McIntyre Thorup (eelcircle27)

show its potential to be applicable to different disease sites.Purpose Since breast mass is a clear sign of breast cancer, its precise segmentation is of great significance for the diagnosis of breast cancer. However, the current diagnosis relies mainly on radiologists who spend time extracting features manually, which inevitably reduces the efficiency of diagnosis. Therefore, designing an automatic segmentation method is urgently necessary for the accurate segmentation of breast masses. Approach We propose an effective attention mechanism and multiscale pooling conditional generative adversarial network (AM-MSP-cGAN), which accurately achieves mass automatic segmentation in whole mammograms. In AM-MSP-cGAN, U-Net is utilized as a generator network by incorporating attention mechanism (AM) into it, which allows U-Net to pay more attention to the target mass regions without additional cost. As a discriminator network, a convolutional neural network with multiscale pooling module is used to learn more meticulous features from the masses with rough and fuzzy boundaries. The proposed model is trained and tested on two public datasets CBIS-DDSM and INbreast. Results Compared with other state-of-the-art methods, the AM-MSP-cGAN can achieve better segmentation results in terms of the dice similarity coefficient (Dice) and Hausdorff distance metrics, achieving top scores of 84.49% and 5.01 on CBIS-DDSM, and 83.92% and 5.81 on INbreast, respectively. Therefore, qualitative and quantitative experiments illustrate that the proposed model is effective and robust for the mass segmentation in whole mammograms. Conclusions The proposed deep learning model is suitable for the automatic segmentation of breast masses, which provides technical assistance for subsequent pathological structure analysis.Human immunodeficiency virus (HIV) is an attractive target for chimeric antigen receptor (CAR) therapy. CAR T cells have proved remarkably potent in targeted killing of cancer cells, and we surmised that CAR T cells could prove useful in eradicating HIV-infected cells. Toward this goal, we interrogate several neutralizing single-chain variable fragments (scFvs) that target different regions of the HIV envelope glycoprotein, gp120. We find here that CAR T cells with scFv from NIH45-46 antibody demonstrated the highest cytotoxicity. Although NIH45-46 CAR T cells are capable of eliminating antigen-expressing cells, we wanted to address HIV reactivation from ex vivo culture of HIV patient-derived CAR T cells. In order to capitalize on the HIV reactivation, we developed a conditionally replicating lentiviral vector (crLV). The crLV can hijack HIV machinery, forming a chimeric lentivirus (LV) instead of HIV and delivered to uninfected cells. We find that CAR T cells generated with crLVs have similar CAR-mediated functionality as traditional CARs. We also demonstrate crLVs' capability of expanding CAR percentage and protecting CD4 CAR T cell in HIV donors. Collectively, we demonstrate here that the novel crLV NIH45-46 CAR can serve as a strategy to combat HIV, as well as overcome HIV reactivation in CD4+ CAR T cells.With many ongoing clinical trials utilizing adeno-associated virus (AAV) gene therapy, it is necessary to find scalable and serotype-independent primary capture and recovery methods to allow for efficient and robust manufacturing processes. Here, we demonstrate the ability of a hydrophobic interaction chromatography membrane to capture and recover AAV1, AAV5, AAV8, and AAV "Mutant C" (a novel serotype incorporating elements of AAV3B and AAV8) particles from cell culture media and cell lysate with recoveries of 76%-100% of loaded material, depending on serotype. A simple, novel technique that integrates release and recovery of cell-associated AAV capsids is demonstrated. We show that by the addition of lyotropic salts to AAV-containing cell suspensions, AAV is released at an equivalent efficiency to mechanical lysis. The addition of the lyotro