Cardiac autonomic regulation in response to functional power threshold testing in elite cyclists / Regulación autonómica cardiaca en respuesta a la prueba de umbral de potencia funcional en ciclistas deélite

Objective: To evaluate the impact of a functional power threshold test (FTP) on cardiac autonomic regulation indicators in high performance cyclists.Methods: A total of 12 male elite cyclists (mean age 36.1 ± 11.2 years) were recruited. Body composition parameters were measured usingbioimpedancemetry and heart rate variability (HRV) before and after the application of the FTP assessment. Results: We observed that a greater sympathetic nervous system (SNS) index and Stress index on baseline were correlated with a smaller decrease in theparasympathetic nervous system (PNS) activity in response to the FTP test (ρ= 0.69, p = 0.013). Concerning morphological parameters, the skeletalmuscle index (SMI) was the only one that was inversely correlated with ∆PNS (ρ= -0.69, p = 0.02) whereas the muscle-bone index (MBI) displayed apositive correlation with ∆SNS (ρ = 0.82, p = 0.001). In fully adjusted models we found that waist-to-hip ratio (β= 7.90, CI95%[4.16, 11.63], t(8) = 4.88, p =0.001) and SMI significantly influenced ∆PNS (β = -1.38, CI95%[-1.84, -0.92], t(8) = -6.94, p < 0.001), whereas MBI (β= 10.26, CI95%[8.10, 12.42], t(8) =10.96, p < 0.001) and the interaction between the latter and Power achieved during FTP influenced ∆SNS (β = -0.05, CI95%[-0.09, -4.99e-03], t(8) = -2.56, p= 0.033). Conclusion: Our findings indicate that the SMI had a negative effect on the ∆PNS, while the MBI was positively correlated with the ∆SNS in cyclists. Thesefindings suggest that a higher SMI and MBI could have a detrimental impact on the cardiac autonomic response to maximal aerobic exercise in high-performance cyclists, such as FTP.(AU)

Objetivo: Evaluar el impacto de una prueba de umbral de potencia funcional (FTP) sobre los indicadores de regulación autonómica cardiaca en ciclistasde alto rendimiento. Métodos: Se reclutó a un total de 12 ciclistas de élite masculinos (edad media 36.1 ± 11.2 años). Se midieron los parámetros de composición corporalmediante bioimpedanciometría y la variabilidad de la frecuencia cardiaca (HRV) antes y después de la aplicación de la evaluación del FTP. Resultados: Observamos que un mayor índice del sistema nervioso simpático (SNS) e índice de estrés basalmente se correlacionaron con una menordisminución de la actividad del sistema nervioso parasimpático (PNS) en respuesta a la prueba FTP (ρ= 0.69, p = 0.013). En cuanto a los parámetrosmorfológicos, el índice músculo esquelético (SMI) fue el único que se correlacionó inversamente con el ∆PNS (ρ= -0.69, p = 0.02) mientras que el índicemúsculo-hueso (MBI) mostró una correlación positiva con ∆SNS (ρ = 0.82, p = 0.001). En los modelos totalmente ajustados encontramos que la relacióncintura-cadera (β= 7.90, CI95%[4.16, 11.63], t(8) = 4.88, p = 0.001) y el SMI influían significativamente en el ∆PNS (β= -1.38, CI95%[-1.84, -0.92], t(8) = -6.94,p < 0.001), mientras que el MBI (β = 10.26, CI95%[8.10, 12.42], t(8) = 10.96, p < 0.001) y la interacción entre este último y la Potencia alcanzada durante elFTP influían en el ∆SNS (β= -0.05, CI95%[-0.09, -4.99e-03], t(8) = -2.56, p = 0.033). Conclusión: Nuestros hallazgos indican que el SMI tuvo un efecto negativo sobre el ∆PNS, mientras que el MBI se correlacionó positivamente con el ∆SNSen ciclistas. Estos hallazgos sugieren que un mayor SMI y MBI podrían tener un impacto perjudicial en la respuesta autonómica cardíaca al ejercicioaeróbico máximo en ciclistas de alto rendimiento, como el FTP.(AU)

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management.

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.

Mutation in a SARS-CoV-2 Haplotype from Sub-Antarctic Chile Reveals New Insights into the Spike's Dynamics.

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.