Introduction: COVID-19 is a novel and devastating disease. Its manifestations vary from asymptomatic to lethal. Moreover, mortality rates differ based on underlying health conditions and ethnicity. We investigated the biochemical rationale behind these observations using machine reasoning by the sci.AI system (https://sci.ai/). Facts were extracted and linked from publications available in nlm.nih.gov and Europe PMC to form the dataset which was validated by medical experts. Results: Based on the analysis of experimental and clinical data, we synthesized detailed biochemical pathways of COVID-19 pathogenesis which were used to explain epidemiological and clinical observations. Clinical manifestations and biomarkers are highlighted to monitor the course of COVID-19 and navigate treatment. As depicted in the Graphical Abstract, SARS-CoV-2 triggers a pro-oxidant (PO) response leading to the production of reactive oxygen species (ROS) as a normal innate defense. However, SARS-CoV-2's unique interference with the antioxidant (AO) system, through suppression of nitric oxide (NO) production in the renin-angiotensin-aldosterone system (RAAS), leads to an excessive inflammatory PO response. The excessive PO response becomes critical in cohorts with a compromised AO system such as patients with glucose-6-phosphate dehydrogenase deficiency (G6PDd) where NO and glutathione (GSH) mechanisms are impaired. G6PDd develops in patients with metabolic syndrome. It is mediated by aldosterone (Ald) which also increases specifically in COVID-19. Conclusion: G6PD is essential for an adequate immune response. Both G6PDd and SARS-CoV-2 compromise the AO system through the same pathways rendering G6PDd the Achilles' heel for COVID-19. Thus, the evolutionary antimalarial advantage of the G6PDd cohort can be a disadvantage against SARS-CoV-2.

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Abstract. The number of published findings in biomedicine increases continually. At the same time, specifics of the domain's terminology complicates the task of relevant publications retrieval. In the current research, we investigate influence of terms' variability and ambiguity on a paper's likelihood of being retrieved. We obtained statistics that demonstrate significance of the issue and its challenges, followed by presenting the sci.AI platform, which allows precise terms labeling as a resolution.

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