Osteoporosis is an important cause of internal fixation loosening after spinal surgery.Cement-augmented pedicle screw instru-mentation(CAPSI)technique is the most widely used technique in clinical practice to improve the stability of pedicle screw,mainly applied in osteoporosis and revision surgery,which included conventional solid pedicles crews and fenestrated/cannulated pedicle screws technique.CAPSI technique may cause cement leakage and pulmonary embolism,and there is no consensus on its indications or technical points.Therefore,this article reviews the research progress of CAPSI,in order to provide relevant reference for clinical practice.

Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and model-assisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.

Objective To develop a clinical prediction model for predicting risk factors for prolonged hospital stay after anterior cervical discectomy and fusion(ACDF).Methods The clinical data of 914 patients underwent ACDF treatment for cervical spondylotic myelopathy(CSM)were retrospectively analyzed.According to the screening criteria,800 eligible patients were eventually included,and the patients were divided into the development cohort(n=560)and the validation cohort(n=240).LASSO regression was used to screen variables,and multivariate Logistic regression analysis was used to establish a prediction model.The prediction model was evaluated from three aspects:differentiation,calibration and clinical effectiveness.The performance of the model was evaluated by area under the curve(AUC)and Hosmer-Lemeshow test.Decision curve analysis(DCA)was used to evaluate the clinical effectiveness of the model.Results In this study,the five factors that were significantly associated with prolonged hospital stay were male,abnormal BMI,mild-to-moderate anemia,stage of surgery(morning,afternoon,evening),and alcohol consumption history.The AUC of the development cohort was 0.778(95%CI:0.740 to 0.816),with a cutoff value of 0.337,and that of the validation cohort was 0.748(95%CI:0.687 to 0.809),with a cutoff value of 0.169,indicating that the prediction model had good differentiation.At the same time,the Hosmer-Lemeshow test showed that the model had a good calibration degree,and the DCA proved that it was effective in clinical application.Conclusion The prediction model established in this study has excellent comprehensive performance,which can better predict the risk of prolonged hospital stay,and can guide clinical intervention as soon as possible,so as to minimize the postoperative hospital stay and reduce the cost of hospitalization.

Objective To explore the risk factors for surgical site infection(SSI)after transforaminal lumbar interbody fusion(TLIF)for the treatment of lumbar degenerative diseases.Methods A total of 1 000 patients who underwent TLIF for lumbar degenerative diseases in our hospital were included and divided into the infection group(n=23)and the non-infection group(n=977)according to whether the surgical incision was infected.General data,surgical and laboratory indicators of patients were collected,and potential risk factors of SSI were screened by univariate analysis and multivariate regression analysis,a nomogram model was established,and its predictive efficiency was validated by the receive operating characteristic(ROC)curve.Results The incidence of SSI in patients after TLIF was 2.3％.The results of univariate analysis showed that age,operative time,intraoperative blood loss,preoperative C-reactive protein(CRP),smoking,and diabetes mellitus were the significant risk factors for the occurrence of SSI.Multivariate regression analysis showed that older age,longer operation time,more intraoperative blood loss,smoking and diabetes mellitus were the independent risk factors for postoperative SSI.ROC curve showed that the nomogram model established in this study has good predictive efficiency.Conclusion Older age,longer operation time,more intraoperative blood loss,smoking,and diabetes mellitus were independent risk factors for postoperative SSI.For patients with these high risk factors,corresponding intervention measures should be taken before operation to reduce the incidence of SSI.

The present situation of finite element methods was reviewed when applied in investigating the influences of structural type and structural parameters on mechanical properties of vascular stents,the differences in fatigue performance of vascular stents made of different materials and the interactions of vascular stents,blood vessels and blood.The shortcomings of finite element methods were analyzed,and improvement of simulation accuracy and optimization of vessel and vascular stent setup were suggested to be focused on when performing finite element analysis of vascular stents.[Chinese Medical Equipment Journal,2024,45(10):107-113]

Objective To investigate the biocompatibility of new gadolinium-doped hydroxyapatite(Gd-HA)composite scaffolds and to explore their feasibility as cell culture materials and bone tissue engineering scaffolds.Methods The Gd-HA composite scaffolds were chemically synthesized and placed under the electron microscope for observation.The experiment was divided into three groups,the HA group,the Gd-HA group,and the control group.Rabbit adipose-derived mesenchymal stem cells(ADSCs)were isolated,cultured and characterized,and the Gd-HA composite scaffold extract was added to the ADSCs in vitro culture system.Cell survival and cytotoxicity were assessed by live-dead cell staining,cell proliferation ability within the scaffolds was assessed by CCK-8 assay,and the scaffolds were assessed by alizarin red staining for cell osteogenic differentiation.The toxic reactions of the scaffold materials were observed by skin irritation test,systemic acute toxicity test and muscle tissue and liver and kidney pathology at the site of intramuscular implantation of the scaffolds.Results The Gd-HA composite scaffold showed irregular void structure under electron microscope.Cell morphology observation showed that ADSCs grew adherently to the wall and were long shuttle-shaped.The positivity rate of CD29 was 96.94％,CD44 was 97.90％,CD45 was 0.10％,and CD34 was 0.46％,which was obtained using flow cytometry.Live-dead cell staining showed that the amount of live cells in the Gd-HA group was significantly better than that in the hydroxyapatite(HA)group after 5 days of co-culture.CCK-8 assay showed no significant difference in cell proliferation within 0-3 days.After 3 days,the Gd-HA group was significantly better than the HA group and the control group(P＜0.05).Calcium nodule deposition after alizarin red staining was significantly better in the Gd-HA group than in the HA and control groups,showing a deeper red color.No skin irritation was observed in gross and skin tissue HE observations after the contact of the extract with the skin.The general condition of the experimental groups was good after the infusion of the extract into the abdominal cavity,and the body mass tended to increase steadily(P＞0.05).HE staining showed that inflammatory reaction at the interface between the material and muscle tissue of the stent intramuscular implantation site in Gd-HA group was significantly higher than that of the control group,and the inflammatory cell infiltration was gradually reduced with the prolongation of implantation time.At the 8th weeks the morphology of the tissue around the material was close to normal muscle tissue,and no pathological changes were observed in the HE staining of liver and kidney at the 12th week.Conclusion Gd-HA composite scaffolds exhibit good biocompatibility and facilitate cell proliferation and osteogenic differentiation,and they are expected to serve as good carriers for stem cell transplantation in tissue engineering.

Background/Aims: Low educational attainment is a well-established risk factor for nonalcoholic fatty liver disease (NAFLD) in developed areas. However, the association between educational attainment and the risk of NAFLD is less clear in China. Methods: A cross-sectional study including over 200,000 Chinese adults across mainland China was conducted. Information on education level and lifestyle factors were obtained through standard questionnaires, while NAFLD and advanced fibrosis were diagnosed using validated formulas. Outcomes included the risk of NAFLD in the general population and high probability of fibrosis among patients with NAFLD. Logistic regression analysis was employed to estimate the risk of NAFLD and fibrosis across education levels. A causal mediation model was used to explore the potential mediators. Results: Comparing with those receiving primary school education, the multi-adjusted odds ratios (95% confidence intervals) for NAFLD were 1.28 (1.16 to 1.41) for men and 0.94 (0.89 to 0.99) for women with college education after accounting for body mass index. When considering waist circumference, the odds ratios (95% CIs) were 0.94 (0.86 to 1.04) for men and 0.88 (0.80 to 0.97) for women, respectively. The proportions mediated by general and central obesity were 51.00% and 68.04% for men, while for women the proportions were 48.58% and 32.58%, respectively. Furthermore, NAFLD patients with lower educational attainment showed an incremental increased risk of advanced fibrosis in both genders. Conclusions In China, a low education level was associated with a higher risk of prevalent NAFLD in women, as well as high probability of fibrosis in both genders.

Objective:To develop a risk prediction model for the recurrence of diabetic foot ulcer (DFU) in diabetic patients and primarily validate its predictive value.Methods:Meta-analysis combined with retrospective cohort study was conducted. The Chinese and English papers on risk factors related to DFU recurrence publicly published in China Biology Medicine disc, China National Knowledge Infrastructure, Wanfang Database, VIP Database, and PubMed, Embase, Cochrane Library, and Web of Science, and the search time was from the establishment date of each database until March 31 st, 2022. The papers were screened and evaluated, the data were extracted, a meta-analysis was performed using RevMan 5.4.1 statistical software to screen risk factors for DFU recurrence, and Egger's linear regression was used to assess the publication bias of the study results. Risk factors for DFU recurrence mentioned in ≥3 studies and with statistically significant differences in the meta-analysis were selected as the independent variables to develop a logistic regression model for risk prediction of DFU recurrence. The medical records of 101 patients with DFU who met the inclusion criteria and were admitted to Affiliated Hospital of Guizhou Medical University from January 2019 to June 2022 were collected. There were 69 males and 32 females, aged (63±14) years. The receiver operating characteristic (ROC) curve of the predictive performance of the above constructed predictive model for DFU recurrence was drawn, and the area under the ROC curve, maximum Youden index, and sensitivity and specificity at the point were calculated. Dataset including data of 8 risk factors for DFU recurrence and the DFU recurrence rates of 10 000 cases was simulated using RStudio software and a scatter plot was drawn to determine two probabilities for risk division of DFU recurrence. Using the β coefficients corresponding to 8 DFU recurrence risk factors ×10 and taking the integer as the score of coefficient weight of each risk factor, the total score was obtained by summing up, and the cutoff scores for risk level division were calculated based on the total score × two probabilities for risk division of DFU recurrence. Results:Finally, 20 papers were included, including 3 case-control studies and 17 cohort studies, with a total of 4 238 cases and DFU recurrence rate of 22.7% to 71.2%. Meta-analysis showed that glycosylated hemoglobin >7.5% and with plantar ulcer, diabetic peripheral neuropathy, diabetic peripheral vascular disease, smoking, osteomyelitis, history of amputation/toe amputation, and multidrug-resistant bacterial infection were risk factors for the recurrence of DFU (with odds ratios of 3.27, 3.66, 4.05, 3.94, 1.98, 7.17, 11.96, 3.61, 95% confidence intervals of 2.79-3.84, 2.06-6.50, 2.50-6.58, 2.65-5.84, 1.65-2.38, 2.29-22.47, 4.60-31.14, 3.13-4.17, respectively, P<0.05). There were no statistically significant differences in publication biases of diabetic peripheral neuropathy, diabetic peripheral vascular disease, glycosylated hemoglobin >7.5%, plantar ulcer, smoking, multidrug-resistant bacterial infection, or osteomyelitis ( P>0.05), but there was a statistically significant difference in the publication bias of amputation/toe amputation ( t=-30.39, P<0.05). The area under the ROC curve of the predictive model was 0.81 (with 95% confidence interval of 0.71-0.91) and the maximum Youden index was 0.59, at which the sensitivity was 72% and the specificity was 86%. Ultimately, 29.0% and 44.8% were identified respectively as the cutoff for dividing the probability of low risk and medium risk, and medium risk and high risk for DFU recurrence, while the corresponding total scores of low, medium, and high risks of DFU recurrence were <37, 37-57, and 58-118, respectively. Conclusions:Eight risk factors for DFU recurrence are screened through meta-analysis and the risk prediction model for DFU recurrence is developed, which has moderate predictive accuracy and can provide guidance for healthcare workers to take interventions for patient with DFU recurrence risk.

Aim To explore the effects of NaAsO