During anticoagulant therapy, major bleeding is one of the most severe adverse effects. This study aimed to evaluate the relationships between ABCB1, ABCG2, and CYP3A5 polymorphisms and plasma trough concentrations of apixaban, a direct inhibitor of coagulation factor X. A total of 70 plasma concentrations of apixaban from 44 Japanese patients with atrial fibrillation were analyzed. In these analyses, the plasma trough concentration/dose (C/D) ratio of apixaban was used as a pharmacokinetic index and all data were stratified according to the presence of ABCB1 (ABCB1 1236C>T, 2677G>T/A, and 3435C>T), ABCG2 (ABCG2 421C>A), and CYP3A5 (CYP3A5*3) polymorphisms. Influences of various clinical laboratory parameters (age, serum creatinine, estimated glomerular filtration rate, aspartate amino transferase, and alanine amino transferase) on the plasma trough C/D ratio of apixaban were included in analyses. Although no ABCB1 polymorphisms affected the plasma trough C/D ratio of apixaban, the plasma trough C/D ratio of apixaban was significantly higher in patients with the ABCG2 421A/A genotype than in patients with the ABCG2 421C/C genotype (P<0.01). The plasma trough C/D ratio of apixaban in patients with CYP3A5*1/*3 or *3/*3 genotypes was also significantly higher than that in patients with the CYP3A5*1/*1 genotype (P<0.05). Furthermore, the plasma trough C/D ratio of apixaban decreased with increased estimated glomerular filtration rate. These results indicate that ABCG2 421A/A and CYP3A5*3 genotypes and renal function are considered potential factors affecting trough concentrations of apixaban.

Trajectory prediction has gained great attention and significant progress has been made in recent years. However, most works rely on a key assumption that each video is successfully preprocessed by detection and tracking algorithms and the complete observed trajectory is always available. However, in complex real-world environments, we often encounter miss-detection of target agents (e.g., pedestrian, vehicles) caused by the bad image conditions, such as the occlusion by other agents. In this paper, we address the problem of trajectory prediction from incomplete observed trajectory due to miss-detection, where the observed trajectory includes several missing data points. We introduce a two-block RNN model that approximates the inference steps of the Bayesian filtering framework and seeks the optimal estimation of the hidden state when miss-detection occurs. The model uses two RNNs depending on the detection result. One RNN approximates the inference step of the Bayesian filter with the new measurement when the detection succeeds, while the other does the approximation when the detection fails. Our experiments show that the proposed model improves the prediction accuracy compared to the three baseline imputation methods on publicly available datasets: ETH and UCY (9% and 7% improvement on the ADE and FDE metrics). We also show that our proposed method can achieve better prediction compared to the baselines when there is no miss-detection.

Detecting surgical tools is an essential task for analyzing and evaluating surgical videos. However, most studies focus on minimally invasive surgery (MIS) and cataract surgery. Mainly because of a lack of a large, diverse, and well-annotated dataset, research in the area of open surgery has been limited so far. Open surgery video analysis is challenging because of its properties: varied number and roles of people (e.g., main surgeon, assistant surgeons, and nurses), a complex interaction of tools and hands, various operative environments, and lighting conditions. In this paper, to handle these limitations and difficulties, we introduce an egocentric open surgery dataset that includes 15 open surgeries recorded with a head-mounted camera. More than 67k bounding boxes are labeled to 19k images with 31 surgical tool categories. Finally, we present a surgical tool detection baseline model based on recent advances in object detection. The results of our new dataset show that our presented dataset provides enough interesting challenges for future methods and that it can serve as a strong benchmark to address the study of tool detection in open surgery.