoperative complications on adverse outcomes following curative-intent resection for gallbladder cancer (GBC).Methods:The multi-center real-world study was conducted. The clinicopathological data of 629 patients with GBC, who were admitted to 14 medical centers including The First Affiliated Hospital of Army Medical University from the national multicenter database of Biliary Surgery Group of Elite Group of Chinese Journal of Digestive Surgery, from April 2020 to April 2024 were collected. There were 225 males and 404 females, aged (64±10)years. Patients underwent open curative-intent resection for GBC. Observation indicators: (1)surgery, postoperative complica-tions and adverse outcomes; (2) analysis of risk factors affecting postoperative adverse outcomes in patients and population attributable fraction (PAF). Missing data in predictor variables were addressed using multiple imputation with chained equations, while cases with missing outcome variables were addressed using the "multiple imputation then deletion (MID)" strategy. The severity of multicollinearity among independent variables was assessed using the variance inflation factor (VIF) test. Multivariable possion regression models with log link and robust error variance were construc-ted incorporating restricted cubic splines (3 knots) to address nonlinear relationships in continuous variables, calculating adjusted relative risk ( RR) with corresponding 95% confidence interval ( CI). Adjusted PAF was calculated for each imputed dataset using the AF package of R software, with subsequent pooling performed according to Rubin's rules. Results:(1) Surgery, postoperative complications and adverse outcomes. All 629 patients underwent curative-intent resection for GBC, of which 143 cases had postoperative complications, including 68 cases of intra-abdominal ascites, 39 cases of pulmonary infection, 21 cases of bile leakage, 12 cases of intra-abdominal hemorrhage, 11 cases of liver failure, 10 cases of pan-creatic fistula, 10 cases of wound infection, 10 cases of gastroparesis, 7 cases of cholangitis, 7 cases of sepsis. The same patient could have more than one kind of complication. Of 629 patients, there were 19 cases of postoperative 90-day death and 11 cases of missing data, 42 cases with post-operative 90-day reoperation and 7 cases with missing data, 44 cases with postoperative 90-day readmission and 3 cases with missing data, 155 cases with prolonged postoperative hospital stay and 3 cases with missing data. (2) Analysis of risk factors affecting the postoperative adverse outcomes in patients and PAF. Results of multivariate analysis showed that pulmonary infection and liver failure were independent risk factors for postoperative 90-day mortality ( RR=3.74, 12.15, 95% CI as 1.18-11.83, 1.98-74.48, P<0.05). Pulmonary infection demons-trated the highest PAF as 4.61% (95% CI as 3.94%-5.28%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, and intra-abdominal hemorrhage were independent risk factors for post-operative 90-day reoperation ( RR=4.80, 3.62, 3.46, 4.99, 95% CI as 2.49-9.26, 1.42-9.21, 1.34-8.92, 1.55-16.06, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 8.65% (95% CI as 8.22%-9.08%, P<0.05). Intra-abdominal ascites, bile leakage, and liver failure were independent risk factors for postoperative 90-day readmission ( RR=6.20, 3.33, 14.33, 95% CI as 3.21-11.95, 1.33-8.35, 3.72-55.28, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 9.11% (95% CI as 8.85%-9.37%, P<0.05). Intra-abdominal ascites, pulmonary infection, bile leakage, liver failure, and wound infection were independent risk factors for prolonged postoperative hospital stay ( RR=2.29, 2.21, 2.26, 2.14, 3.35, 95% CI as 1.63-3.23, 1.41-3.46, 1.32-3.86, 1.11-4.13, 1.70-6.60, P<0.05). Intra-abdominal ascites demonstrated the highest PAF as 6.03% (95% CI as 5.71%-6.35%, P<0.05). Conclusion:Pulmonary infection is the most significant risk factor for postoperative 90-day mortality after curative-intent resection for GBC, while intra-abdominal ascites is the most significant risk factor for postoperative 90-day reoperation, postoperative 90-day readmission, and prolonged postoperative hospital stay.

Objective To investigate the effect of dexmedetomidine(Dex)on bone loss in tail-suspended rats and primarily explore its regulatory mechanism on bone metabolism.Methods A total of 30 male rats were randomly divided into a control group,a model group,and a Dex group,with 10 animals in each group.Rat model of osteoporosis was established by hind limb suspension for 4 weeks.Dex at a dose of 10 μg/kg was given intraperitoneally,once every other day from the day of tail suspension.And equal amount of normal saline was given to the control and model group.Bone histological staining was used to observe the trabecular bone area fraction.Biomechanical three-point bending test was employed to measure the maximum load,stiffness,and fracture energy.Dual calcein/alizarin red fluorescence labeling and tartrate resistant acid phosphatase(TRAP)staining were applied respectively to detect the mineral apposition rate and bone formation rate as well as the number of osteoclasts on bone surfaces.Secondly,after primary osteoblasts were isolated from the tibiae of tail-suspended rats and then treated with 1 nmol/L Dex,the proportion of alkaline phosphatase(ALP)-positive osteoblasts and the activity of the enzyme were detected by ALP staining and activity test.qRT-PCR was applied to measure the expression of osteogenic activity-related factors,including osteocalcin(Ocn),Runt related transcription factor 2(Runx2),Osterix protein(Osx),and type 1 collagen(Col1).Results The animal experiments revealed that Dex treatment significantly increased the tibial trabecular bone area fraction,inhibited the decrease in bone mechanical strength,and enhanced the mineralization deposition rate and new bone formation rate of trabecular bone in the tail-suspended rats(all P＜0.001).The in vitro experiments showed that Dex treatment obviously improved ALP activity and the number of ALP-positive osteoblasts in primary osteoblasts isolated from tail-suspended rats(P＜0.01),and up-regulated the expression levels of osteogenic differentiation-related genes,such as Ocn,Runx2,Osx and Col1(P＜0.01).Conclusion Dex exerts anti-bone loss effect in tail-suspended rats,which may be associated with its stimulation on osteoblast-mediated bone formation.