BACKGROUND:Currently,treatment method for knee osteoarthritis includes oral medicine,joint cavity drug injection,and physiotherapy,but the curative effect is limited.Existing studies have confirmed that silicon-based bioceramics can promote cartilage and subchondral bone repair and vascular regeneration.OBJECTIVE:To explore the effect of different concentrations of silicon-based bioceramics injected into the knee joint cavity in the treatment of knee osteoarthritis in rats.METHODS:Silicon-based bioceramics-calcium silicate was prepared.Twenty-five SD rats were randomly divided into five groups,with five rats in each group.The healthy group did not receive any intervention,and the modeling group,low-dose calcium silicate group,high-dose calcium silicate group,and saline group used anterior cruciate ligament transection to establish bilateral knee osteoarthritis models.Four weeks after modeling,0.05 mL of 50 and 100 mg/mL calcium silicate solution were injected into the knee joint cavity in the low-dose calcium silicate group and high-dose calcium silicate group,respectively,and 0.05 mL of saline was injected into the knee joint cavity in the saline group,once a week for 4 consecutive weeks.In the fifth week of administration,bilateral knee joint Micro-CT detection,knee joint cartilage hematoxylin-eosin staining,and modified Mankin score were performed.RESULTS AND CONCLUSION:(1)Micro-CT quantitative analysis showed that compared with the healthy group,the volume fraction and number of trabeculae of the medial tibial plateau in the modeling group decreased(P＜0.05),and the separation of trabeculae increased(P＜0.05).Compared with the modeling group,the volume fraction and number of trabeculae of the medial tibial plateau in the low-dose calcium silicate group and the saline group increased(P＜0.05),and the separation of trabeculae decreased(P＜0.05).(2)Hematoxylin-eosin staining showed that the cartilage surface of the healthy group and the low-dose calcium silicate group was relatively smooth and flat,the chondrocytes were evenly distributed,without clustered chondrocytes,the tide line was complete,and the staining was uniform;the cartilage surface of the high-dose calcium silicate group was slightly uneven,the middle and deep cells were disordered,with a small number of clustered chondrocytes,the tide line was discontinuous,and the staining was uneven;the cartilage surface of the saline group and the modeling group was obviously rough,the cells were disordered,with a large number of clustered chondrocytes,the tide line disappeared,and the staining was uneven.The modified Mankin score of the healthy group was lower than that of the high-dose calcium silicate group,the saline group,and the modeling group(P＜0.05).The modified Mankin score of the high-dose calcium silicate group and the low-dose calcium silicate group was lower than that of the saline group and the modeling group(P＜0.05).(3)The results show that calcium silicate knee joint injection has a certain effect in the treatment of knee osteoarthritis.Compared with 100 mg/mL calcium silicate solution,50 mg/mL calcium silicate solution can promote the recovery of subchondral bone and cartilage.

BACKGROUND:Currently,treatment method for knee osteoarthritis includes oral medicine,joint cavity drug injection,and physiotherapy,but the curative effect is limited.Existing studies have confirmed that silicon-based bioceramics can promote cartilage and subchondral bone repair and vascular regeneration.OBJECTIVE:To explore the effect of different concentrations of silicon-based bioceramics injected into the knee joint cavity in the treatment of knee osteoarthritis in rats.METHODS:Silicon-based bioceramics-calcium silicate was prepared.Twenty-five SD rats were randomly divided into five groups,with five rats in each group.The healthy group did not receive any intervention,and the modeling group,low-dose calcium silicate group,high-dose calcium silicate group,and saline group used anterior cruciate ligament transection to establish bilateral knee osteoarthritis models.Four weeks after modeling,0.05 mL of 50 and 100 mg/mL calcium silicate solution were injected into the knee joint cavity in the low-dose calcium silicate group and high-dose calcium silicate group,respectively,and 0.05 mL of saline was injected into the knee joint cavity in the saline group,once a week for 4 consecutive weeks.In the fifth week of administration,bilateral knee joint Micro-CT detection,knee joint cartilage hematoxylin-eosin staining,and modified Mankin score were performed.RESULTS AND CONCLUSION:(1)Micro-CT quantitative analysis showed that compared with the healthy group,the volume fraction and number of trabeculae of the medial tibial plateau in the modeling group decreased(P＜0.05),and the separation of trabeculae increased(P＜0.05).Compared with the modeling group,the volume fraction and number of trabeculae of the medial tibial plateau in the low-dose calcium silicate group and the saline group increased(P＜0.05),and the separation of trabeculae decreased(P＜0.05).(2)Hematoxylin-eosin staining showed that the cartilage surface of the healthy group and the low-dose calcium silicate group was relatively smooth and flat,the chondrocytes were evenly distributed,without clustered chondrocytes,the tide line was complete,and the staining was uniform;the cartilage surface of the high-dose calcium silicate group was slightly uneven,the middle and deep cells were disordered,with a small number of clustered chondrocytes,the tide line was discontinuous,and the staining was uneven;the cartilage surface of the saline group and the modeling group was obviously rough,the cells were disordered,with a large number of clustered chondrocytes,the tide line disappeared,and the staining was uneven.The modified Mankin score of the healthy group was lower than that of the high-dose calcium silicate group,the saline group,and the modeling group(P＜0.05).The modified Mankin score of the high-dose calcium silicate group and the low-dose calcium silicate group was lower than that of the saline group and the modeling group(P＜0.05).(3)The results show that calcium silicate knee joint injection has a certain effect in the treatment of knee osteoarthritis.Compared with 100 mg/mL calcium silicate solution,50 mg/mL calcium silicate solution can promote the recovery of subchondral bone and cartilage.

Objective To analyze the risk factors for aseptic inflammation in elderly osteoporosis pa-tients after artificial knee replacement surgery.Methods The clinical data of 223 elderly osteoporosis patients who underwent artificial knee replacement surgery were retrospectively analyzed.The incidence of postopera-tive aseptic inflammation was counted.Patients were divided into occurrence and non-occurrence groups based on whether aseptic inflammation occurred.Univariate and multivariate logistic regression were used to analyze the risk factors for concurrent aseptic inflammation.Results Among all patients,31 cases developed postoper-ative aseptic inflammation,with an incidence rate of 13.90%.The proportions of elderly patients,obese pa-tients,patients with diabetes,those with poor postoperative drainage,patients with hypoproteinemia,and those with excessive postoperative exercise were higher in the occurrence group than in the non-occurrence group(P<0.05).Advanced age,obesity,diabetes,poor postoperative drainage,hypoproteinemia and excessive post-operative exercise were risk factors for aseptic inflammation after total knee arthroplasty in elderly osteoporo-sis patients(OR=2.204,2.261,2.540,2.177,2.109,2.079;P<0.05).Conclusion Advanced age,obesity,diabetes,poor postoperative drainage,hypoproteinemia and excessive postoperative exercise are risk factors for aseptic inflammation after total knee arthroplasty in elderly osteoporosis patients.

Granulocytic myeloid-derived suppressor cells(G-MDSCs)are the principal subsets of myeloid-derived suppressor cells(MDSCs),which involved in development and progression of a variety of tumor and non-tumor diseases.G-MDSCs mediated disfunction of immune effector cells(such as T cells and NK cells)through producing Arg-1,ROS,iNOS,etc.is major cause of its immunosuppressive function.To date,a large number of studies have focused on tumor-promoting effects of G-MDSCs.However,their complex immunomodulatory functions,especially their positive effects in diseases such as autoimmune arthritis,are often overlooked.Besides,G-MDSCs share the same origin and phenotype with neutrophils,but their immune functions are heterogeneous.Therefore,precision of G-MDSCs targeted therapy will be largely improved if the two can be accurately distinguished,though this field is still under exploration.At present,there are abundant experimental studies on G-MDSCs at home and abroad,mainly in tumors,but there are no domestic reports on the immunomodulatory function of G-MDSCs in tumors and non-tumor diseases.Therefore,in this review,we summarizes the different roles played by G-MDSCs in tumor and non-tumor diseases,in order to reveal the important and complex functions of G-MDSCs in immune regulation.

Objective:To construct a deep learning model based on YOLOV8-Seg algorithm to conduct automatic segmentation for the central gland(CG)and peripheral zone(PZ)of prostate,so as to provide a reliable basis for clinical diagnosis and treatment.Methods:The sequence data of T2-weighted imaging(T2WI)of horizontal relaxation time of 158 patients were selected from a public data set of magnetic resonance imaging(MRI)for prostate MRI,which was provided by the Charité University Hospital in Berlin,were selected.The all data were divided into a training set(109 cases),a validation set(16 cases),and a test set(33 cases)as the ratio of 7 to1 to 2.A lightweight asymmetric decoupled head(LADH)structure and the large kernel UniRepLKNetBlock module were integrated into the YOLOV8-Seg algorithm to enhance the capabilities of model's extraction feature,and the new model was named as YOLOV8-URLK.The assessment model with mean Average Precision(mAP),Dice Similarity Coefficient(DSC),95%Hausdorff Distance(HD95),and Average Surface Distance(ASD)was adopted to segment performance of the detection at prostate CG and PZ.Comparative experiments were conducted among that and YOLOV8-Seg,TransU-Net,and U-Net network,so as to validate the effectiveness of YOLOV8-URLK for detection and segmentation at prostate zone.Results:On the test set,the mAP@0.5(box)of YOLOV8-URLK model was 0.878,and the mean Dice coefficients,the mean HD95 values and the ASD values of that at CG and PZ were respectively(0.867,17.123 and 1.461)and(14.902,0.898 and 1.112).On the test set,the mAP@0.5(box)of YOLOV8-Seg model was 0.860,and the mean Dice coefficients of that at CG and PZ were 0.851 and 0.884,the mean HD95 values of that at them were 19.174 and 15.298,and ASD values of that at them were 1.781 and 1.219,respectively.On test set,the mean Dice coefficients of TransU-Net model at CG and PZ were 0.864 and 0.824,and the mean HD95 values of that at them were 18.134 and 19.402,and ASD values of that at them were 1.698 and 1.717,respectively.On the test set,the mean Dice coefficients of the U-Net model at CG and PZ were 0.857 and 0.690,and the mean HD95 values of that at them were 18.976 and 26.934,and ASD values of that at them were 1.753 and 2.135.The YOLOV8-URLK model can better reappear the segmentation trend of manual annotations.Conclusion:The YOLOV8-URLK model demonstrates higher precision in the detection and segmentation of MRI images of prostate,which were superior to YOLOV8-Seg,TransU-Net and U-Net.It can enhance the efficiency of the detection and segmentation.

Objective To explore methods for point estimation in adaptive enrichment design,providing a reference for effect estimation inn this context.Methods We simulated efficacy data under adaptive enrichment design and subsequentially estimated the effects using five methods:maximum likelihood method,stage 2 method,Lindley's method,parametric Bootstrap method,and the uniformly minimum variance conditionally unbiased estimated(UMVCUE)method.The performance of these methods was further evaluated.Results The simulation study showed that the maximum likelihood method has a large bias and is very sensitive to some parameters,while Lindley's method tends to underestimate and the parametric bootstrap method sometimes overestimates.The stage 2 method and UMVCUE are almost unbiased.In terms of mean squared error(MSE),all methods perform well except stage 2 estimator.Conclusion In adaptive enrichment design,the UMVCUE performs well in terms of bias and MSE.Therefore,we recommend it as the point estimator for the treatment effect in adaptive enrichment design.

Objective To explore a Bayesian logistic regression model for step－in dosing regimens(eBLRM),which considers the cumulative toxicity probability across different dosing cycles to identify the maximum tolerated schedule(MTS).Methods The Bayesian logistic regression model(BLRM)was extended to obtain a posterior estimate for the cumulative toxicity probability of the last cycle based on accumulated patient data,enabling exploration of dose sequences.Results The performance of eBLRM was evaluated by comparison with the existing methods.Simulation results indicated that eBLRM performed better or equivalent in the proportion of the correct selection of MTS and patients assigned to real MTS under low－toxicity scenarios.In the case of high－toxicity scenarios,eBLRM had a higher proportion of early trial termination due to safety,resulting in slightly inferior performance compared to the existing method.Conclusion The eBLRM method demonstrates relatively good performance,providing a simple and comprehensible dose exploration approach for step－in dosing regimens.

Objective To explore methods for point estimation in adaptive enrichment design,providing a reference for effect estimation inn this context.Methods We simulated efficacy data under adaptive enrichment design and subsequentially estimated the effects using five methods:maximum likelihood method,stage 2 method,Lindley's method,parametric Bootstrap method,and the uniformly minimum variance conditionally unbiased estimated(UMVCUE)method.The performance of these methods was further evaluated.Results The simulation study showed that the maximum likelihood method has a large bias and is very sensitive to some parameters,while Lindley's method tends to underestimate and the parametric bootstrap method sometimes overestimates.The stage 2 method and UMVCUE are almost unbiased.In terms of mean squared error(MSE),all methods perform well except stage 2 estimator.Conclusion In adaptive enrichment design,the UMVCUE performs well in terms of bias and MSE.Therefore,we recommend it as the point estimator for the treatment effect in adaptive enrichment design.

Objective To explore a Bayesian logistic regression model for step－in dosing regimens(eBLRM),which considers the cumulative toxicity probability across different dosing cycles to identify the maximum tolerated schedule(MTS).Methods The Bayesian logistic regression model(BLRM)was extended to obtain a posterior estimate for the cumulative toxicity probability of the last cycle based on accumulated patient data,enabling exploration of dose sequences.Results The performance of eBLRM was evaluated by comparison with the existing methods.Simulation results indicated that eBLRM performed better or equivalent in the proportion of the correct selection of MTS and patients assigned to real MTS under low－toxicity scenarios.In the case of high－toxicity scenarios,eBLRM had a higher proportion of early trial termination due to safety,resulting in slightly inferior performance compared to the existing method.Conclusion The eBLRM method demonstrates relatively good performance,providing a simple and comprehensible dose exploration approach for step－in dosing regimens.

Objective:To construct a deep learning model based on YOLOV8-Seg algorithm to conduct automatic segmentation for the central gland(CG)and peripheral zone(PZ)of prostate,so as to provide a reliable basis for clinical diagnosis and treatment.Methods:The sequence data of T2-weighted imaging(T2WI)of horizontal relaxation time of 158 patients were selected from a public data set of magnetic resonance imaging(MRI)for prostate MRI,which was provided by the Charité University Hospital in Berlin,were selected.The all data were divided into a training set(109 cases),a validation set(16 cases),and a test set(33 cases)as the ratio of 7 to1 to 2.A lightweight asymmetric decoupled head(LADH)structure and the large kernel UniRepLKNetBlock module were integrated into the YOLOV8-Seg algorithm to enhance the capabilities of model's extraction feature,and the new model was named as YOLOV8-URLK.The assessment model with mean Average Precision(mAP),Dice Similarity Coefficient(DSC),95%Hausdorff Distance(HD95),and Average Surface Distance(ASD)was adopted to segment performance of the detection at prostate CG and PZ.Comparative experiments were conducted among that and YOLOV8-Seg,TransU-Net,and U-Net network,so as to validate the effectiveness of YOLOV8-URLK for detection and segmentation at prostate zone.Results:On the test set,the mAP@0.5(box)of YOLOV8-URLK model was 0.878,and the mean Dice coefficients,the mean HD95 values and the ASD values of that at CG and PZ were respectively(0.867,17.123 and 1.461)and(14.902,0.898 and 1.112).On the test set,the mAP@0.5(box)of YOLOV8-Seg model was 0.860,and the mean Dice coefficients of that at CG and PZ were 0.851 and 0.884,the mean HD95 values of that at them were 19.174 and 15.298,and ASD values of that at them were 1.781 and 1.219,respectively.On test set,the mean Dice coefficients of TransU-Net model at CG and PZ were 0.864 and 0.824,and the mean HD95 values of that at them were 18.134 and 19.402,and ASD values of that at them were 1.698 and 1.717,respectively.On the test set,the mean Dice coefficients of the U-Net model at CG and PZ were 0.857 and 0.690,and the mean HD95 values of that at them were 18.976 and 26.934,and ASD values of that at them were 1.753 and 2.135.The YOLOV8-URLK model can better reappear the segmentation trend of manual annotations.Conclusion:The YOLOV8-URLK model demonstrates higher precision in the detection and segmentation of MRI images of prostate,which were superior to YOLOV8-Seg,TransU-Net and U-Net.It can enhance the efficiency of the detection and segmentation.