This paper proposes a gateway framework for in-vehicle networks based on CAN, FlexRay, and Ethernet. The proposed gateway framework is designed to be easy to reuse and verify, in order to reduce development costs and time. The gateway framework can be configured, and its verification environment is automatically generated by a program with a dedicated graphical user interface. The gateway framework provides state of the art functionalities that include parallel reprogramming, diagnostic routing, network management, dynamic routing update, multiple routing configuration, and security. The proposed gateway framework was developed, and its performance was analyzed and evaluated.
Kidney organoids derived from the human pluripotent stem cells (hPSCs) recapitulating human kidney are the attractive tool for kidney regeneration, disease modeling, and drug screening. However, the kidney organoids cultured by static conditions have the limited vascular networks and immature nephron-like structures unlike human kidney. Here, we developed a kidney organoid-on-a-chip system providing fluidic flow mimicking shear stress with optimized extracellular matrix (ECM) conditions. We demonstrated that the kidney organoids cultured in our microfluidic system showed more matured podocytes and vascular structures as compared to the static culture condition. Additionally, the kidney organoids cultured in microfluidic systems showed higher sensitivity to nephrotoxic drugs as compared with those cultured in static conditions. We also demonstrated that the physiological flow played an important role in maintaining a number of physiological functions of kidney organoids. Therefore, our kidney organoid-on-a-chip system could provide an organoid culture platform for in vitro vascularization in formation of functional three-dimensional (3D) tissues.
The intestinal microbiome affects a number of biological functions of the organism. Although the animal model is a powerful tool to study the relationship between the host and microbe, a physiologically relevant in vitro human intestinal system has still unmet needs. Thus, the establishment of an in vitro living cell-based system of the intestine that can mimic the mechanical, structural, absorptive, transport and pathophysiological properties of the human intestinal environment along with its commensal bacterial strains can promote pharmaceutical development and potentially replace animal testing. In this paper, we present a microfluidic-based gut model which allows co-culture of human and microbial cells to mimic the gastrointestinal structure. The gut microenvironment is recreated by flowing fluid at a low rate (21 μL/h) over the microchannels. Under these conditions, we demonstrated the capability of gut-on-a-chip to recapitulate in vivo relevance epithelial cell differentiation including highly polarized epithelium, mucus secretion, and tight membrane integrity. Additionally, we observed that the co-culture of damaged epithelial layer with the probiotics resulted in a substantial responded recovery of barrier function without bacterial overgrowth in a gut-on-a-chip. Therefore, this gut-on-a-chip could promote explorations interaction with host between microbe and provide the insights into questions of fundamental research linking the intestinal microbiome to human health and disease.
Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin-stained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods A total of 297 digital slides were obtained from frozen SLN sections, which include postneoadjuvant cases (n=144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). Results The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.
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