Inhibition of Fas expression by RNAi modulates 5-fluorouracil-induced apoptosis in HCT116 cells expressing wild-type p53

AbstractDrug resistance to 5-fluorouracil (5-FU) is still a major limitation to its clinical use. In addition, the clinical value of p53 as a predictive marker for 5-FU-based chemotherapy remains a matter of debate. Here, we used HCT116 human colorectal cancer cells expressing wild-type p53 and investigated whether inhibition of Fas expression by interference RNA modulates 5-FU-induced apoptosis. Cells were treated with 5-FU (1, 4 or 8 μM) for 8–48 h. Cell viability was evaluated by trypan blue dye exclusion. Apoptosis was assessed by changes in nuclear morphology and caspase activity. The interference RNA technology was used to silence Fas expression. Caspase activation, p53, Fas, cytochrome c, and Bcl-2 family protein expression was evaluated by immunoblotting. 5-FU was cytotoxic in HCT116 cells (p<0.001). Nuclear fragmentation and caspase-3, -8 and -9 activities were also markedly increased in HCT116 cells after 5-FU (p<0.001). In addition, wild-type p53 and Fas expression were 25- and 4-fold increased (p<0.05). Notably, when interference RNA was used to inhibit Fas, 5-FU-mediated nuclear fragmentation and caspase activity were markedly reduced in HCT116 cells. Finally, western blot analysis of mitochondrial extracts from HCT116 cells exposed to 5-FU showed a 6-fold increase in Bax, together with a 3-fold decrease in cytochrome c (p<0.001). In conclusion, 5-FU exerts its cytotoxic effects, in part, through a p53/Fas-dependent apoptotic pathway that involves Bax translocation and mitochondrial permeabilization

Developing a victorious strategy to the second strong gravitational lensing data challenge

Strong lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with deep learning have become a popular approach due to these astronomical objects’ rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analysed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific strong lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named strong gravitational lensing challenge (SGLC). This work presents the deep learning strategies and methodology used to design the highest scoring algorithm in the second SGLC (II SGLC). We discuss the approach used for this data set, the choice of a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a data set representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable, and accurate analyses of strong lenses with deep learning frameworks