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 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.

ObjectiveTo investigate the neuroprotective effect of glycosides of cistanches （GCs） in 1-methyl-4-phenyl-1，2，3，6-tetrahydropyridine （MPTP）-induced Parkinson’s disease （PD） model mice and its potential mechanism. MethodsForty mice were randomly divided into control group， PD group， GCs group and levodopa （L-DOPA） group， with 10 mice in each group. The control group received intraperitoneal injection of normal saline， while the other groups were intraperitoneally injected with MPTP （30 mg/kg） once daily for 5 consecutive days. After model establishment， the control group and PD group were intragastrically administered normal saline， and the GCs group and L-DOPA group received intragastric administration once daily for 28 consecutive days， at doses of 100 mg/kg and 50 mg/kg， respectively. The open field test and rotarod test were used to evaluate motor function. Immunohistochemistry was performed to detect the number of tyrosine hydroxylase （TH）-positive neurons in the substantia nigra of the midbrain. Transmission electron microscopy was adopted to observe the ultrastructure of mitochondria in the substantia nigra. Biochemical assays were used to determine the levels of glutathione （GSH）， malondialdehyde （MDA）， total superoxide dismutase （T-SOD） and reactive oxygen species （ROS） in substantia nigra tissues. Western blot was applied to detect the protein expression of TH， nuclear factor erythroid 2-related factor 2 （Nrf2）， Kelch-like ECH-associated protein 1 （Keap-1） and heme oxygenase-1 （HO-1） in substantia nigra tissues. ResultsCompared with the control group， mice in the PD group exhibited markedly impaired motor function， significantly decreased T-SOD activity， obvious reductions in the number of TH-positive neurons and GSH content， as well as elevated MDA and ROS levels in the substantia nigra （P<0.01）. Compared with the PD group， mice in the GCs group showed improved motor function， increased T-SOD activity， higher number of TH-positive neurons and elevated GSH content， accompanied by decreased MDA and ROS levels （P<0.05）. In the control group， mitochondria in the substantia nigra displayed normal morphology without obvious blurred membrane structures or vacuolar degeneration. The PD group presented mitochondrial swelling， disordered cristae and vacuolar changes， whereas these mitochondrial pathological alterations were alleviated in the GCs group. Compared with the control group， the protein levels of TH， Nrf2 and HO-1 were significantly downregulated， while Keap-1 protein expression was markedly upregulated in the PD group （P<0.01）. Compared with the PD group， the GCs group showed significant upregulation of TH， Nrf2 and HO-1 protein levels and a notable reduction in Keap-1 protein level （P<0.05）. ConclusionGCs ameliorate motor dysfunction and attenuate dopaminergic neuronal and mitochondrial damage in MPTP-induced PD model mice. These effects may be associated with modulation of Nrf2/HO-1 pathway-related protein expression， enhancement of antioxidant capacity， and alleviation of oxidative stress.

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.

Intelligent prescription recommendation has become an important research direction in traditional Chinese medicine (TCM), offering new opportunities to support clinical decision-making and promote the modernization of TCM practice. With the rapid development of artificial intelligence (AI), a variety of computational approaches have been proposed to learn prescription patterns from clinical data and generate personalized treatment recommendations. However, despite increasing research activity, systematic and comprehensive reviews of AI-driven methods for TCM prescription recommendation remain limited. In this study, we present a comprehensive review of computational approaches for herbal prescription recommendation (HPR) in TCM. Existing methods are systematically categorized into several major paradigms, including traditional machine learning methods, topic model methods, sequential generative methods, deep learning and graph-based methods, and large language model–based frameworks. In addition to summarizing methodological developments, we also review commonly used public datasets and evaluation metrics in this field. Furthermore, representative models with publicly available implementations are experimentally evaluated on multiple benchmark datasets to provide a comparative analysis of their performance on the HPR task. Finally, we discuss the key challenges that hinder the practical deployment of intelligent prescription recommendation systems, including data heterogeneity, limited interpretability, and insufficient integration of TCM domain knowledge. Future research directions are outlined to facilitate the development of more reliable, interpretable, and clinically applicable AI-assisted HPR systems for TCM.

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 investigate the prognostic value of aldosterone synthase (CYP11B2) immunohistochemical expression in adrenal specimens for surgical outcomes of primary aldosteronism (PA).Methods:The clinical data of 99 patients who underwent total unilateral adrenalectomy from June 2022 to January 2023 at Beijing Anzhen Hospital was retrospectively analysed. The clinical data of 99 patients who underwent unilateral total adrenalectomy at Beijing Anzhen Hospital from June 2022 to January 2023 were retrospectively analyzed.There were 59 patients in the PA group, age (53.02±10.56) years, body mass index (BMI) (26.28±4.33) kg/m 2, preoperative aldosterone 29.0(15.9, 61.5)ng/dl, plasma renin 1.3(0.6, 2.8)μIU/ml, aldosterone renin ratio (ARR) 19.3(9.1, 59.2) μg/μIU, preoperative potassium (3.60±0.69) mmol/L, and systolic blood pressure (156.54±21.39) mmHg (1 mmHg=0.133 kPa).There were 40 cases in the nonfunctioning adenoma (NFA) group, age (57.23±9.39) years, BMI (27.07±3.46) kg/m 2, preoperative aldosterone 9.0(7.2, 14.1) ng/dl, plasma renin 18.0(5.2, 47.6)μIU/ml, ARR 0.6(0.2, 1.4) μg/μIU, preoperative potassium (4.17±0.41) mmol/L, and systolic blood pressure (157.97±26.87) mmHg. The differences between the two groups were statistically significant for potassium ( P<0.01), aldosterone ( P=0.012), renin ( P<0.01), and ARR ( P<0.01).Surgical outcomes were assessed using the Consensus on the Outcome of Surgery for Primary Aldosteronism (PASO) (complete/partial/no success for clinical and biochemical outcomes). CYP11B2 expression was evaluated by immunohistochemistry using the 2022 World Health Organization's histopathology of primary aldosteronism (HISTALDO) criteria. The correlation between the expression of CYP11B2 and surgical outcomes was assessed. Results:The mean follow-up of 99 patients was (11.73±4.92) months. Of these, 36 out of 59 PA patients had positive CYP11B2 expression in their adrenal specimens, while 23 were negative; all 40 NFA patients were negative for CYP11B2. Among the 36 CYP11B2-positive PA patients, there were 19 cases of aldosterone-producing adenomas, 3 aldosterone-producing nodules, 4 aldosterone-producing micronodules, 8 multiple aldosterone-producing micronodules, and 2 aldosterone-producing diffuse hyperplasia. 36 cases of CYP11B2-positive PA patients had complete clinical success in 15 cases, partial success in 20 cases, and no success in 1 case, and complete biochemical success in 24 cases, partial success in 11 cases, and no success in 1 case; 23 CYP11B2-negative PA patients had complete clinical success in 4 cases, partial success in 15 cases, and no success in 4 cases, and complete biochemical success in 6 cases, partial success in 15 cases, and no success in 2 cases. Adrenal specimens from CYP11B2-positive PA patients had significantly better clinical ( P=0.038) and biochemical ( P=0.008) success rates than CYP11B2-negative PA patients. Patients with aldosterone-producing adenomas had complete clinical success in 8 cases, partial success in 11 cases, and no success in 0 cases, and biochemical success was completely achieved in 16 cases, partially achieved in 2 cases, and not successful in 1 case. They also had significantly higher clinical ( P=0.028) and biochemical ( P<0.01) success rates compared to CYP11B2-negative PA patients. Conclusions:Patients with PA who had immunohistochemical staining for CYP11B2 positivity and high expression in adrenal specimens had a better postoperative clinical and biochemical prognosis. Patients with aldosterone-producing adenomas had the greatest postoperative outcome of all pathological subtypes of PA.

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.

Forced oscillation technique (FOT) enables early diagnosis of chronic obstructive pulmonary disease (COPD) by quantifying the impedance of COPD patients during normal breathing and reflecting the airway obstruction and distribution of the patients. The flow sensors using in FOT for diagnosis of COPD mainly include differential pressure flow sensors, ultrasonic flow sensors, hot wire gas flow sensors, fiber-optic flow sensors and flow sensors based on friction nanoelectricity technology. In this review, the principles, characteristics, and current application status of these five types of flow sensors were summarized, and their future development prospects were prospected.