Fangcang shelter hospitals were established in China during the coronavirus disease 2019 (COVID-19) pandemic as a countermeasure to stop the spread of the disease. To our knowledge, no research has been conducted on mental health problems among patients in Fangcang shelter hospitals. This study aimed to determine the prevalence and major influencing factors of anxiety and depressive symptoms among COVID-19 patients admitted to Fangcang shelter hospitals. From February 23, 2020, to February 26, 2020, we obtained sociodemographic and clinical characteristics information of COVID-19 patients in Jianghan Fangcang Shelter Hospital (Wuhan, China) and assessed their mental health status and sleep quality. Data were obtained with an online questionnaire. The questionnaire consisted of a set of items on demographic characteristics, a set of items on clinical characteristics, the Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Pittsburgh Sleep Quality Index. Three hundred seven COVID-19 patients who were admitted to Jianghan Fangcang Shelter Hospital participated in this study. The prevalence of anxiety and depressive symptoms were 18.6% and 13.4%, respectively. Poor sleep quality and having ≥ two current physical symptoms were independent risk factors for anxiety symptoms. Female sex, having a family member with confirmed COVID-19, and having ≥ two current physical symptoms were independent risk factors for depressive symptoms. Anxiety and depressive symptoms were found to be common among COVID-19 patients in Fangcang Shelter Hospital, with some patients being at high risk.

Lipids participate in many important biological functions through energy storage, material transport, signal transduction, and molecular recognition processes. Studies have reported that asthmatic patients have abnormal lipid metabolism. However, there are limited studies on the characterization of lipid metabolism in asthmatic patients by lipidomics. We characterized the plasma lipid profile of 28 healthy controls and 33 outpatients with asthma (18 mild, 15 moderate) by liquid chromatography mass spectrometry/mass spectrometry-based lipidomics. We determined 1338 individual lipid species in the plasma. Significant changes were identified in ten lipid species in asthmatic patients than in healthy controls (all P < 0.05). Phosphatidylethanolamine (PE) (18:1p/22:6), PE (20:0/18:1), PE (38:1), sphingomyelin (SM) (d18:1/18:1), and triglyceride (TG) (16:0/16:0/18:1) positively correlated with the severity of asthma (all P < 0.05). Phosphatidylinositol (PI) (16:0/20:4), TG (17:0/18:1/18:1), phosphatidylglycerol (PG) (44:0), ceramide (Cer) (d16:0/27:2), and lysophosphatidylcholine (LPC) (22:4) negatively correlated with the severity of asthma (all P < 0.05). Correlation analysis showed a significant correlation between all ten lipid species (all P < 0.05). From the area under the curve of the receiver operating characteristic curve analysis, PE (38:1) was the major lipid metabolite that distinguished asthmatic patients from healthy controls, and may be considered a potential lipid biomarker. PE (20:0/18:1) and TG (16:0/16:0/18:1) might be related to IgE levels in asthmatic patients. Our results indicated the presence of abnormal lipid metabolism, which correlated with the severity and IgE levels in asthmatic patients.

Background: Molecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective.Methods: Two independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools.Results: The WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells.Conclusion: Our study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.

Background: The exact risk assessment is crucial for the management of connective tissue disease-associated interstitial lung disease (CTD-ILD) patients. In the present study, we develop a nomogram to predict 3‑ and 5-year mortality by using machine learning approach and test the ILD-GAP model in Chinese CTD-ILD patients. Methods: CTD-ILD patients who were diagnosed and treated at the First Affiliated Hospital of Zhengzhou University were enrolled based on a prior well-designed criterion between February 2011 and July 2018. Cox regression with the least absolute shrinkage and selection operator (LASSO) was used to screen out the predictors and generate a nomogram. Internal validation was performed using bootstrap resampling. Then, the nomogram and ILD-GAP model were assessed via likelihood ratio testing, Harrell’s C index, integrated discrimination improvement (IDI), the net reclassification improvement (NRI) and decision curve analysis. Results: A total of 675 consecutive CTD-ILD patients were enrolled in this study, during the median follow-up period of 50 (interquartile range, 38–65) months, 158 patients died (mortality rate 23.4%). After feature selection, 9 variables were identified: age, rheumatoid arthritis, lung diffusing capacity for carbon monoxide, right ventricular diameter, right atrial area, honeycombing, immunosuppressive agents, aspartate transaminase and albumin. A predictive nomogram was generated by integrating these variables, which provided better mortality estimates than ILD-GAP model based on the likelihood ratio testing, Harrell’s C index (0.767 and 0.652 respectively) and calibration plots. Application of the nomogram resulted in an improved IDI (3- and 5-year, 0.137 and 0.136 respectively) and NRI (3- and 5-year, 0.294 and 0.325 respectively) compared with ILD-GAP model. In addition, the nomogram was more clinically useful revealed by decision curve analysis. Conclusions: The results from our study prove that the ILD-GAP model may exhibit an inapplicable role in predicting mortality risk in Chinese CTD-ILD patients. The nomogram we developed performed well in predicting 3‑ and 5-year mortality risk of Chinese CTD-ILD patients, but further studies and external validation will be required to determine the clinical usefulness of the nomogram.

Background: Long non-coding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) plays a crucial role in non-small cell lung cancer (NSCLC). Nonetheless, regulatory effects of PVT1 on functions of NSCLC cells remain blurry. Methods: Relative expression levels of PVT1, miR-551b and FGFR1 mRNA in tumor tissues and cells were examined employing quantitative real-time polymerase chain reaction (qRT-PCR); CCK-8 and BrdU assays were utilized for measuring cell viability and proliferation of H1299 and A549 cells; cell migration and invasion were detected deploying Transwell assay; dual-luciferase assay was used for the validation of binding sequence between PVT1 and miR-551b. FGFR1 expression in protein level was quantified employing Western blot. Results: PVT1 was highly expressed in NSCLC tissues and cell lines, whereas miR-551b expression was down-regulated. Overexpression of PVT1 potentiated viability, proliferation, migration and invasion of NSCLC cells while miR-551b inhibited the biological behaviors mentioned above. MiR-551b was predicted and then confirmed as a direct downstream target of PVT1. Meanwhile, a negative correlation was observed between PVT1 expression and miR-551b expression in NSCLC tissues. Besides, PVT1 could increase FGFR1 expression by repressing miR-551b expression. Conclusion: PVT1 promotes the proliferation, migration and invasion of NSCLC cells by indirectly mediating FGFR1 via targeting miR-551b.

Background: Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE. Methods: The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model. Results: A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736–0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA. Conclusion: Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.