Image2_Comparison of current treatment strategy for osteonecrosis of the femoral head from the perspective of cell therapy.TIFF

Aims: The purpose of our study is to compare the effects of core decompression (CD) and bone grafting (BG) on osteonecrosis of the femoral head (ONFH). And evaluate the efficacy of CD based on cell therapy to provide guidance for the dose and number of cells.Methods: We searched PubMed, Embase, and the Cochrane Library between 2012 and 2022, with keywords including “osteonecrosis of the femoral head”, “core decompression” and “bone grafting”. We selected comparative studies of CD and BG, and the comparison of CD combined with bone marrow (BM) transplantation and CD alone. Changes in hip pain were assessed by VAS, hip function were assessed by HHS and WOMAC, and THA conversion rate was used as an evaluation tool for femoral head collapse. From these three aspects, the dose of bone marrow and the number of cells transplantation were subgroup analyzed.Results: Eleven studies were used to compare the efficacy of CD and BG. There was no significant difference in HHS, and the THA conversion rate of BG was significantly lower than that of CD. Thirteen CD studies based on cell therapy were included in the meta-analysis. Bone marrow aspiration concentrate (BMAC) can significantly improve VAS (mean difference (MD), 10.15; 95% confidence intervals (CI) 7.35 to 12.96, p 9 magnitude in VAS score were lower.Conclusion: In general, there is no consensus on the use of BG in the treatment of ONFH. The enhancement of cell-based CD procedure shows promising results. Using 20 mL BMAC and 109 magnitude BMMC is likely to achieve better results.</p

Image3_Comparison of current treatment strategy for osteonecrosis of the femoral head from the perspective of cell therapy.TIFF

Aims: The purpose of our study is to compare the effects of core decompression (CD) and bone grafting (BG) on osteonecrosis of the femoral head (ONFH). And evaluate the efficacy of CD based on cell therapy to provide guidance for the dose and number of cells.Methods: We searched PubMed, Embase, and the Cochrane Library between 2012 and 2022, with keywords including “osteonecrosis of the femoral head”, “core decompression” and “bone grafting”. We selected comparative studies of CD and BG, and the comparison of CD combined with bone marrow (BM) transplantation and CD alone. Changes in hip pain were assessed by VAS, hip function were assessed by HHS and WOMAC, and THA conversion rate was used as an evaluation tool for femoral head collapse. From these three aspects, the dose of bone marrow and the number of cells transplantation were subgroup analyzed.Results: Eleven studies were used to compare the efficacy of CD and BG. There was no significant difference in HHS, and the THA conversion rate of BG was significantly lower than that of CD. Thirteen CD studies based on cell therapy were included in the meta-analysis. Bone marrow aspiration concentrate (BMAC) can significantly improve VAS (mean difference (MD), 10.15; 95% confidence intervals (CI) 7.35 to 12.96, p 9 magnitude in VAS score were lower.Conclusion: In general, there is no consensus on the use of BG in the treatment of ONFH. The enhancement of cell-based CD procedure shows promising results. Using 20 mL BMAC and 109 magnitude BMMC is likely to achieve better results.</p

Image1_Comparison of current treatment strategy for osteonecrosis of the femoral head from the perspective of cell therapy.TIFF

Aims: The purpose of our study is to compare the effects of core decompression (CD) and bone grafting (BG) on osteonecrosis of the femoral head (ONFH). And evaluate the efficacy of CD based on cell therapy to provide guidance for the dose and number of cells.Methods: We searched PubMed, Embase, and the Cochrane Library between 2012 and 2022, with keywords including “osteonecrosis of the femoral head”, “core decompression” and “bone grafting”. We selected comparative studies of CD and BG, and the comparison of CD combined with bone marrow (BM) transplantation and CD alone. Changes in hip pain were assessed by VAS, hip function were assessed by HHS and WOMAC, and THA conversion rate was used as an evaluation tool for femoral head collapse. From these three aspects, the dose of bone marrow and the number of cells transplantation were subgroup analyzed.Results: Eleven studies were used to compare the efficacy of CD and BG. There was no significant difference in HHS, and the THA conversion rate of BG was significantly lower than that of CD. Thirteen CD studies based on cell therapy were included in the meta-analysis. Bone marrow aspiration concentrate (BMAC) can significantly improve VAS (mean difference (MD), 10.15; 95% confidence intervals (CI) 7.35 to 12.96, p 9 magnitude in VAS score were lower.Conclusion: In general, there is no consensus on the use of BG in the treatment of ONFH. The enhancement of cell-based CD procedure shows promising results. Using 20 mL BMAC and 109 magnitude BMMC is likely to achieve better results.</p

Prediction performance of quantile regression forests for CCLE data set.

(A) Bar chart of Pearson correlation coefficients of drug responses and predicted values by QRFs, ENR, ISIS, and CRF-20000. QRFs (mean): (conditional) mean prediction of drug response given genomic features using QRFs; QRFs (median): median prediction of drug response using QRFs. (B) Scatter plots of observed and predicted drug responses (activity area) for four drugs in CCLE using QRFs.</p

Workflow of the three-step quantile regression forest method.

All features were screened by their Pearson correlations with drug response. Then a random forest was trained to rank selected features by their importance. The variables with the importance of twice standard deviation greater than the mean of importance were selected for the final quantile regression forest.</p

Information of the 95% and 80% prediction intervals of drug responses for 24 drugs.

Information of the 95% and 80% prediction intervals of drug responses for 24 drugs.</p

A quantile regression forest based method to predict drug response and assess prediction reliability

Drug response prediction is a critical step for personalized treatment of cancer patients and ultimately leads to precision medicine. A lot of machine-learning based methods have been proposed to predict drug response from different types of genomic data. However, currently available methods could only give a “point” prediction of drug response value but fail to provide the reliability and distribution of the prediction, which are of equal interest in clinical practice. In this paper, we proposed a method based on quantile regression forest and applied it to the CCLE dataset. Through the out-of-bag validation, our method achieved much higher prediction accuracy of drug response than other available tools. The assessment of prediction reliability by prediction intervals and its significance in personalized medicine were illustrated by several examples. Functional analysis of selected drug response associated genes showed that the proposed method achieves more biologically plausible results.</div

The 95% prediction intervals and mean predictions by quantile regression forests.

Red triangular indicates the point (or mean) prediction of drug response, two red dots indicates the upper and lower boundaries of 95% prediction interval. (A) and (B) show the comparisons of 24 drugs for cell lines “CAPAN2” and “C2BBE1”, respectively. (C) and (D) are the comparisons of four different cell lines to drugs “Irinotecan” and “Topotecan”, respectively.</p

Variable importance and word clouds of functional annotations for the genes used by QRFs.

Panels (A) and (B) are the bar charts of variable importance for drugs 17-AAG and AZD6244. Word clouds of functional annotations of the genes for 24 drugs are in panel (C) (all genes) and panel (D) (ensemble of top 30 genes of each drug), where font size of each annotation indicates its enrichment score.</p

Boxplots and normal Q-Q plots of the activity areas in the CCLE dataset.

Panel (A) shows the boxplots of activity areas for 24 drugs. Panel (B) shows the normal Q-Q plots of activity area for two example drugs Lapatinib and Paclitaxel.</p