Establishment of a monoclonal antibody for human LXRα: Detection of LXRα protein expression in human macrophages

Liver X activated receptor alpha (LXRα) forms a functional dimeric nuclear receptor with RXR that regulates the metabolism of several important lipids, including cholesterol and bile acids. As compared with RXR, the LXRα protein level in the cell is low and the LXRα protein itself is very hard to detect. We have previously reported that the mRNA for LXRα is highly expressed in human cultured macrophages. In order to confirm the presence of the LXRα protein in the human macrophage, we have established a monoclonal antibody against LXRα, K-8607. The binding of mAb K-8607 to the human LXRα protein was confirmed by a wide variety of different techniques, including immunoblotting, immunohistochemistry, and electrophoretic mobility shift assay (EMSA). By immunoblotting with this antibody, the presence of native LXR protein in primary cultured human macrophage was demonstrated, as was its absence in human monocytes. This monoclonal anti-LXRα antibody should prove to be a useful tool in the analysis of the human LXRα protein

Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping

This paper presents a fully-automated method for the identification of suspicious regions of a coronavirus disease (COVID-19) on chest CT volumes. One major role of chest CT scanning in COVID-19 diagnoses is identification of an inflammation particular to the disease. This task is generally performed by radiologists through an interpretation of the CT volumes, however, because of the heavy workload, an automatic analysis method using a computer is desired. Most computer-aided diagnosis studies have addressed only a portion of the elements necessary for the identification. In this work, we realize the identification method through a classification task by using a 2.5-dimensional CNN with three-dimensional attention mechanisms. We visualize the suspicious regions by applying a backpropagation based on positive gradients to attention-weighted features. We perform experiments on an in-house dataset and two public datasets to reveal the generalization ability of the proposed method. The proposed architecture achieved AUCs of over 0.900 for all the datasets, and mean sensitivity $0.853 \pm 0.036$ and specificity $0.870 \pm 0.040$. The method can also identify notable lesions pointed out in the radiology report as suspicious regions.Comment: 10 pages, 3 figure