Effects of antimony exposure on DNA damage and genome-wide variation in zebrafish (Danio rerio) liver.

Antimony (Sb) is a potentially toxic and carcinogenic cumulative contaminant that poses a serious threat to aquatic ecosystems. To better clarify the genotoxicity of Sb and its mechanism of action. In this study, we investigated DNA damage and genome-wide variation in the liver of a model organism, zebrafish (Danio rerio), under subacute Sb exposure and explored its potential toxicological mechanisms. The results showed that medium and high concentrations of Sb significantly reduced the total antioxidant capacity and increased the content of reactive oxygen species in zebrafish liver, and further studies revealed that it increased oxidative DNA damage and DNA-DNA cross-link (DDC), but had little effect on DNA-protein cross-link (DPC). The result of resequencing showed that the mutation sites of the genes with high concentrations of Sb were higher than those with medium concentrations, and the mutation was mainly a single nucleotide. The pathways significantly enriched for nonsynonymous single nucleotide polymorphisms (SNPs) and insertion/deletion mutations (InDels) variant genes in the coding regions of both the medium and high Sb-treated groups were ECM-receptor interactions, and the high Sb-treated group also included lysine degradation, hematopoietic cell lineage, and cytokine-cytokine receptor interactions. This suggests that ECM-receptor interactions play an important role in the mechanism of antimony toxicity to the liver of zebrafish.

Deep Learning for Detecting Cerebral Aneurysms with CT Angiography.

Background Cerebral aneurysm detection is a challenging task. Deep learning may become a supportive tool for more accurate interpretation. Purpose To develop a highly sensitive deep learning-based algorithm that assists in the detection of cerebral aneurysms on CT angiography images. Materials and Methods Head CT angiography images were retrospectively retrieved from two hospital databases acquired across four different scanners between January 2015 and June 2019. The data were divided into training and validation sets; 400 additional independent CT angiograms acquired between July and December 2019 were used for external validation. A deep learning-based algorithm was constructed and assessed. Both internal and external validation were performed. Jackknife alternative free-response receiver operating characteristic analysis was performed. Results A total of 1068 patients (mean age, 57 years ± 11 [standard deviation]; 660 women) were evaluated for a total of 1068 CT angiograms encompassing 1337 cerebral aneurysms. Of these, 534 CT angiograms (688 aneurysms) were assigned to the training set, and the remaining 534 CT angiograms (649 aneurysms) constituted the validation set. The sensitivity of the proposed algorithm for detecting cerebral aneurysms was 97.5% (633 of 649; 95% CI: 96.0, 98.6). Moreover, eight new aneurysms that had been overlooked in the initial reports were detected (1.2%, eight of 649). With the aid of the algorithm, the overall performance of radiologists in terms of area under the weighted alternative free-response receiver operating characteristic curve was higher by 0.01 (95% CI: 0.00, 0.03). Conclusion The proposed deep learning algorithm assisted radiologists in detecting cerebral aneurysms on CT angiography images, resulting in a higher detection rate. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kallmes and Erickson in this issue.