Deep learning-based pectoralis muscle volume segmentation method from chest computed tomography image using sagittal range detection and axial slice-based segmentation.

The pectoralis muscle is an important indicator of respiratory muscle function and has been linked to various parenchymal biomarkers, such as airflow limitation severity and diffusing capacity for carbon monoxide, which are widely used in diagnosing parenchymal diseases, including asthma and chronic obstructive pulmonary disease. Pectoralis muscle segmentation is a method for measuring muscle volume and mass for various applications. The segmentation method is based on deep-learning techniques that combine a muscle area detection model and a segmentation model. The training dataset for the detection model comprised multichannel images of patients, whereas the segmentation model was trained on 7,796 cases of the computed tomography (CT) image dataset of 1,841 patients. The dataset was expanded incrementally through an active learning process. The performance of the model was evaluated by comparing the segmentation results with manual annotations by radiologists and the volumetric differences between the CT image datasets of the same patients. The results indicated that the machine learning model is promising in segmenting the pectoralis major muscle, with good agreement between the automatic segmentation and manual annotations by radiologists. The training accuracy and loss values of the validation set were 0.9954 and 0.0725, respectively, and for segmentation, the loss value was 0.0579. This study shows the potential clinical usefulness of the machine learning model for pectoralis major muscle segmentation as a quantitative biomarker for various parenchymal and muscular diseases.

Isolation of Foreign Material-Free Endothelial Progenitor Cells Using CD31 Aptamer and Therapeutic Application for Ischemic Injury.

Endothelial progenitor cells (EPCs) can be isolated from human bone marrow or peripheral blood and reportedly contribute to neovascularization. Aptamers are 40-120-mer nucleotides that bind to a specific target molecule, as antibodies do. To utilize apatmers for isolation of EPCs, in the present study, we successfully generated aptamers that recognize human CD31, an endothelial cell marker. CD31 aptamers bound to human umbilical cord blood-derived EPCs and showed specific interaction with human CD31, but not with mouse CD31. However, CD31 aptamers showed non-specific interaction with CD31-negative 293FT cells and addition of polyanionic competitor dextran sulfate eliminated non-specific interaction without affecting cell viability. From the mixture of EPCs and 293FT cells, CD31 aptamers successfully isolated EPCs with 97.6% purity and 94.2% yield, comparable to those from antibody isolation. In addition, isolated EPCs were decoupled from CD31 aptamers with a brief treatment of high concentration dextran sulfate. EPCs isolated with CD31 aptamers and subsequently decoupled from CD31 aptamers were functional and enhanced the restoration of blood flow when transplanted into a murine hindlimb ischemia model. In this study, we demonstrated isolation of foreign material-free EPCs, which can be utilized as a universal protocol in preparation of cells for therapeutic transplantation.