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The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.
Assuntos
COVID-19 , Humanos , COVID-19/epidemiologia , SARS-CoV-2 , Pandemias , Síndrome de COVID-19 Pós-Aguda , Política de Saúde , Aprendizado de MáquinaRESUMO
Clinical and molecular heterogeneity are hallmarks of chronic lymphocytic leukemia (CLL), a neoplasm characterized by accumulation of mature and clonal long-lived CD5 + B-lymphocytes. Mutational status of the IgHV gene of leukemic clones is a powerful prognostic tool in CLL, and it is well established that unmutated CLLs (U-CLLs) have worse evolution than mutated cases. Nevertheless, progression and treatment requirement of patients can evolve independently from the mutational status. Microenvironment signaling or epigenetic changes partially explain this different behavior. Thus, we think that detailed characterization of the miRNAs landscape from patients with different clinical evolution could facilitate the understanding of this heterogeneity. Since miRNAs are key players in leukemia pathogenesis and evolution, we aim to better characterize different CLL behaviors by comparing the miRNome of clinically progressive U-CLLs vs. stable U-CLLs. Our data show up-regulation of miR-26b-5p, miR-106b-5p, and miR-142-5p in progressive cases and indicate a key role for miR-26b-5p during CLL progression. Specifically, up-regulation of miR-26b-5p in CLL cells blocks TGF-ß/SMAD pathway by down-modulation of SMAD-4, resulting in lower expression of p21-Cip1 kinase inhibitor and higher expression of c-Myc oncogene. This work describes a new molecular mechanism linking CLL progression with TGF-ß modulation and proposes an alternative strategy to explore in CLL therapy.
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The emergence of SARS-CoV-2 variants, as observed with the D614G spike protein mutant and, more recently, with B.1.1.7 (501Y.V1), B.1.351 (501Y.V2) and B.1.1.28.1 (P.1) lineages, represent a continuous threat and might lead to strains of higher infectivity and/or virulence. We report on the occurrence of a SARS-CoV-2 haplotype with nine mutations including D614G/T307I double-mutation of the spike. This variant expanded and completely replaced previous lineages within a short period in the subantarctic Magallanes Region, southern Chile. The rapid lineage shift was accompanied by a significant increase of cases, resulting in one of the highest incidence rates worldwide. Comparative coarse-grained molecular dynamic simulations indicated that T307I and D614G belong to a previously unrecognized dynamic domain, interfering with the mobility of the receptor binding domain of the spike. The T307I mutation showed a synergistic effect with the D614G. Continuous surveillance of new mutations and molecular analyses of such variations are important tools to understand the molecular mechanisms defining infectivity and virulence of current and future SARS-CoV-2 strains.