Publications

The issue of failed weaning is a critical concern in the intensive care unit (ICU) setting. This scenario occurs when a patient experiences difficulty maintaining spontaneous breathing and ensuring a patent airway within the first 48 hours after the withdrawal of mechanical ventilation. Approximately 20% of ICU patients experience this phenomenon, which has severe repercussions on their health. It also has a substantial impact on clinical evolution and mortality, which can increase by 25% to 50%. To address this issue, we propose a medical support system that uses a convolutional neural network (CNN) to assess a patient's suitability for disconnection from a mechanical ventilator after a spontaneous breathing test (SBT). During SBT, respiratory flow and electrocardiographic activity were recorded and after processed using time-frequency analysis (TFA) techniques. Two CNN architectures were evaluated in this study: one based on ResNet50, with parameters tuned using a Bayesian optimization algorithm, and another CNN designed from scratch, with its structure also adapted using a Bayesian optimization algorithm. The WEANDB database was used to train and evaluate both models. The results showed remarkable performance, with an average accuracy 98 +/- 1.8% when using CNN from scratch. This model has significant implications for the ICU because it provides a reliable tool to enhance patient care by assisting clinicians in making timely and accurate decisions regarding weaning. This can potentially reduce the adverse outcomes associated with failed weaning events.

JTD Keywords: Bayesian optimization algorithm (boa, Continuous wavelet transform (cwt), Convolutional, Extubation, Failur, Intensive-care-unit, Neural network (cnn) from scratch, Respiratory-distress-syndrome, Time-frequency analysis (tfa), Weaning

A fundamental task of the respiratory system is to operate as a mechanical gas pump ensuring that fresh air gets in close contact with the blood circulating through the lung capillaries to achieve O2 and CO2 exchange. To ventilate the lungs, the respiratory muscles provide the pressure required to overcome the viscoelastic mechanical load of the respiratory system. From a mechanical viewpoint, the most relevant respiratory system properties are the resistance of the airways (R aw), and the compliance of the lung tissue (C L) and chest wall (C CW). Both airflow and lung volume changes in spontaneous breathing and mechanical ventilation are determined by applying the fundamental mechanical laws to the relationships between the pressures inside the respiratory system (at the airway opening, alveolar, pleural, and muscular) and R aw, C L, and C CW. These relationships also are the basis of the different methods available to measure respiratory mechanics during spontaneous and artificial ventilation. Whereas a simple mechanical model (R aw, C L, and C CW) describes the basic understanding of ventilation mechanics, more complex concepts (nonlinearity, inhomogeneous ventilation, or viscoelasticity) should be employed to better describe and measure ventilation mechanics in patients.Thieme. All rights reserved.

JTD Keywords: airway-resistance, alveolar, compliance, dilution, elastance, flow, inhomogeneous ventilation, input impedance, lung-volume, mechanical ventilation, monitoring, pendelluft, pleural pressure, respiratory-distress-syndrome, viscoelasticity, Chest-wall mechanics, Resistance

The use of physiomimetic decellularized extracellular matrix-derived hydrogels is attracting interest since they can modulate the therapeutic capacity of numerous cell types, including mesenchymal stromal cells (MSCs). Remarkably, extracellular vesicles (EVs) derived from MSCs display similar functions as their parental cells, mitigating tissue damage in lung diseases. However, recent data have shown that ECM-derived hydrogels could release other resident vesicles similar to EVs. Here, we aim to better understand the contribution of EVs and ECM-vesicles released from MSCs and/or lung-derived hydrogel (L-HG) in lung repair by using an in vitro lung injury model. L-HG derived-vesicles and MSCs EVs cultured either in L-HG or conventional plates were isolated and characterized. The therapeutic capacity of vesicles obtained from each experimental condition was tested by using an alveolar epithelial wound-healing assay. The number of ECM-vesicles released from acellular L-HG was 10-fold greater than EVs from conventional MSCs cell culture revealing that L-HG is an important source of bioactive vesicles. MSCs-derived EVs and L-HG vesicles have similar therapeutic capacity in lung repair. However, when wound closure rate was normalized by total proteins, the MSCs-derived EVs shows higher therapeutic potential to those released by L-HG. The EVs released from L-HG must be considered when HG is used as substrate for cell culture and EVs isolation.

JTD Keywords: cell, extracellular vesicles, hydrogel, lung epithelial cells, lung repair, mesenchymal stem cells, Extracellular matrix, Extracellular vesicles, Hydrogel, Lung epithelial cells, Lung repair, Mesenchymal stem cells, Respiratory-distress-syndrome