Macrophages are professional phagocytes at the front line of immune defenses against foreign bodies and microbial pathogens. Various bacteria, which are responsible for deadly diseases including tuberculosis and salmonellosis, are capable of hijacking this important immune cell type and thrive intracellularly, either in the cytoplasm or in specialized vacuoles. Tight regulation of cellular metabolism is critical in shaping the macrophage polarization states and immune functions. Lipids, besides being the bulk component of biological membranes, serve as energy sources as well as signaling molecules during infection and inflammation. With the advent of systems-scale analyses of genes, transcripts, proteins, and metabolites, in combination with classical biology, it is increasingly evident that macrophages undergo extensive lipid remodeling during activation and infection. Each bacterium species has evolved its own tactics to manipulate host metabolism toward its own advantage. Furthermore, modulation of host lipid metabolism affects disease susceptibility and outcome of infections, highlighting the critical roles of lipids in infectious diseases. Here, we will review the emerging roles of lipids in the complex host–pathogen relationship and discuss recent methodologies employed to probe these versatile metabolites during the infection process. An improved understanding of the lipid-centric nature of infections can lead to the identification of the Achilles’ heel of the pathogens and host-directed targets for therapeutic interventions. Currently, lipid-moderating drugs are clinically available for a range of non-communicable diseases, which we anticipate can potentially be tapped into for various infections.

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Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

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