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 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 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 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.

In recent years, a variety of innovative fast algorithm techniques have emerged in the field of transcranial magnetic stimulation (TMS) electric field solving, which show great potential to meet the requirements of real-time clinical applications. In this review, the crucial processes of TMS electric field modeling were summarized, focusing on two prominent fast algorithm techniques, namely the basis function method and the deep neural network (DNN)-based method. The advantages and limitations of these two techniques were analyzed in detail. Commonly used software tools for electric field modeling were discussed, and a prospective discussion of future developments was offered, aiming to provide a reference for the further development of TMS electric field modeling technology.

Alzheimer′s disease (AD) is a degenerative disorder of the central nervous system. In the early stages of AD, i.e. when cognitive impairment is mild or absent but biomarkers are present, patients show abnormal neuroelectric activity. Repetitive transcranial magnetic stimulation (rTMS) is an important method of non-invasively regulating neuroelectric activity in the brain. It does so by inducing microcurrents to change the membrane potential of nerve cells in the brain based on electromagnetic induction. In this review, the characteristics of neuroelectric activity of AD patients and AD model mice in the early stage of AD were mainly introduced, and the regulation of rTMS on the neuroelectric activity of early AD was discussed.

Objective:To explore the diagnosis and treatment strategy of emphysematous pyelonephritis (EPN).Methods:The clinical data of 11 cases patients with EPN admitted to 3 hospitals from March 2016 to June 2022 were retrospectively analyzed, among them, 4 cases from Qianhai Shekou Free Trade Zone Hospital, 5 cases from the Second Hospital Affiliated to Kunming Medical University, 2 cases from the Second Affiliated Hospital of Hubei University of Scinece and Technology. Among the 11 patients, 2 were males and 9 were females, aged 50-82 years; the lesions were located on the left side in 6 cases, right side in 4 cases and bilateral in 1 case; all patients had type 2 diabetes and poor glycemic control. The clinical manifestations at admission including back pain in 8 cases, fever in 11 cases, nausea and vomiting in 5 cases, disturbance of consciousness in 3 cases, septic shock in 3 cases, accompanied with ureteral or kidney stones in 5 cases. The pathogenic bacteria were Escherichia coli in 8 cases, Klebsiella pneumoniae in 2 cases and Proteus mirabilis in 1 case. All patients received minimally invasive surgery, anti-infection, subcutaneous injection of insulin, fluid rehydration and nutritional support after admission. 2 cases had a combination of an initial ureteral stenting and second stage percutaneous drainage, 3 patients underwent ureteral stent implantation, 6 patients underwent percutaneous drainage. According to the CT classification of EPN, there were 1 case of type Ⅰ, 3 cases of type Ⅱ, 2 cases of type ⅢA, 4 cases of type ⅢB, and 1 case of type Ⅳ. Results:All 11 cases were cured, 4 cases were admitted to intensive care unit for 2-7 days, 1 case underwent nephrectomy during hospitalization, and 1 case underwent nephrectomy due to renal atrophy during follow-up. After 12 to 18 months of follow-up with urinary CT or B-ultrasound, there were no recurrence cases.Conclusions:EPN is a rare and serious renal parenchymal necrotic infection. Early urinary CT examination is necessary for the diagnosis, and positive minimally invasive surgery combined with comprehensive medical treatment is the preferred treatment strategy. If those above treatment does not work, nephrectomy should be performed.