Publications

This study explored the use of parasternal second intercostal space and lower intercostal space surface electromyogram (sEMG) and surface mechanomyogram (sMMG) recordings (sEMGpara and sMMGpara, and sEMGlic and sMMGlic, respectively) to assess neural respiratory drive (NRD), neuromechanical (NMC) and neuroventilatory (NVC) coupling, and mechanical efficiency (MEff) noninvasively in healthy subjects and chronic obstructive pulmonary disease (COPD) patients. sEMGpara, sMMGpara, sEMGlic, sMMGlic, mouth pressure (Pmo), and volume (Vi) were measured at rest, and during an inspiratory loading protocol, in 16 COPD patients (8 moderate and 8 severe) and 9 healthy subjects. Myographic signals were analyzed using fixed sample entropy and normalized to their largest values (fSEsEMGpara%max, fSEsMMGpara%max, fSEsEMGlic%max, and fSEsMMGlic%max). fSEsMMGpara%max, fSEsEMGpara%max, and fSEsEMGlic%max were significantly higher in COPD than in healthy participants at rest. Parasternal intercostal muscle NMC was significantly higher in healthy than in COPD participants at rest, but not during threshold loading. Pmo-derived NMC and MEff ratios were lower in severe patients than in mild patients or healthy subjects during threshold loading, but differences were not consistently significant. During resting breathing and threshold loading, Vi-derived NVC and MEff ratios were significantly lower in severe patients than in mild patients or healthy subjects. sMMG is a potential noninvasive alternative to sEMG for assessing NRD in COPD. The ratios of Pmo and Vi to sMMG and sEMG measurements provide wholly noninvasive NMC, NVC, and MEff indices that are sensitive to impaired respiratory mechanics in COPD and are therefore of potential value to assess disease severity in clinical practice. Author

JTD Keywords: biomedical measurement, chronic obstructive pulmonary disease, couplings, diaphragm, disease severity, efficiency, electromyography, exacerbations, healthy volunteers, inspiratory muscles, loading, mechanomyography, obstructive pulmonary-disease, pressure measurement, protocols, respiratory mechanics, respiratory muscles, responsiveness, spirometry, stimulation, volume measurement, At rests, Biomedical measurement, Biomedical measurements, Chronic obstructive pulmonary disease, Couplings, Disease severity, Efficiency ratio, Electromyography, Healthy subjects, Healthy volunteers, Loading, Mechanical efficiency, Mechanomyogram, Muscle, Muscles, Neural respiratory drive, Noninvasive medical procedures, Pressure measurement, Protocols, Pulmonary diseases, Surface electromyogram, Volume measurement

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, mavie16, mouse, 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, 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

Introduction: After a stroke, a wide range of deficits can occur with varying onset latencies. As a result, assessing impairment and recovery are enormous challenges in neurorehabilitation. Although several clinical scales are generally accepted, they are time-consuming, show high inter-rater variability, have low ecological validity, and are vulnerable to biases introduced by compensatory movements and action modifications. Alternative methods need to be developed for efficient and objective assessment. In this study, we explore the potential of computer-based body tracking systems and classification tools to estimate the motor impairment of the more affected arm in stroke patients. Methods: We present a method for estimating clinical scores from movement parameters that are extracted from kinematic data recorded during unsupervised computer-based rehabilitation sessions. We identify a number of kinematic descriptors that characterise the patients' hemiparesis (e.g., movement smoothness, work area), we implement a double-noise model and perform a multivariate regression using clinical data from 98 stroke patients who completed a total of 191 sessions with RGS. Results: Our results reveal a new digital biomarker of arm function, the Total Goal-Directed Movement (TGDM), which relates to the patients work area during the execution of goal-oriented reaching movements. The model's performance to estimate FM-UE scores reaches an accuracy of R-2: 0.38 with an error (sigma: 12.8). Next, we evaluate its reliability (r = 0.89 for test-retest), longitudinal external validity (95% true positive rate), sensitivity, and generalisation to other tasks that involve planar reaching movements (R-2: 0.39). The model achieves comparable accuracy also for the Chedoke Arm and Hand Activity Inventory (R-2: 0.40) and Barthel Index (R-2: 0.35). Conclusions: Our results highlight the clinical value of kinematic data collected during unsupervised goal-oriented motor training with the RGS combined with data science techniques, and provide new insight into factors underlying recovery and its biomarkers.

JTD Keywords: interactive feedback, motion classification, motion sensing, multivariate regression, posture monitoring, rehabilitation, stroke, Adult, Aged, Analytic method, Arm movement, Article, Barthel index, Brain hemorrhage, Cerebrovascular accident, Chedoke arm and hand activity inventory, Clinical protocol, Cognitive defect, Computer analysis, Controlled study, Convergent validity, Correlation coefficient, Disease severity, External validity, Female, Fugl meyer assessment for the upper extremity, Functional assessment, Functional status assessment, General health status assessment, Hemiparesis, Human, Interactive feedback, Ischemic stroke, Kinematics, Major clinical study, Male, Mini mental state examination, Motion classification, Motion sensing, Motor analog scale, Movement, Multivariate regression, Muscle function, Posture monitoring, Probability, Recovery, Rehabilitation, Reliability, Retrospective study, Stroke, Stroke patient, Test retest reliability, Therapy, Total goal directed movement, Upper extremities, Upper limb, Upper-limb, Wolf motor function test