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 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.

ObjectiveTo analyze the distribution characteristics of traditional Chinese medicine （TCM） syndrome elements in different risk populations of heart failure complicated with type 2 diabetes. MethodsClinical data of 675 type 2 diabetes patients were retrospectively collected. Lasso-multivariate Logistic regression was used to construct a clinical prediction nomogram model. Based on this， 441 non-heart failure patients were divided into a low-risk group （325 cases） and a high-risk group （116 cases） according to the median risk score of heart failure complicated with type 2 diabetes. TCM diagnostic information （four diagnostic methods） was collected for both groups， and factor analysis was applied to summarize the distribution of TCM syndrome elements in different risk populations. ResultsLasso-multivariate Logistic regression analysis identified age， disease duration， coronary heart disease， old myocardial infarction， arrhythmia， absolute neutrophil count， activated partial thromboplastin time， and α-hydroxybutyrate dehydrogenase as independent risk factors for heart failure complicated with type 2 diabetes. These were used as final predictive factors to construct the nomogram model. Model validation results showed that the area under the curve （AUC） of the receiver operating characteristic （ROC） curve for the modeling group and validation group were 0.934 and 0.935， respectively. The Hosmer-Lemeshow test （modeling group P = 0.996， validation group P = 0.121） indicated good model discrimination. Decision curve analysis showed that the curves for All and None crossed in the upper right corner， indicating high clinical utility. The low-risk and high-risk groups each obtained 14 common factors. Preliminary analysis revealed that the main disease elements in the low-risk group were qi deficiency （175 cases， 53.85%）， dampness （118 cases， 36.31%）， and heat （118 cases， 36.31%）， with the primary locations in the spleen （125 cases， 38.46%） and lungs （99 cases， 30.46%）. In the high-risk group， the main disease elements were yang deficiency （73 cases， 62.93%）， blood stasis （68 cases， 58.62%）， and heat （49 cases， 42.24%）， with the primary locations in the kidney （84 cases， 72.41%） and heart （70 cases， 60.34%）. ConclusionThe overall disease characteristics in different risk populations of type 2 diabetes patients with heart failure are a combination of deficiency and excess， with deficiency being predominant. Deficiency and heat are present throughout. The low-risk population mainly shows qi deficiency with dampness and heat， related to the spleen and lungs. The high-risk population shows yang deficiency with blood stasis and heat， related to the kidneys and heart.

ChuanWu (CW), the dried mother root of Aconitum carmichaelii Debx., is a well-known traditional Chinese medicine (TCM) recognized for its potent efficacy but inherent toxicity, primarily due to its alkaloid content. Traditional and modern detoxification methods for CW include proper processing, rational compatibility, and specialized decoction techniques, among which honey-boiled CW is particularly distinctive. However, research on the detoxification mechanism of honey-boiled CW remains limited. This study investigated this mechanism by analyzing alkaloid transformation and supramolecular aggregation. Honey-boiled and water-boiled CW preparations were compared. Ultra-high-performance liquid chromatography-tandem mass spectrometry was used to analyze CW alkaloids, specifically diester alkaloids (DDAs), monoester alkaloids (MDAs), and non-esterified diterpenoid alkaloids (NDAs). Transmission electron microscopy was employed to observe and identify supramolecular aggregates in the honey-boiled CW decoction. In vivo absorption of water-boiled, honey-boiled, and NADES-boiled CW was compared. Median lethal dose (LD₅₀) tests assessed toxicity, including hepatotoxicity and nephrotoxicity. In vitro experiments evaluated the safety, anti-inflammatory, and analgesic effects of CW-medicated serum on RAW264.7 cells, with in vivo validation in mice. Results showed that honey promoted the conversion of highly toxic DDAs to less toxic MDAs and prevented MDAs from hydrolyzing into NDAs. Honey-boiled CW formed approximately 250 nm supramolecular aggregates that encapsulated MDAs, inhibiting their conversion to NDAs. These encapsulated MDAs acted as a stable delivery system with higher bioavailability than free benzoylmesaconine. Subsequent mouse experiments confirmed that honey-boiled CW significantly increased the LD₅₀ of CW while reducing hepatotoxicity and nephrotoxicity. Additionally, honey-boiled CW significantly improved cell safety and enhanced anti-inflammatory and analgesic effects. Our findings reveal that honey-boiled CW exhibits a potent detoxification mechanism by influencing alkaloid transformation and facilitating the formation of supramolecular aggregates. This study lays the groundwork for developing detoxification or synergistic strategies within honey-boiled TCM.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Objective:To investigate the factors influencing the prognosis of critically ill patients and the predictive value of the C-reactive protein-albumin-lymphocyte (CALLY) index.Methods:A retrospective analysis was conducted on the clinical data of 122 critically ill patients admitted to Guoyao Dongfeng General Hospital affiliated with Hubei University of Medicine from June 2022 to December 2023. Patients were divided into a death group and a survival group based on their 28-day prognosis. Clinical data were compared between the two groups to analyze the factors influencing prognosis and assess the predictive value of various indicators. Normally distributed measurement data were expressed as Mean±SD, and intergroup comparisons were performed by independent samples t-test; non-normally distributed measurement data were expressed as M( Q1,Q3), and intergroup comparisons were performed by the Mann-Whitney U test. Counting data were expressed as case (%), and intergroup comparisons were performed by the χ2 test. Multivariate logistic regression was used to analyze factors influencing patient prognosis, and receiver operating characteristic (ROC) curves were plotted to analyze the predictive value of each indicator. Results:The Acute Physiology and Chronic Health Evaluation Ⅱ (APACHE Ⅱ) score, blood lactate, CRP, and B-type natriuretic peptide levels in the death group were higher than those in the survival group [22 (17, 30) points vs. 17 (14, 22) points, (4.8±1.4) mmol/L vs. (3.3±1.0) mmol/L, 134 (83, 2 381) mg/L vs. 13 (10, 27) mg/L, 259 (111, 592) ng/L vs. 108 (40, 247) ng/L; Z=3.04, P=0.002; t=5.79, P<0.001; Z=8.57, P<0.001; Z=3.28, P=0.001, respectively]. Albumin, neutrophil count, lymphocyte count (LYC), and the CALLY index were lower in the death group than in the survival group [(31±5) g/L vs. (37±6) g/L, (58±9)×10 9/L vs. (63±10)×10 9/L, 0.6 (0.4, 0.8)×10 9/L vs. 1.3 (0.8, 1.7)×10 9/L, 0.03 (0.02, 0.11) vs. 0.26 (0.13, 0.49); t=6.05, P<0.001; t=3.04, P=0.003; Z=5.82, P<0.001; Z=6.52, P<0.001, respectively]. Multivariate logistic regression analysis indicated that the APACHE Ⅱ score and CRP were risk factors for poor prognosis in critically ill patients ( OR=1.349, 95% CI: 1.004-1.821, P=0.048; OR=1.006, 95% CI: 1.003-1.010, P=0.001, respectively), while LYC and the CALLY index were protective factors ( OR=0.297, 95% CI: 0.111-0.795, P=0.016; OR=0.989, 95% CI: 0.955-0.999, P=0.001, respectively). The area under the ROC curve for the CALLY index predicting 28-day mortality in critically ill patients was 0.872 (95% CI: 0.800-0.926), which was higher than that of the APACHE Ⅱ score, LYC, and CRP [0.673 (95% CI: 0.582-0.756), 0.664 (95% CI: 0.573-0.748), 0.576 (95% CI: 0.482-0.665), respectively]. The cut-off values were 0.06, 20 points, 0.8×10 9/L, and 50 mg/L, respectively. When the CALLY index was 0.06, the specificity was 97.65%, the sensitivity was 72.97%, and the Youden index was 0.706. Conclusions:The APACHE Ⅱ score, CRP, LYC, and CALLY index are all factors influencing the prognosis of critically ill patients. The CALLY index has certain predictive value, but its false negative rate is relatively high. Further combination with other indicators is needed to improve its predictive value.