OBJECTIVE:Postoperative recurrence is a common complication of percutaneous endoscopic lumbar discectomy for lumbar disc herniation,which can significantly increase the risk of reoperation.A well-performing risk prediction model can help identify high-risk groups early and prevent postoperative recurrence.This study systematically evaluated the risk prediction model for postoperative recurrence after percutaneous endoscopic lumbar discectomy to provide a reference for surgical decision-making.METHODS:The PubMed,Embase,Web of Science,CNKI,WanFang Data,VIP,and CBM were electronically searched to collect studies on the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy from inception to July 1,2024.Two reviewers independently screened the literature and extracted data.The models' risk of bias,applicability,and report quality were assessed using prediction model risk of bias assessment tool(PROBAST)and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis(TRIPOD)tools,respectively.Meta-analysis of postoperative recurrence rate of percutaneous endoscopic lumbar discectomy and related predictors was performed using Revman 5.4 software.RESULTS:(1)A total of 15 studies were included,all of which were retrospective studies,including 24 models for predicting the risk of recurrence after percutaneous endoscopic lumbar discectomy.(2)The PROBAST evaluation results indicated that all 15 studies exhibited a high risk of bias.Regarding applicability,two studies demonstrated a low risk,while 13 presented a high risk.(3)Regarding the TRIPOD reporting quality,the overall quality across the 15 studies was low.The primary reasons for this low compliance included the failure to report blinding,a lack of explanation for the sample size calculation method,lack of detailed description of missing data processing methods,and lack of information such as introduction to the model used.(4)Furthermore,the area under the receiver operating characteristic curve for the model ranged from 0.684 to 0.972,with the number of potential predictor variables varying from 15 to 28.(5)The results of meta-analysis showed that the postoperative recurrence rate of lumbar disc herniation patients treated with percutaneous endoscopic lumbar discectomy was 12％(95％CI=9.0％-15.0％),Modic changes(OR=6.72,95％CI=3.90-11.59),body mass index(OR=1.28,95％CI=1.10-1.49),work intensity(OR=3.22,95％CI=1.85-5.59),age(OR=2.28,95％CI=1.50-3.48),and smoking history(OR=2.65,95％CI=1.75-4.00)were independent influencing factors for postoperative recurrence of percutaneous endoscopic lumbar discectomy(all P＜0.05).CONCLUSION:The overall predictive performance of the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy is satisfactory;however,the model exhibits a high overall risk of bias and applicability,coupled with low reporting quality.Additionally,there is a lack of prospective research and external validation.Future,risk prediction models should consider factors such as Modic changes,body mass index,work intensity,age,and smoking history as potential predictors.

OBJECTIVE:Postoperative recurrence is a common complication of percutaneous endoscopic lumbar discectomy for lumbar disc herniation,which can significantly increase the risk of reoperation.A well-performing risk prediction model can help identify high-risk groups early and prevent postoperative recurrence.This study systematically evaluated the risk prediction model for postoperative recurrence after percutaneous endoscopic lumbar discectomy to provide a reference for surgical decision-making.METHODS:The PubMed,Embase,Web of Science,CNKI,WanFang Data,VIP,and CBM were electronically searched to collect studies on the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy from inception to July 1,2024.Two reviewers independently screened the literature and extracted data.The models' risk of bias,applicability,and report quality were assessed using prediction model risk of bias assessment tool(PROBAST)and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis(TRIPOD)tools,respectively.Meta-analysis of postoperative recurrence rate of percutaneous endoscopic lumbar discectomy and related predictors was performed using Revman 5.4 software.RESULTS:(1)A total of 15 studies were included,all of which were retrospective studies,including 24 models for predicting the risk of recurrence after percutaneous endoscopic lumbar discectomy.(2)The PROBAST evaluation results indicated that all 15 studies exhibited a high risk of bias.Regarding applicability,two studies demonstrated a low risk,while 13 presented a high risk.(3)Regarding the TRIPOD reporting quality,the overall quality across the 15 studies was low.The primary reasons for this low compliance included the failure to report blinding,a lack of explanation for the sample size calculation method,lack of detailed description of missing data processing methods,and lack of information such as introduction to the model used.(4)Furthermore,the area under the receiver operating characteristic curve for the model ranged from 0.684 to 0.972,with the number of potential predictor variables varying from 15 to 28.(5)The results of meta-analysis showed that the postoperative recurrence rate of lumbar disc herniation patients treated with percutaneous endoscopic lumbar discectomy was 12％(95％CI=9.0％-15.0％),Modic changes(OR=6.72,95％CI=3.90-11.59),body mass index(OR=1.28,95％CI=1.10-1.49),work intensity(OR=3.22,95％CI=1.85-5.59),age(OR=2.28,95％CI=1.50-3.48),and smoking history(OR=2.65,95％CI=1.75-4.00)were independent influencing factors for postoperative recurrence of percutaneous endoscopic lumbar discectomy(all P＜0.05).CONCLUSION:The overall predictive performance of the recurrence risk prediction models after percutaneous endoscopic lumbar discectomy is satisfactory;however,the model exhibits a high overall risk of bias and applicability,coupled with low reporting quality.Additionally,there is a lack of prospective research and external validation.Future,risk prediction models should consider factors such as Modic changes,body mass index,work intensity,age,and smoking history as potential predictors.

Objective To evaluate the changes of bronchial mucosa observed by fiberoptic bronchoscopy after bronchial arterial embolization(BAE)treatment.Methods A total of 176 patients,who received BAE at the First Affiliated Hospital of Guangzhou Medical University of China from May 2019 to March 2024,were enrolled in this study.The pre-BAE and post-BAE bronchial mucosa was checked by fiberoptic bronchoscopy.Results Of the 176 patients,fiberoptic bronchoscopy showed no abnormal findings in 143 and showed abnormal findings in 33.All the abnormal findings were mucosal congestion and oedema,in some cases coexisting vascular bulge was seen,but no manifestations of ischemia or necrosis of the bronchial mucosa could be found.In 22 patients,the preoperative and postoperative 7-day fiberoptic bronchoscopy revealed that both preoperative and postoperative examinations showed no obvious abnormalities of the bronchial mucosa in 13 patients,preoperative examination had abnormalities of the bronchial mucosa in 9 patients,postoperative examination showed no obvious abnormalities of the bronchial mucosa in 3 patients,and in one patient the postoperative degree of bronchial mucosal congestion and oedema was significantly improved when compared with its preoperative degree.Conclusion BAE does not cause ischemic necrosis or shedding of bronchial mucosa,and BAE can reduce the degree of bronchial mucosal congestion in some patients.

Objective To investigate the clinical features,etiological features and prognosis of patients with hematologic diseases complicated with candidemia for improving clinical diagnosis and treatment.Methods A retrospective analysis was conducted for 107 hematological patients complicated with candidemia who were treated in the First Affiliated Hospital of Zhengzhou University,Henan Cancer Hospital,Henan Provincial People's Hospital,or Zhengzhou People's Hospital from June 2022 to May 2024.The clinical data and pathogenic bacteria were analyzed by univariate and multivariate analyses.Results The Candida pathogen of the 107 cases of candidemia were mostly Candida tropicalis(73.8％),followed by Candida parapsilosis,Candida glabrata,and Candida albicans.Antifungal susceptibility testing showed that 43.9％,47.7％,and 48.6％of the Candida strains were resistant voriconazole,fluconazole and itraconazole,respectively.Logistic regression analysis found that disease not in remission(OR=7.795,95％CI:2.274-26.723),septic shock(OR=10.376,95％CI:1.129-95.388),multiple organ dysfunction syndrome(MODS)(OR=9.107,95％CI:1.789-46.361),and inappropriate antifungal treatment(OR=3.422,95％CI:1.153-10.153)were risk factors for 30-day mortality in hematological patients with candidemia.Conclusions Candidemia in patients with hematological diseases is associated with high mortality rate,the major pathogen of which is Candida tropicalis.The Candida isolates showed high resistance rates to azoles.Disease not in remission,septic shock,MODS,and inappropriate antifungal treatment are the risk factors for mortality.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Although metal blocks have been widely used for reconstructing uncontained tibial bone defects, the influence of their elastic modulus on the stability of tibial prosthesis fixation remains unclear. Based on this, a finite element model incorporating constrained condylar knee (CCK) prosthesis, tibia, and metal block was established. Considering the influence of the post-restraint structure of the prosthesis, the effects of variations in the elastic modulus of the block on the von Mises stress distribution in the tibia and the block, as well as on the micromotion at the bone-prosthesis fixation interface, were investigated. Results demonstrated that collision between the insert post and femoral prosthesis during tibial internal rotation increased tibial von Mises stress, significantly influencing the prediction of block elastic modulus variation. A decrease in the elastic modulus of the metal block resulted in increased von Mises stress in the proximal tibia, significantly reduced von Mises stress in the distal tibia, decreased von Mises stress of the block, and increased micromotion at the bone-prosthesis fixation interface. When the elastic modulus of the metal block fell below that of bone cement, inadequate block support substantially increased the risk of stress shielding in the distal tibia and fixation interface loosening. Therefore, this study recommends that biomechanical investigations of CCK prostheses must consider the post-constraint effect, and the elastic modulus of metal blocks for bone reconstruction should not be lower than 3 600 MPa.
Knee Prosthesis ; Humans ; Finite Element Analysis ; Tibia/surgery* ; Elastic Modulus ; Arthroplasty, Replacement, Knee/methods* ; Stress, Mechanical ; Metals ; Prosthesis Design ; Knee Joint/surgery* ; Biomechanical Phenomena

Objective To investigate the clinical features,etiological features and prognosis of patients with hematologic diseases complicated with candidemia for improving clinical diagnosis and treatment.Methods A retrospective analysis was conducted for 107 hematological patients complicated with candidemia who were treated in the First Affiliated Hospital of Zhengzhou University,Henan Cancer Hospital,Henan Provincial People's Hospital,or Zhengzhou People's Hospital from June 2022 to May 2024.The clinical data and pathogenic bacteria were analyzed by univariate and multivariate analyses.Results The Candida pathogen of the 107 cases of candidemia were mostly Candida tropicalis(73.8％),followed by Candida parapsilosis,Candida glabrata,and Candida albicans.Antifungal susceptibility testing showed that 43.9％,47.7％,and 48.6％of the Candida strains were resistant voriconazole,fluconazole and itraconazole,respectively.Logistic regression analysis found that disease not in remission(OR=7.795,95％CI:2.274-26.723),septic shock(OR=10.376,95％CI:1.129-95.388),multiple organ dysfunction syndrome(MODS)(OR=9.107,95％CI:1.789-46.361),and inappropriate antifungal treatment(OR=3.422,95％CI:1.153-10.153)were risk factors for 30-day mortality in hematological patients with candidemia.Conclusions Candidemia in patients with hematological diseases is associated with high mortality rate,the major pathogen of which is Candida tropicalis.The Candida isolates showed high resistance rates to azoles.Disease not in remission,septic shock,MODS,and inappropriate antifungal treatment are the risk factors for mortality.

OBJECTIVE To conduct a reevaluation of the systematic review(SR)/meta-analysis on the use of Xianling gubao capsules(XLGBC)for knee osteoarthritis(KOA),and provide evidence-based support for the clinical use of the drugs.METHODS Computerized searches including CNKI,Wanfang Data,VIP,China Biomedical Literature Database,the Cochrane Library,PubMed,Embase and Web of Science were conducted to collect systematic reviews(SR)or meta-analyses of XLGBC for the treatment of KOA from the inception to May 31st,2024.The report quality,methodological quality,risk of bias and evidence quality were assessed using the PRISMA 2020 statement,AMSTAR 2 scale,ROBIS tool and GRADE tool,respectively.A comprehensive quality analysis of the quantitative results from the SR/meta-analysis was also performed.RESULTS A total of five SR/meta-analyses were included.The evaluation results based on the PRISMA 2020 statement showed that one study report was relatively complete(21 points),while four studies had deficiencies(18-20 points).The assessment using the AMSTAR 2 scale indicated that the methodological quality of all five studies was rated as very low.According to the ROBIS tool evaluation,the risk of comprehensive bias in all five studies was classified as high.GRADE tool evaluation revealed that among 49 outcome indicators,5(10.2％)were rated as moderate-quality evidence(10.2％),12 as low-quality evidence(24.5％),and 32 as very low-qualitv evidence(65.3％).The results of comprehensive quality analysis showed that the clinical efficacy,visual analogue scale score,pain relief time,comprehensive indexes of knee joint function,the levels of inflammatory factors and the incidence of adverse events in patients with XLGBC combined with conventional treatment were significantly better than conventional treatment alone(P＜0.05).CONCLUSIONS Compared with conventional treatment,XLGBC in combination with conventional treatment for KOA may have some efficacy and safety advantages.However,due to the low quality of evidence for the outcome indicators included in the studies,the conclusions should be interpreted with caution.

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present. OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose. METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis. RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.