Aims Human influenza A virus (hIAV) infection is associated with important cardiovascular complications, although cardiac infection pathophysiology is poorly understood. We aimed to study the ability of hIAV of different pathogenicity to infect the mouse heart, and establish the relationship between the infective capacity and the associated in vivo, cellular and molecular alterations. Methods and results We evaluated lung and heart viral titres in mice infected with either one of several hIAV strains inoculated intranasally. 3D reconstructions of infected cardiac tissue were used to identify viral proteins inside mouse cardiomyocytes, Purkinje cells, and cardiac vessels. Viral replication was measured in mouse cultured cardiomyocytes. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were used to confirm infection and study underlying molecular alterations associated with the in vivo electrophysiological phenotype. Pathogenic and attenuated hIAV strains infected and replicated in cardiomyocytes, Purkinje cells, and hiPSC-CMs. The infection was also present in cardiac endothelial cells. Remarkably, lung viral titres did not statistically correlate with viral titres in the mouse heart. The highly pathogenic human recombinant virus PAmut showed faster replication, higher level of inflammatory cytokines in cardiac tissue and higher viral titres in cardiac HL-1 mouse cells and hiPSC-CMs compared with PB2mut-attenuated virus. Correspondingly, cardiac conduction alterations were especially pronounced in PAmut-infected mice, associated with high mortality rates, compared with PB2mut-infected animals. Consistently, connexin43 and NaV1.5 expression decreased acutely in hiPSC-CMs infected with PAmut virus. YEM1L protease also decreased more rapidly and to lower levels in PAmut-infected hiPSC-CMs compared with PB2mut-infected cells, consistent with mitochondrial dysfunction. Human IAV infection did not increase myocardial fibrosis at 4-day post-infection, although PAmut-infected mice showed an early increase in mRNAs expression of lysyl oxidase. Conclusion Human IAV can infect the heart and cardiac-specific conduction system, which may contribute to cardiac complications and premature death.

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.

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Unifying system identification and biomechanical formulations for the estimation of muscle, tendon and joint stiffness during human movement To cite this article: Christopher P Cop et al 2021 Prog. Biomed. Eng. 3 033002 View the article online for updates and enhancements.

Quantifying human joint stiffness in vivo during movement remains challenging. Well established stiffness estimation methods include system identification and the notion of quasi-stiffness, with experimental and conceptual limitations, respectively. Joint stiffness computation via biomechanical models is an emerging solution to overcome such limitations. However, these models make assumptions that hamper their generalization across muscle architectures. Here we present a stiffness formulation that considers the muscle's pennation angle, and its comparison to a simpler formulation that does not. Model-based stiffness estimates are evaluated against joint-perturbation-based system identification. Results on muscles with different pennation angle show that our formulation seamlessly adjusts the muscle-tendon units' stiffness depending on their architecture. At the joint level, our new model improved the stiffness estimations. Our study's relevance is the creation and validation of a modeling formulation that does not require joint perturbation. This will enable better estimations and understanding of stiffness properties and human movement.