Publications

Despite tremendous progress in the understanding of COVID-19, mechanistic insight into immunological, disease-driving factors remains limited. We generated maVie16, a mouse-adapted SARS-CoV-2, by serial passaging of a human isolate. In silico modeling revealed how only three Spike mutations of maVie16 enhanced interaction with murine ACE2. maVie16 induced profound pathology in BALB/c and C57BL/6 mice, and the resulting mouse COVID-19 (mCOVID-19) replicated critical aspects of human disease, including early lymphopenia, pulmonary immune cell infiltration, pneumonia, and specific adaptive immunity. Inhibition of the proinflammatory cyto-kines IFN? and TNF substantially reduced immunopathology. Importantly, genetic ACE2-deficiency completely prevented mCOVID-19 development. Finally, inhalation therapy with recombinant ACE2 fully protected mice from mCOVID-19, revealing a novel and efficient treatment. Thus, we here present maVie16 as a new tool to model COVID-19 for the discovery of new therapies and show that disease severity is determined by cytokine-driven immunopathology and critically dependent on ACE2 in vivo. © Gawish et al.

JTD Keywords: covid-19 mouse model, covid-19 therapy, cytokine storm, immunology, inflammation, mavie16, mouse, mouse-adapted sars-cov-2, program, recombinant soluble ace2, tmprss2, Adaptive immunity, Angiotensin converting enzyme 2, Angiotensin-converting enzyme 2, Animal, Animal cell, Animal experiment, Animal model, Animal tissue, Animals, Apoptosis, Article, Bagg albino mouse, Breathing rate, Bronchoalveolar lavage fluid, C57bl mouse, Cell composition, Cell infiltration, Controlled study, Coronavirus disease 2019, Coronavirus spike glycoprotein, Covid-19, Cytokeratin 18, Cytokine production, Dipeptidyl carboxypeptidase, Disease model, Disease models, animal, Disease severity, Drosophila-melanogaster, Enzyme linked immunosorbent assay, Expression vector, Flow cytometry, Gamma interferon, Gene editing, Gene expression, Gene mutation, Genetic engineering, Genetics, Glycosylation, High mobility group b1 protein, Histology, Histopathology, Immune response, Immunocompetent cell, Immunology, Immunopathology, Interferon-gamma, Interleukin 2, Metabolism, Mice, inbred balb c, Mice, inbred c57bl, Mouse-adapted sars-cov-2, Myeloperoxidase, Neuropilin 1, Nonhuman, Nucleocapsid protein, Pathogenicity, Peptidyl-dipeptidase a, Pyroptosis, Recombinant soluble ace2, Renin angiotensin aldosterone system, Rna extraction, Rna isolation, Sars-cov-2, Severe acute respiratory syndrome coronavirus 2, Spike glycoprotein, coronavirus, T lymphocyte activation, Trabecular meshwork, Tumor necrosis factor, Virology, Virus load, Virus replication, Virus transmission, Virus virulence

Fixed sample entropy (fSampEn) is a promising technique for the analysis of respiratory electromyographic (EMG) signals. Its use has shown outperformance of amplitude-based estimators such as the root mean square (RMS) in the evaluation of respiratory EMG signals with cardiac noise and a high correlation with respiratory signals, allowing changes in respiratory muscle activity to be tracked. However, the relationship between the fSampEn response to a given muscle activation has not been investigated. The aim of this study was to analyze the nature of the fSampEn measurements that are produced as the EMG activity increases linearly. Simulated EMG signals were generated and increased linearly. The effect of the parameters r and the size of the moving window N of the fSampEn were evaluated and compared with those obtained using the RMS. The RMS showed a linear trend throughout the study. A non-linear, sigmoidal-like behavior was found when analyzing the EMG signals using the fSampEn. The lower the values of r, the higher the non-linearity observed in the fSampEn results. Greater moving windows reduced the variation produced by too small values of r.Clinical Relevance - Understanding the inherent non-linear relationship produced when using the fSampEn in EMG recordings will contribute to the improvement of the respiratory muscle activation assessment at different levels of respiratory effort in patients with respiratory conditions, particularly during the inspiratory phase © 2021 IEEE.

JTD Keywords: Breathing muscle, Breathing rate, Electromyography, Entropy, Heart, Human, Humans, Respiratory muscles, Respiratory rate

Many studies have focused on novel noninvasive techniques to monitor respiratory rate such as bioimpedance. We propose an algorithm to detect respiratory phases using wearable bioimpedance to compute time parameters like respiratory rate, inspiratory and expiratory times, and duty cycle. The proposed algorithm was compared with two other algorithms from literature designed to estimate the respiratory rate using physiological signals like bioimpedance. We acquired bioimpedance and airflow from 50 chronic obstructive pulmonary disease (COPD) patients during an inspiratory loading protocol. We compared performance of the algorithms by computing accuracy and mean average percentage error (MAPE) between the bioimpedance parameters and the reference parameters from airflow. We found similar performance for the three algorithms in terms of accuracy (>0.96) and respiratory time and rate errors (<3.42 %). However, the proposed algorithm showed lower MAPE in duty cycle (10.18 %), inspiratory time (10.65 %) and expiratory time (8.61 %). Furthermore, only the proposed algorithm kept the statistical differences in duty cycle between COPD severity levels that were observed using airflow. Accordingly, we suggest bioimpedance to monitor breathing pattern parameters in home situations.Clinical relevance - This study exhibits the suitability of wearable thoracic bioimpedance to detect respiratory phases and to compute accurate breathing pattern parameters. © 2021 IEEE.

JTD Keywords: algorithms, copd, signals, Algorithm, Algorithms, Bioimpedance, Breathing rate, Chronic obstructive lung disease, Electronic device, Human, Humans, Lung, Pulmonary disease, chronic obstructive, Respiratory rate, Wearable electronic devices