Depletion of COPI in cancer cells: the role of reactive oxygen species in the induction of lipid accumulation, noncanonical lipophagy and apoptosis.

The coatomer protein complex 1 (COPI) is a multisubunit complex that coats intracellular vesicles and is involved in intracellular protein trafficking. Recently we and others found that depletion of COPI complex subunits zeta (COPZ1) and delta (ARCN1) preferentially kills tumor cells relative to normal cells. Here we delineate the specific cellular effects and sequence of events of COPI complex depletion in tumor cells. We find that this depletion leads to the inhibition of mitochondrial oxidative phosphorylation and the elevation of reactive oxygen species (ROS) production, followed by accumulation of lipid droplets (LDs) and autophagy-associated proteins LC3-II and SQSTM1/p62 and, finally, apoptosis of the tumor cells. Inactivation of ROS in COPI-depleted cells with the mitochondrial-specific quencher, mitoquinone mesylate, attenuated apoptosis and markedly decreased both the size and the number of LDs. COPI depletion caused ROS-dependent accumulation of LC3-II and SQSTM1 which colocalizes with LDs. Lack of double-membrane autophagosomes and insensitivity to Atg5 deletion suggested an accumulation of a microlipophagy complex on the surface of LDs induced by depletion of the COPI complex. Our findings suggest a sequence of cellular events triggered by COPI depletion, starting with inhibition of oxidative phosphorylation, followed by ROS activation and accumulation of LDs and apoptosis.

Artificial intelligence: a disruptive tool for a smarter medicine.

OBJECTIVE: Although highly successful, the medical R&D model is failing at improving people's health due to a series of flaws and defects inherent to the model itself. A new collective intelligence, incorporating human and artificial intelligence (AI) could overcome these obstacles. Because AI will play a key role in this new collective intelligence, it is necessary that those involved in healthcare have a general knowledge of how these technologies work. With this comprehensive review, we intend to provide it. MATERIALS AND METHODS: A broad-ranging search has been undertaken on institutional and non-institutional websites in order to identify relevant papers, comments and reports. RESULTS: We firstly describe the flaws and defects of the current R&D biomedical model and how the generation of a new collective intelligence will result in a better and wiser medicine through a truly personalized and holistic approach. We, then, discuss the new forms of data collection and data processing and the different types of artificial learning and their specific algorithms. Finally, we review the current uses and applications of AI in the biomedical field and how these can be expanded, as well as the limitations and challenges of applying these new technologies in the medical field. CONCLUSIONS: This colossal common effort based on a new collective intelligence will exponentially improve the quality of medical research, resulting in a radical change for the better in the healthcare model. AI, without replacing us, is here to help us achieve the ambitious goal set by the WHO in the Alma Ata declaration of 1978: "Health for All".