With the unprecedented developments in deep learning, automatic segmentation of main abdominal organs (i.e., liver, kidney, and spleen) seems to be a solved problem as the state-of-the-art (SOTA) methods have achieved comparable results with inter-observer variability on existing benchmark datasets. However, most of the existing abdominal organ segmentation benchmark datasets only contain single-center, single-phase, single-vendor, or single-disease cases, thus, it is unclear whether the excellent performance can generalize on more diverse datasets. In this paper, we present a large and diverse abdominal CT organ segmentation dataset, termed as AbdomenCT-1K, with more than 1000 (1K) CT scans from 11 countries, including multi-center, multi-phase, multi-vendor, and multi-disease cases. Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation, as well as reveal the unsolved segmentation problems of the SOTA method, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases. To advance the unsolved problems, we build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines. We believe the introduction of the AbdomenCT-1K dataset will promote the future in-depth research towards clinical applicable abdominal organ segmentation methods. Moreover, the datasets, codes and trained models of baseline methods will be publicly available.
The discovery of vibegron, a potent and selective human β3-AR agonist for the treatment of overactive bladder (OAB), is described. An early-generation clinical β3-AR agonist MK-0634 (3) exhibited efficacy in humans for the treatment of OAB, but development was discontinued due to unacceptable structure-based toxicity in preclinical species. Optimization of a series of second-generation pyrrolidine-derived β3-AR agonists included reducing the risk for phospholipidosis, the risk of formation of disproportionate human metabolites, and the risk of formation of high levels of circulating metabolites in preclinical species. These efforts resulted in the discovery of vibegron, which possesses improved druglike properties and an overall superior preclinical profile compared to MK-0634. Structure-activity relationships leading to the discovery of vibegron and a summary of its preclinical profile are described.
Background: Insulin resistance (IR) is a significant risk factor for cardiovascular disease (CVD). In this study, the association of the triglyceride glucose (TyG) index, a simple surrogate marker of IR, with arterial stiffness and 10-year CVD risk was evaluated.Methods: A total of 13,706 participants were enrolled. Anthropometric and cardiovascular risk factors were determined in all participants, while serum insulin levels were only measured in 955 participants. Arterial stiffness was measured through brachial-ankle pulse wave velocity (baPWV), and 10-year CVD risk was evaluated using the Framingham risk score.Results: All participants were classified into four groups according to the quartile of the TyG index. BaPWV and the percentage of participants in the 10-year CVD risk categories significantly increased with increasing quartiles of the TyG index. Logistic regression analysis showed that the TyG index was independently associated with a high baPWV and 10-year CVD risk after adjusting for traditional CVD risk factors. The area under the receiver operating characteristics curve (AUROC) of the TyG index for predicting a high baPWV was 0.708 (95%CI 0.693–0.722, P < 0.001) in women, higher than that in men. However, the association of the homeostatic model assessment of IR (HOMA-IR) with a high baPWV and the 10-year CVD risk was absent when adjusting for multiple risk factors in 955 participants.Conclusions: The TyG index is independently associated with arterial stiffness and 10-year CVD risk.
Nitrogen (N) deposition has been steadily increasing for decades, with consequences for soil respiration. However, we have a limited understanding of how soil respiration responds to N availability. Here, we investigated the soil respiration responses to low and high levels of N addition (0.4 mol N m−2 yr−1 vs 1.6 mol N m−2 yr−1) over a two-year period in a semiarid Leymus chinensis grassland in Inner Mongolia, China. Our results show that low-level N addition increased soil respiration, plant belowground biomass and soil microbial biomass carbon (MBC), while high-level N additions decreased them. Soil respiration was positively correlated with plant belowground biomass, MBC, soil temperature and soil moisture. Together plant belowground biomass and MBC explained 99.4% of variation in mean soil respiration, with plant belowground biomass explaining 63.4% of the variation and soil MBC explaining the remaining 36%. Finally, the temperature sensitivity of soil respiration was not influenced by N additions. Overall, our results suggest that low levels of N deposition may stimulate soil respiration, but large increases in N availability may decrease soil respiration, and that these responses are driven by the dissimilar responses of both plant belowground biomass and soil MBC.
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