N-acetylcysteine Reduces Inflammasome Activation Induced by SARS-CoV-2 Proteins In Vitro.

Inflammasome activation is one of the first steps in initiating innate immune responses. In this work, we studied the activation of inflammasomes in the airways of critically ill COVID-19 patients and the effects of N-acetylcysteine (NAC) on inflammasomes. Tracheal biopsies were obtained from critically ill patients without COVID-19 and no respiratory disease (control, n = 32), SARS-CoV-2 B.1 variant (n = 31), and B.1.1.7 VOC alpha variant (n = 20) patients. Gene expression and protein expression were measured by RT-qPCR and immunohistochemistry. Macrophages and bronchial epithelial cells were stimulated with different S, E, M, and N SARS-CoV-2 recombinant proteins in the presence or absence of NAC. NLRP3 inflammasome complex was over-expressed and activated in the COVID-19 B.1.1.7 VOC variant and associated with systemic inflammation and 28-day mortality. TLR2/MyD88 and redox NOX4/Nrf2 ratio were also over-expressed in the COVID-19 B.1.1.7 VOC variant. The combination of S-E-M SARS-CoV-2 recombinant proteins increased cytokine release in macrophages and bronchial epithelial cells through the activation of TLR2. NAC inhibited SARS-CoV-2 mosaic (S-E-M)-induced cytokine release and inflammasome activation. In summary, inflammasome is over-activated in severe COVID-19 and increased in B.1.1.7 VOC variant. In addition, NAC can reduce inflammasome activation induced by SARS-CoV-2 in vitro, which may be of potential translational value in COVID-19 patients.

Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.

Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (SRF). The aim of our study was to provide evidence on predictors of poor outcome in patients with COVID-19 treated with tocilizumab, using machine learning (ML) techniques. We conducted a retrospective study, analyzing the clinical, laboratory and sociodemographic data of patients admitted for severe COVID-19 with SRF, treated with tocilizumab. The extreme gradient boost (XGB) method had the highest balanced accuracy (93.16%). The factors associated with a worse outcome of tocilizumab use in terms of mortality were: baseline situation at the start of tocilizumab treatment requiring invasive mechanical ventilation (IMV), elevated ferritin, lactate dehydrogenase (LDH) and glutamate-pyruvate transaminase (GPT), lymphopenia, and low PaFi [ratio between arterial oxygen pressure and inspired oxygen fraction (PaO2/FiO2)] values. The factors associated with a worse outcome of tocilizumab use in terms of hospital stay were: baseline situation at the start of tocilizumab treatment requiring IMV or supplemental oxygen, elevated levels of ferritin, glutamate-oxaloacetate transaminase (GOT), GPT, C-reactive protein (CRP), LDH, lymphopenia, and low PaFi values. In our study focused on patients with severe COVID-19 treated with tocilizumab, the factors that were weighted most strongly in predicting worse clinical outcome were baseline status at the start of tocilizumab treatment requiring IMV and hyperferritinemia.

eXtreme Gradient Boosting-based method to classify patients with COVID-19.

Different demographic, clinical and laboratory variables have been related to the severity and mortality following SARS-CoV-2 infection. Most studies applied traditional statistical methods and in some cases combined with a machine learning (ML) method. This is the first study to date to comparatively analyze five ML methods to select the one that most closely predicts mortality in patients admitted with COVID-19. The aim of this single-center observational study is to classify, based on different types of variables, adult patients with COVID-19 at increased risk of mortality. SARS-CoV-2 infection was defined by a positive reverse transcriptase PCR. A total of 203 patients were admitted between March 15 and June 15, 2020 to a tertiary hospital. Data were extracted from the electronic medical record. Four supervised ML algorithms (k-nearest neighbors (KNN), decision tree (DT), Gaussian naïve Bayes (GNB) and support vector machine (SVM)) were compared with the eXtreme Gradient Boosting (XGB) method proposed to have excellent scalability and high running speed, among other qualities. The results indicate that the XGB method has the best prediction accuracy (92%), high precision (>0.92) and high recall (>0.92). The KNN, SVM and DT approaches present moderate prediction accuracy (>80%), moderate recall (>0.80) and moderate precision (>0.80). The GNB algorithm shows relatively low classification performance. The variables with the greatest weight in predicting mortality were C reactive protein, procalcitonin, glutamyl oxaloacetic transaminase, glutamyl pyruvic transaminase, neutrophils, D-dimer, creatinine, lactic acid, ferritin, days of non-invasive ventilation, septic shock and age. Based on these results, XGB is a solid candidate for correct classification of patients with COVID-19.

Role of JAK/STAT in Interstitial Lung Diseases; Molecular and Cellular Mechanisms.

Interstitial lung diseases (ILDs) comprise different fibrotic lung disorders characterized by cellular proliferation, interstitial inflammation, and fibrosis. The JAK/STAT molecular pathway is activated under the interaction of a broad number of profibrotic/pro-inflammatory cytokines, such as IL-6, IL-11, and IL-13, among others, which are increased in different ILDs. Similarly, several growth factors over-expressed in ILDs, such as platelet-derived growth factor (PDGF), transforming growth factor ß1 (TGF-ß1), and fibroblast growth factor (FGF) activate JAK/STAT by canonical or non-canonical pathways, which indicates a predominant role of JAK/STAT in ILDs. Between the different JAK/STAT isoforms, it appears that JAK2/STAT3 are predominant, initiating cellular changes observed in ILDs. This review analyzes the expression and distribution of different JAK/STAT isoforms in ILDs lung tissue and different cell types related to ILDs, such as lung fibroblasts and alveolar epithelial type II cells and analyzes JAK/STAT activation. The effect of JAK/STAT phosphorylation on cellular fibrotic processes, such as proliferation, senescence, autophagy, endoplasmic reticulum stress, or epithelial/fibroblast to mesenchymal transition will be described. The small molecules directed to inhibit JAK/STAT activation were assayed in vitro and in in vivo models of pulmonary fibrosis, and different JAK inhibitors are currently approved for myeloproliferative disorders. Recent evidence indicates that JAK inhibitors or monoclonal antibodies directed to block IL-6 are used as compassionate use to attenuate the excessive inflammation and lung fibrosis related to SARS-CoV-2 virus. These altogether indicate that JAK/STAT pathway is an attractive target to be proven in future clinical trials of lung fibrotic disorders.