RESUMEN
This study introduces a novel method for detecting the post-COVID state using ECG data. By leveraging a convolutional neural network, we identify "cardiospikes" present in the ECG data of individuals who have experienced a COVID-19 infection. With a test sample, we achieve an 87 percent accuracy in detecting these cardiospikes. Importantly, our research demonstrates that these observed cardiospikes are not artifacts of hardware-software signal distortions, but rather possess an inherent nature, indicating their potential as markers for COVID-specific modes of heart rhythm regulation. Additionally, we conduct blood parameter measurements on recovered COVID-19 patients and construct corresponding profiles. These findings contribute to the field of remote screening using mobile devices and heart rate telemetry for diagnosing and monitoring COVID-19.
Asunto(s)
COVID-19 , Electrocardiografía , Humanos , COVID-19/diagnóstico , Algoritmos , Redes Neurales de la Computación , Aprendizaje AutomáticoRESUMEN
A tetravalent-substituted cobalt ludwigite Co2.5Ge0.5BO5 has been synthesized using the flux method. The compound undergoes two magnetic transitions: a long-range antiferromagnetic transition at TN1 = 84 K and a metamagnetic one at TN2 = 36 K. The sample-oriented magnetization measurements revealed a fully compensated magnetic moment along the a- and c-axes and an uncompensated one along the b-axis leading to high uniaxial anisotropy. A field-induced enhancement of the ferromagnetic correlations at TN2 is observed in specific heat measurements. The DFT+GGA calculation predicts the spin configuration of (↑↓↓↑) as a ground state with a magnetic moment of 1.37 µB/f.u. The strong hybridization of Ge(4s, 4p) with O (2p) orbitals resulting from the high electronegativity of Ge4+ is assumed to cause an increase in the interlayer interaction, contributing to the long-range magnetic order. The effect of two super-superexchange pathways Co2+-O-B-O-Co2+ and Co2+-O-M4-O-Co2+ on the magnetic state is discussed.