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Acute Respiratory Distress Syndrome (ARDS) is a condition that endangers the lives of many Intensive Care Unit patients through gradual reduction of lung function. Due to its heterogeneity, this condition has been difficult to diagnose and treat, although it has been the subject of continuous research, leading to the development of several tools for modeling disease progression on the one hand, and guidelines for diagnosis on the other, mainly the "Berlin Definition". This paper describes the development of a deep learning-based surrogate model of one such tool for modeling ARDS onset in a virtual patient: the Nottingham Physiology Simulator. The model-development process takes advantage of current machine learning and data-analysis techniques, as well as efficient hyperparameter-tuning methods, within a high-performance computing-enabled data science platform. The lightweight models developed through this process present comparable accuracy to the original simulator (per-parameter R2 > 0.90). The experimental process described herein serves as a proof of concept for the rapid development and dissemination of specialised diagnosis support systems based on pre-existing generalised mechanistic models, making use of supercomputing infrastructure for the development and testing processes and supported by open-source software for streamlined implementation in clinical routines. [ABSTRACT FROM AUTHOR]

Acute pancreatitis (AP) is a prevalent clinical condition of the digestive system, with a growing frequency each year. Approximately 20% of patients suffer from severe acute pancreatitis (SAP) with local consequences and multi-organ failure, putting a significant strain on patients' health insurance. According to reports, the lungs are particularly susceptible to SAP. Acute respiratory distress syndrome, a severe type of acute lung injury (ALI), is the primary cause of mortality among AP patients. Controlling the mortality associated with SAP requires an understanding of the etiology of AP-associated ALI, the discovery of biomarkers for the early detection of ALI, and the identification of potentially effective drug treatments. Exosomes are a class of extracellular vesicles with a diameter of 30–150 nm that are actively released into tissue fluids to mediate biological functions. Exosomes are laden with bioactive cargo, such as lipids, proteins, DNA, and RNA. During the initial stages of AP, acinar cell-derived exosomes suppress forkhead box protein O1 expression, resulting in M1 macrophage polarization. Similarly, macrophage-derived exosomes activate inflammatory pathways within endothelium or epithelial cells, promoting an inflammatory cascade response. On the other hand, a part of exosome cargo performs tissue repair and anti-inflammatory actions and inhibits the cytokine storm during AP. Other reviews have detailed the function of exosomes in the development of AP, chronic pancreatitis, and autoimmune pancreatitis. The discoveries involving exosomes at the intersection of AP and acute lung injury (ALI) are reviewed here. Furthermore, we discuss the therapeutic potential of exosomes in AP and associated ALI. With the continuous improvement of technological tools, the research on exosomes has gradually shifted from basic to clinical applications. Several exosome-specific non-coding RNAs and proteins can be used as novel molecular markers to assist in the diagnosis and prognosis of AP and associated ALI. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study was to evaluate High-Resolution CT (HRCT) findings in SARS-CoV-2-related ARDS survivors treated with prolonged low-dose methylprednisolone after hospital discharge. Methods: A total of 44 consecutive patients (M: 32, F: 12, average age: 64), hospitalised in our department from April to September 2020 for SARS-CoV-2-related ARDS, who had a postdischarge CT scan, were enrolled into this retrospective study. We reviewed the electronic medical charts to collect laboratory, clinical, and demographic data. The CT findings were evaluated and classified according to lung segmental distribution. The imaging findings were correlated with spirometry results and included ground glass opacities (GGOs), consolidations, reticulations, bronchiectasis/bronchiolectasis, linear bands, and loss of pulmonary volume. Results: Alterations in the pulmonary parenchyma were observed in 97.7% of patients at HRCT (median time lapse between ARDS diagnosis and HRCT: 2.8 months, range 0.9 to 6.7). The most common findings were linear bands (84%), followed by GGOs (75%), reticulations (34%), bronchiolectasis (32%), consolidations (30%), bronchiectasis (30%) and volume loss (25%). They had a symmetric distribution, and both lower lobes were the most affected areas. Conclusions: A reticular pattern with a posterior distribution was observed 3 months after discharge from severe COVID-19 pneumonia, and this differs from previously described postCOVID-19 fibrotic-like changes. We hypothesized that the systematic use of prolonged low-dose of corticosteroid could be the main reason of this different CT scan appearance. [ABSTRACT FROM AUTHOR]