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 investigate the causes and management strategies for lower limb ischemic necrosis following xenogeneic heterotopic heart transplantation from a multigene-edited pig to a rhesus monkey. Methods A xenogeneic heterotopic heart transplantation was performed on December 16, 2023, at the Institute of Experimental Animals of Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, using a quintuple-gene-edited pig as the donor and a rhesus monkey as the recipient. On postoperative day (POD) 9, the recipient monkey underwent left lower limb amputation due to ischemic necrosis. Blood samples were collected at various time points after transplantation for analysis of hematologic parameters, liver and renal function, myocardial enzymes, and coagulation profiles. Ultrasound and computed tomography (CT) were used to evaluate anastomotic patency and cardiac structure. Immunological assays, including complement-dependent cytotoxicity (CDC) and IgG/IgM antibody detection, combined with clinical observations, were employed to assess rejection type and therapeutic response. Results The recipient monkey survived for 46 days after transplantation. Echocardiography demonstrated preserved biventricular systolic function in the recipient’s native heart, with left ventricular ejection fraction (LVEF) consistently exceeding 50%. In the donor pig heart, left ventricular endocardial thickening was noted on POD 9, followed by right ventricular endocardial thickening on POD 24, while LVEF remained around 35%. No hyperacute or acute rejection was detected immunologically. CDC positivity ranged between 3.4% and 5.1%, with IgG/IgM antibody binding trends consistent with CDC results. Following amputation, the recipient exhibited elevated inflammatory markers, coagulopathy, and reactive thrombocytosis, which later normalized. Immunohistochemical staining of the necrotic limb revealed arterial and venous thrombosis; however, no T-cell or B-cell infiltration was observed in vascular structures, thrombi, nerves, muscles, fascia, or skin tissues, with CD3 and CD20 staining both negative. Conclusion Limb ischemia after xenogeneic heart transplantation may be associated with lower extremity vascular thrombosis triggered by local trauma in the context of transplantation-induced inflammatory activation and coagulation dysfunction. While no clear lymphocyte-mediated rejection was observed, further studies are needed to explore the potential role of non-lymphocyte-mediated immune mechanisms.

Huangqi Guizhi Wuwutang was first documented in ZHANG Zhongjing's Synopsis of the Golden Chamber in the Eastern Han Dynasty as the principal formula for treating blood obstruction syndrome. It is composed of Astragali Radix， Cinnamomi Ramulus， Paeoniae Radix Alba， Zingiberis Rhizoma Recens， and Jujubae Fructus. This formula exhibits the effects of replenishing Qi， warming meridians， harmonizing blood， and relieving obstruction， being primarily indicated for blood obstruction syndrome characterized by diminished Yin and Yang functions with external manifestations of bodily numbness. Traditional Chinese medicine （TCM） emphasizes strict adherence to pathogenesis while applying tailored therapies， employing consistent treatment approaches for conditions with diverse clinical presentations but shared pathogenesis， which embodies the principle of treating different diseases with the same method. In modern clinical practice， Huangqi Guizhi Wuwutang is widely applied in multisystem disorders presenting with symptoms such as cutaneous numbness， cold-induced pain and stiffness， and soreness and weakness， including diabetic peripheral neuropathy， cervical radiculopathy， rheumatoid arthritis， coronary heart disease， pediatric spastic cerebral palsy， and recovery phases of cerebral infarction， demonstrating proven efficacy. Modern pharmacological studies confirm its anti-inflammatory， antioxidant-stress， hemorheological-improving， immunomodulatory， and neuroprotective effects by regulating multiple signaling pathways such as nuclear factor （NF）-κB/NOD-like receptor protein 3 （NLRP3） and nuclear factor erythroid 2-related factor 2 （Nrf2）-antioxidant response element （ARE）， thereby addressing common pathological processes including inflammation， hypercoagulability， oxidative stress， immune dysregulation， and neural injury， highlighting the integrated regulatory advantages of TCM formulas with multi-target， multi-level， multi-component， and multi-pathway mechanisms. According to the theory of treating different diseases with the same method and focusing on three core syndromes of blood obstruction， this article systematically reviews and elucidates the clinical extensions and shared mechanisms of this formula， aiming to provide evidence-based insights for the modern adaptation and precise application of this classical formula in orthopedic and related disciplines for chronic musculoskeletal disorders.

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 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.

Objective To investigate the therapeutic efficacy of artesunate(AS)on polycystic ovary syndrome(PCOS)in mice and explore the potential mechanism primarily.Methods Twenty-five female C57BL/6J mice were randomly divided into Control group,model group(PCOS group),low-and high-dose AS groups(AS15 and AS30 groups)and metformin group(Met group).In addition to the Control group,the mouse model of PCOS was established by subcutaneous injection of dehydroepiandrosterone(DHEA,60 mg/kg)following by a high-fat diet for 21 d.After modeling,AS of 15 and 30 mg/kg was intraperitoneally injected into the mice of the AS 15 and AS30 groups,respectively,and 200 mg/kg Met was given to those of the Met group by gavage,once per day,for 6 weeks.ELISA was used to detect serum testosterone(T),fasting insulin(FINS),luteinizing hormone(LH)and follicle-stimulating hormone(FSH),and the LH/FSH ratio was calculated.The levels of fasting blood glucose(FBG),triglyceride(TG)and total cholesterol(TC)were detected by automatic biochemical analyzer,and the homeostasis model assessment of insulin resistance(HOMA-IR)was calculated.The estrous cycle was observed,and HE staining was performed for pathological changes in the ovary and uterus.Immunofluorescence assay was employed to measure the expression of p-eIF2α,ATF4 and CHOP in the ovarian tissue.After steroidogenic human granulosa-like tumor cell line KGN were exposed to 100 μmol/L DHEA to simulate the hyperandrogen environment of PCOS,and then treated with 5 and 10 μg/mL AS for 24 h,the protein levels of endoplasmic reticulum stress signaling pathway was detected by Western blotting.Results Compared with the Control group,the PCOS mice had disturbed estrous cycle,polycystic changes in the ovaries,and significantly increased serum T level and LH/FSH ratio(P＜0.05),and obviously elevated HOMA-IR,TC and TG levels in terms of metabolism(P＜0.01).The expression levels of p-eIF2α,ATF4 and CHOP were notably up-regulated in the ovarian granulosa cells of PCOS mice and KGN cells after DHEA exposure(P＜0.05).Additionally,AS treatment attenuated the pathological changes of ovary and uterine expression,decreased the serum T level and the LH/FSH ratio(P＜0.05),and reduced HOMA-IR,TC and TG levels(P＜0.05)when compared with the PCOS mice.Moreover,the expression levels of p-eIF2α,ATF4 and CHOP were significantly down-regulated after AS treatment in both ovarian granulosa cells of PCOS mice and KGN cells(P＜0.05).Conclusion AS significantly improves glycolipid metabolic disorder and reproductive dysfunction in PCOS mice,which may be associated with its suppressing endoplasmic reticulum stress by inhibiting the PERK/eIF2α/ATF4/CHOP pathway.

Objective To compare the midterm outcomes of the Bentall procedure versus isolated aortic valve replacement(AVR)in patients with bicuspid aortic valve(BAV)complicated with severe stenosis and ascending aortic dilation in order to assess the therapeutic value of these surgical approaches for this complex cardiac condition.Methods A retrospective cohort study was conducted on 96 eligible patients who underwent surgical treatment in our institute between January 2018 and December 2022.According to surgical approaches,they were divided into an AVR group(65 cases)and a Bentall group(31 cases).Demographic features,comorbidities,preoperative status,and echocardiographic parameters were collected in all patients.Propensity score matching(PSM)was applied in a 1:1 ratio to balance baseline characteristics.Perioperative indicators and follow-up data were compared and analyzed between matched cohorts after control of cofounding factors.Results After PSM,25 matched pairs were screened out and analyzed with comparable baseline data(all P>0.05).The Bentall group demonstrated significantly more superior intraoperative effective orifice area(EOA,2.69±0.47 vs 2.35±0.47 cm2,P=0.013)and EOA index(EOAI,1.69±0.30 vs 1.47±0.29 cm2/m2,P=0.010),and obviously longer cardiopulmonary bypass time[190.00(147.00,257.00)vs 101.00(88.50,124.50)min,P<0.01]and aortic cross-clamp time[141.00(120.00,166.00)vs 66.00(55.00,81.50)min,P<0.01]when compared with the AVR group.During a median follow-up of 28 months,the AVR group had notably larger aortic sinus diameter[32.00(30.00,34.00)vs 26.80(26.00,28.00)mm,P<0.01]and ascending aortic diameter[38.00(34.50,42.00)mm vs 26.00(26.00,28.00)mm,P<0.01],with ongoing dilation in the ascending aorta,while the Bentall group maintained stable dimensions.The Bentall group also showed statistically lower peak aortic valve pressure gradients[21.00(15.50,27.00)vs 25.00(19.50,31.00)mmHg,P=0.049].Conclusion Both Bentall procedure and AVR are effective in treatment of BAV complicated with severe stenosis and ascending aortic dilation.But,Bentall procedure offers advantages in hemodynamic optimization and aortic stability.

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.