Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

Breast Arterial Calcification(BAC)is a mammographic decision dissimilar to cancer and commonly observed in elderly women.Thus identifying BAC could provide an expense,and be inaccurate.Recently Deep Learning(DL)methods have been introduced for automatic BAC detection and quantification with increased accuracy.Previously,classification with deep learning had reached higher efficiency,but designing the structure of DL proved to be an extremely challenging task due to overfitting models.It also is not able to capture the patterns and irregularities presented in the images.To solve the overfitting problem,an optimal feature set has been formed by Enhanced Wolf Pack Algorithm(EWPA),and their irregularities are identified by Dense-kUNet segmentation.In this paper,Dense-kUNet for segmentation and optimal feature has been introduced for classification(severe,mild,light)that integrates DenseUNet and kU-Net.Longer bound links exist among adjacent modules,allowing relatively rough data to be sent to the following component and assisting the system in finding higher qualities.The major contribution of the work is to design the best features selected by Enhanced Wolf Pack Algorithm(EWPA),and Modified Support Vector Machine(MSVM)based learning for classification.k-Dense-UNet is introduced which combines the procedure of Dense-UNet and kU-Net for image segmentation.Longer bound associations occur among nearby sections,allowing relatively granular data to be sent to the next subsystem and benefiting the system in recognizing smaller characteristics.The proposed techniques and the performance are tested using several types of analysis techniques 826 filled digitized mammography.The proposed method achieved the highest precision,recall,F-measure,and accuracy of 84.4333%,84.5333%,84.4833%,and 86.8667%when compared to other methods on the Digital Database for Screening Mammography(DDSM).

Mesenchymal stem cells transduced by PLEGFP-N1 retroviral vector maintain their biological features and differentiation

Background Enhanced green fluorescent protein （EGFP） has been an important reporter gene for gene therapy. Human mesenchymal stem cells （hMSCs） are ideal target cells in cell transplantation and tissue engineering. We investigated their biological characteristics and differentiation mediated by PLEGFP-N1 retroviral transduction.Methods hMSCs were isolated from human bone marrow by density gradient fractionation and adherence to plastic flasks. Individual colonies were selected and cultured in tissue dishes. Packaging cells PT67 were transfected by PLEGFP-N1 retroviral vector , and hMSCs were transduced by viral supernatant infection. Meanwhile, hMSCs-EGFP were identified by immune phenotypes and whether it could differentiate into osteoblasts or adipocytes under conditioned media was investigated.Results The rate of stably transduced hMSCs-EGFP was up to 96% after being screened by G418. hMSCs-EGFP exhibited fibroblast-like morphological features. Flow cytometric analyses showed that hMSCs-EGFP were positive for CD73, CD105, CD166, CD90 and CD44, but negative for CD34 and CD45. In addition, it could functionally be induced into osteocytes or adipocytes under conditioned media. These biological features of hMSCs-EGFP were consistent with those of hMSCs.Conclusions hMSCs transduced by PLEGFP-N1 retroviral vector can be used in vivo securely because they can maintain their biological characteristics and differentiation. It is a simple and reliable way to trace the changes of hMSCs in vivo by EGFP during cell transplantation and gene therapy.