Normal red blood cells (RBCs) have remarkable properties of deformability, which enable them to squeeze through tiny splenic inter-endothelial slits (IESs) without any damage. Decreased surface-area-to-volume (SA/V) ratio through the loss of membrane surface is a key determinant of splenic entrapment of surface-altered RBCs due to cell aging or disease. Here, we investigate the flow dynamics and mechanical retention of the surface-altered RBCs with different extents of surface area loss, using a multiscale RBC (MS-RBC) model implemented in dissipative particle dynamics (DPD). We show that the DPD-based MS-RBC simulations can accurately reproduce the ex vivo experimentally measured rate of RBC mechanical retention when we take into account the distribution of RBC surface area (i.e., the size difference within the RBC population). We also examine the cumulative effect of the cell surface area loss on the traversal dynamics of the surface-altered RBCs, where we found that the final values of cell surface area (or the SA/V ratio) play a key role in determining the RBC traversal dynamics, regardless of the loss pathway of cell surface area. Taken together, these simulation results have implications for understanding the sensitivity of the splenic IESs to retain and clear the surface-altered RBCs with increased surface area loss, providing an insight into the fundamental flow dynamics and mechanical clearance of the surface-altered RBCs by the human spleen.

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Pneumonia is a disease that develops rapidly and seriously threatens the survival and health of human beings. At present, the computer-aided diagnosis (CAD) of pneumonia is mostly based on binary classification algorithms that cannot provide doctors with location information. To solve this problem, this study proposes an end-to-end highly efficient algorithm for the detection of pneumonia based on a convolutional neural network—Pneumonia Yolo (PYolo). This algorithm is an improved version of the Yolov3 algorithm for X-ray image data of the lungs. Dilated convolution and an attention mechanism are used to improve the detection results of pneumonia lesions. In addition, double K-means is used to generate an anchor box to improve the localization accuracy. The algorithm obtained 46.84 mean average precision (mAP) on the X-ray image dataset provided by the Radiological Society of North America (RSNA), surpassing other detection algorithms. Thus, this study proposes an improved algorithm that can provide doctors with location information on lesions for the detection of pneumonia.

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