RESUMEN
BACKGROUND: Since the outbreak of the COVID-19 pandemic, multiple efforts of modelling of the geo-temporal transmissibility of the virus have been undertaken, but none describes the pandemic spread at the global level. The aim of this research is to provide a high-resolution global model of the pandemic that overcomes the problem of biased country-level data on the number of infected cases. To achieve this we propose a novel SIR-type metapopulation transmission model and a set of analytically derived model parameters. We used them to perform a simulation of the disease spread with help of the Global Epidemic and Mobility (GLEAM) framework embedding actual population densities, commute patterns and long-range travel networks. The simulation starts on 17 November 2019 with the index case (presymptomatic, yet infectious) in Wuhan, China, and results in an accurate prediction of the number of diagnosed cases after 154 days in multiple countries across five continents. In addition, the model outcome shows high compliance with the results of a random screening test conducted on pregnant women in the New York area. METHODS: We have built a modified SIR metapopulation transmission model and parameterized it analytically either by setting the values of the parameters based on the literature, or by assuming their plausible values. We compared our results with the number of diagnosed cases in twenty selected countries, ones which provide reliable statistics but differ substantially in terms of strength and speed of undertaken Non-Drug Interventions. The obtained 95% confidence intervals for the predictions are in agreement with the empirical data. RESULTS: The parameters that successfully model the pandemic are: the basic reproduction number R 0, 4.4; a latent non-infectious period of 1.1. days followed by 4.6 days of the presymptomatic infectious period; the probability of developing severe symptoms, 0.01; the probability of being diagnosed when presenting severe symptoms of 0.6; the probability of diagnosis for cases with mild symptoms or asymptomatic, 0.001. DISCUSSION: Parameters that successfully reproduce the observed number of cases indicate that both R 0 and the prevalence of the virus might be underestimated. This is in concordance with the newest research on undocumented COVID-19 cases. Consequently, the actual mortality rate is putatively lower than estimated. Confirmation of the pandemic characteristic by further refinement of the model and screening tests is crucial for developing an effective strategy for the global epidemiological crisis.
RESUMEN
Cystic fibrosis (CF), the most common lethal genetic disease among Caucasians, is caused by mutations in cystic fibrosis transmembrane conductance regulator (CFTR). CFTR's main role is to transport chloride ions across epithelial cell membranes. It also regulates many cell functions. However, the exact role of CFTR in cellular processes is not yet fully understood. It is recognized that a key factor in CFTR-related regulation is its phosphorylation state. The important kinases regulating CFTR are cAMP-dependent protein kinase A (PKA) and 5'-AMP-activated protein kinase (AMPK). PKA and AMPK have opposite effects on CFTR activity despite their highly similar structures and recognition motifs. Utilizing homology modeling, in silico mutagenesis and literature mining, we supplement available information regarding the atomic-resolution structures of PKA, AMPK and CFTR, and the complexes CFTR-PKA and CFTR-AMPK. The atomic-resolution structural predictions reveal an unexpected availability of CFTR Ser813 for phosphorylation by both PKA and AMPK. These results indicate the key role of the structural flexibility of the serine-rich R-domain in CFTR regulation by phosphorylation.
