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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]

Infectious diseases accompanied mankind throughout its existence. However, in the 20th century, with the implementation od mass vaccination, this problem was partially forgotten. It reappeared at the end of the 2019 with the COVID-19 pandemic. The diseases are associated with high mortality, the main causes of which are: respiratory failure, acute respiratory distress syndrome, thrombotic complications, etc. As many centuries ago, the key to fighting a pandemic is to diagnose patients with infections as quickly as possible, isolate them, and implement treatment procedures. In this paper we propose a Platform supporting medics in the fight against epidemic. Unlike alternative systems, the proposed IT Platform will ultimately cover all areas of fighting against COVID-19, from the diagnosis of infection, through treatment, to rehabilitation of post-disease complications. Like most clinical information systems, the Platform is based on Artificial Intelligence, in particular Federated Learning. Also, unlike known solutions, it uses all available historical data of the patient's health and information from real-time mobile diagnostics, using cellular communication and Internet of Things solutions. Such solutions could be helpful in fighting against any future mass infections. [ABSTRACT FROM AUTHOR]

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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]

The pathophysiology of acute respiratory distress syndrome (ARDS) includes the accumulation of protein-rich pulmonary edema in the air spaces and interstitial areas of the lung, variable degrees of epithelial injury, variable degrees of endothelial barrier disruption, transmigration of leukocytes, alongside impaired fluid and ion clearance. These pathophysiological features are different between patients contributing to substantial biological heterogeneity. In this context, it is perhaps unsurprising that a wide range of pharmacological interventions targeting these pathophysiological processes have failed to improve patient outcomes. In this manuscript, our goal is to provide a narrative summary of the potential methods to capture the underlying biological heterogeneity of ARDS and discuss how this information could inform future ARDS redefinitions. We discuss what biological tests are available to identify patients with any of the following predominant biological patterns: (1) epithelial and/or endothelial injury, (2) protein rich pulmonary edema and (3) systemic or within lung inflammatory responses. [ABSTRACT FROM AUTHOR]

Background: Dyspnea with bilateral pulmonary edema is common among patients in emergency departments (EDs) or intensive care units (ICUs). For the initial management of patients with this condition, cardiopulmonary edema (CPE) must be differentiated from acute respiratory distress syndrome (ARDS) in clinical settings. Brain natriuretic peptide (BNP) and N-terminal brain natriuretic peptide (NT-proBNP) are useful in distinguishing these conditions. However, current data about the use of these indexes are limited. Hence, we planned to perform a systematic review and meta-analysis to determine the accuracy of the two indexes for the diagnosis of CPE. Methods: We designed and registered a study protocol for a systematic review and meta-analysis. This study aims to determine the diagnostic accuracy of BNP and NT-proBNP based on the standards of the methodology of the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy and the Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies in reporting the findings of this review. We will search PubMed (MEDLINE), Cochrane Library, Embase, www.ClinicalTrials.gov, International Clinical Trials Registry Platform, and Google Scholar. Randomized controlled trials, cross-sectional studies, and observational cohort studies reporting the accuracy in diagnosing CPE among adult patients with dyspnea and bilateral pulmonary edema will be included in the analysis. There will be no limits regarding language and publication date for this review. Two reviewers will independently screen articles, extract data, evaluate for quality and bias using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), and use Grading of Recommendations Assessment, Development and Evaluation to summarize the strength of body of evidence. Then, a meta-analysis will be performed, and different statistical methods will be used to investigate heterogeneity among studies. A subgroup analysis of elderly patients with left ventricular dysfunction or chronic renal dysfunction will be performed. In the meta-analysis, a hierarchical summary receiver operating characteristic model or a bivariate model will be used in each index test, as appropriate. Discussion: A systematic review and meta-analysis of the accuracy of BNP and NT-proBNP for the diagnosis of CPE will be conducted. The result of this study can help clinicians to identify an appropriate initial treatment for patients with acute respiratory failure, including those with ARDS and CPE. To the best of our knowledge, this will be the first comprehensive systematic review focusing on ARDS management in a specific population. Systematic review registration: PROSPERO ID CRD42020201576 [ABSTRACT FROM AUTHOR]

The article discusses the role of lung ultrasound (LUS) in diagnosing acute respiratory distress syndrome (ARDS) and expresses concerns regarding the complexity of the newly introduced LUS-ARDS score. It suggests that LUS should be used for the diagnostic assessment of ARDS based on the distribution and intensity of specific ultrasound signs, emphasizing the need to distinguish between diagnostic and monitoring roles in LUS applications.

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]