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Recent evidence has demonstrated that mucin 1 (MUC1) is involved in many pathological processes that occur in the lung. MUC1 is a transmembrane protein mainly expressed by epithelial and hematopoietic cells. It has a receptor-like structure, which can sense the external environment and activate intracellular signal transduction pathways through its cytoplasmic domain. The extracellular domain of MUC1 can be released to the external environment, thus acting as a decoy barrier to mucosal pathogens, as well as serving as a serum biomarker for the diagnosis and prognosis of several respiratory diseases such as lung cancer and interstitial lung diseases. Furthermore, bioactivated MUC1-cytoplasmic tail (CT) has been shown to act as an anti-inflammatory molecule in several airway infections and mediates the expression of anti-inflammatory genes in lung diseases such as chronic rhinosinusitis, chronic obstructive pulmonary disease and severe asthma. Bioactivated MUC1-CT has also been reported to interact with several effectors linked to cellular transformation, contributing to the progression of respiratory diseases such as lung cancer and pulmonary fibrosis. In this review, we summarise the current knowledge of MUC1 as a promising biomarker and drug target for lung disease.

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Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease characterized by an abnormal reepithelialisation, an excessive tissue remodelling and a progressive fibrosis within the alveolar wall that are not due to infection or cancer. Oxidative stress has been proposed as a key molecular process in pulmonary fibrosis development and different components of the redox system are altered in the cellular actors participating in lung fibrosis. To this respect, several activators of the antioxidant machinery and inhibitors of the oxidant species and pathways have been assayed in preclinical in vitro and in vivo models and in different clinical trials. This review discusses the role of oxidative stress in the development and progression of IPF and its underlying mechanisms as well as the evidence of oxidative stress in human IPF. Finally, we analyze the mechanism of action, the efficacy and the current status of different drugs developed to inhibit the oxidative stress as anti-fibrotic therapy in IPF.

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Paclitaxel is a microtubule-stabilizing chemotherapeutic agent approved for the treatment of ovarian, non-small cell lung, head, neck, and breast cancers. Despite its beneficial effects on cancer and widespread use, paclitaxel also damages healthy tissues, including the skin. However, the mechanisms that drive these skin adverse events are not clearly understood. In the present study, we demonstrated, by using both primary epidermal keratinocytes (NHEK) and a 3D epidermis model, that paclitaxel impairs different cellular processes: paclitaxel increased the release of IL-1α, IL-6, and IL-8 inflammatory cytokines, produced reactive oxygen species (ROS) release and apoptosis, and reduced the endothelial tube formation in the dermal microvascular endothelial cells (HDMEC). Some of the mechanisms driving these adverse skin events in vitro are mediated by the activation of toll-like receptor 4 (TLR-4), which phosphorylate transcription of nuclear factor kappa B (NF-κb). This is the first study analyzing paclitaxel effects on healthy human epidermal cells with an epidermis 3D model, and will help in understanding paclitaxel’s effects on the skin.

Cellular senescence is the arrest of normal cell division and is commonly associated with aging. The interest in the role of cellular senescence in lung diseases derives from the observation of markers of senescence in chronic obstructive pulmonary disease (COPD), pulmonary fibrosis (IPF), and pulmonary hypertension (PH). Accumulation of senescent cells and the senescence-associated secretory phenotype in the lung of aged patients may lead to mild persistent inflammation, which results in tissue damage. Oxidative stress due to environmental exposures such as cigarette smoke also promotes cellular senescence, together with additional forms of cellular stress such as mitochondrial dysfunction and endoplasmic reticulum stress. Growing recent evidence indicate that senescent cell phenotypes are observed in pulmonary artery smooth muscle cells and endothelial cells of patients with PH, contributing to pulmonary artery remodeling and PH development. In this review, we analyze the role of different senescence cell phenotypes contributing to the pulmonary artery remodeling process in different PH clinical entities. Different molecular pathway activation and cellular functions derived from senescence activation will be analyzed and discussed as promising targets to develop future senotherapies as promising treatments to attenuate pulmonary artery remodeling in PH.

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.