In December 2019, cases of pneumonia appeared in Wuhan, China. The etiology of these infections was a novel coronavirus (2019-nCoV),^1,2 possibly connected to zoonotic or environmental exposure from the seafood market in Wuhan. Human-to-human transmission has accounted for most of the infections, including among health care workers.^3,4 The virus has spread to different parts of China and at least 26 other countries.¹ A high number of men have been infected, and the reported mortality rate has been approximately 2%, which is lower than that reported from other coronavirus epidemics including severe acute respiratory syndrome (SARS; mortality rate, >40% in patients aged >60 years)⁵ and Middle East respiratory syndrome (MERS; mortality rate, 30%).⁶ However, little is known about the clinical manifestations of 2019-nCoV in healthy populations or cases outside Wuhan. We report early clinical features of 13 patients with confirmed 2019-nCoV infection admitted to hospitals in Beijing.

Lung macrophages play important roles in the maintenance of homeostasis, pathogen clearance and immune regulation. The different types of pulmonary macrophages and their roles in lung diseases have attracted attention in recent years. Alveolar macrophages (AMs), including tissue-resident alveolar macrophages (TR-AMs) and monocyte-derived alveolar macrophages (Mo-AMs), as well as interstitial macrophages (IMs) are the major macrophage populations in the lung and have unique characteristics in both steady-state conditions and disease states. The different characteristics of these three types of macrophages determine the different roles they play in the development of disease. Therefore, it is important to fully understand the similarities and differences among these three types of macrophages for the study of lung diseases. In this review, we will discuss the physiological characteristics and unique functions of these three types of macrophages in acute and chronic lung diseases. We will also discuss possible methods to target macrophages in lung diseases.

Acute lung injury (ALI) is a pulmonary disorder, which can result in fibrosis of the lung tissues. Recently, mesenchymal stem cell (MSC) has become a novel therapeutic method for ALI. However, the potential mechanism by which MSC regulates the progression of ALI remains blurry. The present study focused on investigating the mechanism underneath MSC-reversed lung injury and fibrosis. At first, we determined that coculture with MSC led to the inactivation of NF-κB signaling and therefore suppressed hedgehog pathway in LPS-treated MLE-12 cells. Besides, we confirmed that MSC-exosomes were responsible for the inhibition of EMT process in LPS-treated MLE-12 cells through transmitting miRNAs. Mechanism investigation revealed that MSC-exosome transmitted miR-182-5p and miR-23a-3p into LPS-treated MLE-12 cells to, respectively, target Ikbkb and Usp5. Of note, Usp5 interacted with IKKβ to hamper IKKβ ubiquitination. Moreover, co-inhibition of miR-182-5p and miR-23a-3p offset the suppression of MSC on EMT process in LPS-treated MLE-12 cells as well as in LPS-injured lungs of mice. Besides, the retarding effect of MSC on p65 nuclear translocation was also counteracted after co-inhibiting miR-182-5p and miR-23a-3p, both in vitro and in vivo. In summary, MSC-exosome transmitted miR-23a-3p and miR-182-5p reversed the progression of LPS-induced lung injury and fibrosis through inhibiting NF-κB and hedgehog pathways via silencing Ikbkb and destabilizing IKKβ.

A wearable self-powered active sensor for respiration and healthcare monitoring was fabricated based on a flexible piezoelectric nanogenerator. An electrospinning poly(vinylidene fluoride) thin film on silicone substrate was polarized to fabricate the flexible nanogenerator and its electrical property was measured. When periodically stretched by a linear motor, the flexible piezoelectric nanogenerator generated an output open-circuit voltage and short-circuit current of up to 1.5 V and 400 nA, respectively. Through integration with an elastic bandage, a wearable self-powered sensor was fabricated and used to monitor human respiration, subtle muscle movement, and voice recognition. As respiration proceeded, the electrical output signals of the sensor corresponded to the signals measured by a physiological signal recording system with good reliability and feasibility. This self-powered, wearable active sensor has significant potential for applications in pulmonary function evaluation, respiratory monitoring, and detection of gesture and vocal cord vibration for the personal healthcare monitoring of disabled or paralyzed patients.

Background: Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective: The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods: In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results: Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions: The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Background: Although studies have demonstrated the value of the triglyceride–glucose (TyG) index for cardiovascular disease (CVD) and cardiovascular mortality, however, few studies have shown that the TyG index is associated with all-cause or CVD mortality in young patients with diabetes. This study aimed to investigate the association between the TyG index and all-cause and CVD mortality in young patients with diabetes in the United States. Methods: Our study recruited 2440 young patients with diabetes from the National Health and Nutrition Examination Survey (NHANES) 2001–2018. Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31, 2019. Cox regression modeling was used to investigate the association between TyG index and mortality in young patients with diabetes. The nonlinear association between TyG index and mortality was analyzed using restricted cubic splines (RCS), and a two-segment Cox proportional risk model was constructed for both sides of the inflection point. Results: During a median follow-up period of 8.2 years, 332 deaths from all causes and 82 deaths from cardiovascular disease were observed. Based on the RCS, the TyG index was found to have a U-shaped association with all-cause and CVD mortality in young patients with diabetes, with threshold values of 9.18 and 9.16, respectively. When the TyG index was below the threshold value (TyG index < 9.18 in all-cause mortality and < 9.16 in CVD mortality), its association with all-cause and CVD mortality was not significant. When the TyG index was above the threshold (TyG index ≥ 9.18 in all-cause mortality and ≥ 9.16 in CVD mortality), it showed a significant positive association with all-cause mortality and CVD mortality (HR 1.77, 95% CI 1.05–2.96 for all-cause mortality and HR 2.38, 95% CI 1.05–5.38 for CVD mortality). Conclusion: Our results suggest a U-shaped association between TyG index and all-cause and CVD mortality among young patients with diabetes in the United States, with threshold values of 9.18 and 9.16 for CVD and all-cause mortality, respectively.