Staff member

The extracellular matrix (ECM) of the lung is a filamentous network composed mainly of collagens, elastin, and proteoglycans that provides structural and physical support to its populating cells. Proliferation, migration and overall behaviour of those cells is greatly determined by micromechanical queues provided by the ECM. Lung fibrosis displays an aberrant increased deposition of ECM which likely changes filament organization and stiffens the ECM, thus upregulating the profibrotic profile of pulmonary cells. We have previously used AFM to assess changes in the Young’s Modulus (E) of the ECM in the lung. Here, we perform further ECM topographical, mechanical and viscoelastic analysis at the micro- and nano-scale throughout fibrosis development. Furthermore, we provide nanoscale correlations between topographical and elastic properties of the ECM fibres. Firstly, we identify a softening of the ECM after rats are instilled with media associated with recovery of mechanical homeostasis, which is hindered in bleomycin-instilled lungs. Moreover, we find opposite correlations between fibre stiffness and roughness in PBS- vs bleomycin-treated lung. Our findings suggest that changes in ECM nanoscale organization take place at different stages of fibrosis, with the potential to help identify pharmacological targets to hinder its progression.

JTD Keywords: atomic force microscopy, cells, deposition, extracellular matrix, idiopathic pulmonary fibrosis, mechanisms, mechanosensing, membranes, micromechanical properties, pathogenesis, stiffness, tissues, viscoelasticity, Extracellular matrix, Induced lung fibrosis, Mechanosensing

Studies based on the cardiac and respiratory system have allowed a better knowledge of their behavior to contribute with the diagnosis and treatment of diseases associated with them. The main goal of this project was to analyze the behavior of the cardiorespiratory system in healthy subjects, depending on the body position. The electrocardiography and respiratory flow signals were recorded in two positions, supine and sitting. Each signal was analyzed considering sliding windows of 30 s, with and overlapping of 50%. Temporal and spectral features were extracted from each signal. A total of 187 features were extracted for each window. According to statistical analysis, 148 features showed significant differences when comparing the position of the subject. Afterwards, the classifications methods based on decision trees, k-nearest neighbor and support vector machines were applied to identify the best classification model. The most advantageous performance model was obtained with a linear support vector machine method, with an accuracy of 99.5%, a sensitivity of 99.2% and a specificity of 99.6%. In conclusion, we have observed that the position of the body (supine or sitting) could modulate the cardiac and respiratory system response. New statistical models might provide new tools to analyze the behavior of these systems and the cardiorespiratory interaction complexity.

JTD Keywords: Cardiac dynamics, Respiratory dynamics, Statistical models, Supine and sitting posture