Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes. METHODS: A total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study. Proteomics and metabolomics techniques were applied to identify potential biomarkers. An additional 29 COPD and 31 controls were enrolled for validation of the obtained proteomic signatures. Information on demographic, clinical manifestation, and blood test were collected. The ROC analyses were carried out to evaluate the diagnostic performance, and experimentally validated the final biomarkers on mild-to-moderate COPD. Next, molecular subtyping was performed using proteomics data. RESULTS: Theophylline, palmitoylethanolamide, hypoxanthine, and cadherin 5 (CDH5) could effectively diagnose advanced COPD with high accuracy (auROC = 0.98, sensitivity of 0.94, and specificity of 0.95). The performance of the diagnostic panel was superior to that of other single/combined results and blood tests. Proteome based stratification of COPD revealed three subtypes (I-III) related to different clinical outcomes and molecular feature: simplex COPD, COPD co-existing with bronchiectasis, and COPD largely co-existing with metabolic syndrome, respectively. Two discriminant models were established using the auROC of 0.96 (Principal Component Analysis, PCA) and 0.95 (the combination of RRM1 + SUPV3L1 + KRT78) in differentiating COPD and COPD with co-morbidities. Theophylline and CDH5 were exclusively elevated in advanced COPD but not in its mild form. CONCLUSIONS: This integrative multi-omics analysis provides a more comprehensive understanding of the molecular landscape of advanced COPD, which may suggest molecular targets for specialized therapy.

Gene Expression Trajectories from Normal Nonsmokers to COPD Smokers and Disease Progression Discriminant Modeling in Response to Cigarette Smoking.

Background: Cigarette smoking (CS) is considered to the predominant risk factor contributing to the etiopathogenesis of chronic obstructive pulmonary disease (COPD); meanwhile, genetic predisposition likely plays a role in determining disease susceptibility. Objectives: We aimed to investigate gene expression trajectories from normal nonsmokers to COPD smokers and disease progression discriminant modeling in response to cigarette smoking. Methods: Small airway epithelial samples of human with different smoking status using fiberoptic bronchoscopy and corresponding rat lung tissues following 0, 3, and 6 months of CS exposure were obtained. The expression of the significant overlapping genes between human and rats was confirmed in 16HBE cells, rat lung tissues, and human peripheral PBMC using qRT-PCR. Binary logistic regression analysis was carried out to establish discrimination models. Results: The integrated bioinformatic analysis of 8 human GEO datasets (293 individuals) and 9 rat transcriptome databases revealed 13 overlapping genes between humans and rats in response to smoking exposure during COPD progression. Of these, 5 genes (AKR1C3/Akr1c3, ERP27/Erp27, AHRR/Ahrr, KCNMB2/Kcnmb2, and MRC1/Mrc1) were consistently identified in both the human and rat and validated by qRT-PCR. Among them, ERP27/Erp27, KCNMB2/Kcnmb2, and MRC1/Mrc1 were newly identified. On the basis of the overlapping gene panel, discriminant models were established with the receiver operating characteristic curve (AUC) of 0.98 (AKR1C3/Akr1c3 + ERP27/Erp27) and 0.99 (AHRR/Ahrr + KCNMB2/Kcnmb2) in differentiating progressive COPD from normal nonsmokers. In addition, we also found that DEG obtained from each expression profile dataset was better than combined analysis as more genes could be identified. Conclusion: This study identified 5 DEG candidates of COPD progression in response to smoking and developed effective and convenient discriminant models that can accurately predict the disease progression.

Prediction of the Maturity of Greenhouse Grapes Based on Imaging Technology.

To predict grape maturity in solar greenhouses, a plant phenotype-monitoring platform (Phenofix, France) was used to obtain RGB images of grapes from expansion to maturity. Horizontal and longitudinal diameters, compactness, soluble solid content (SSC), titratable acid content, and the SSC/acid of grapes were measured and evaluated. The color values (R, G, B, H, S, and I) of the grape skin were determined and subjected to a back-propagation neural network algorithm (BPNN) to predict grape maturity. The results showed that the physical and chemical properties (PCP) of the three varieties of grapes changed significantly during the berry expansion stage and the color-changing maturity stage. According to the normalized rate of change of the PCP indicators, the ripening process of the three varieties of grapes could be divided into two stages: an immature stage (maturity coefficient Mc < 0.7) and a mature stage (after which color changes occurred) (0.7 ≤ Mc < 1). When predicting grape maturity based on the R, G, B, H, I, and S color values, the R, G, and I as well as G, H, and I performed well for Drunk Incense, Muscat Hamburg, and Xiang Yue grape maturity prediction. The GPI ranked in the top three (up to 0.87) when the above indicators were used in combination with BPNN to predict the grape Mc by single-factor and combined-factor analysis. The results showed that the prediction accuracy (RG and HI) of the two-factor combination was better for Drunk Incense, Muscat Hamburg, and Xiang Yue grapes (with recognition accuracies of 79.3%, 78.2%, and 79.4%, respectively), and all of the predictive values were higher than those of the single-factor predictions. Using a confusion matrix to compare the accuracy of the Mc's predictive ability under the two-factor combination method, the prediction accuracies were in the following order: Xiang Yue (88%) > Muscat Hamburg (81.3%) > Drunk Incense (76%). The results of this study provide an effective way to predict the ripeness of grapes in the greenhouse.

Proteomic profiling reveals a distinctive molecular signature for critically ill COVID-19 patients compared with asthma and chronic obstructive pulmonary disease.

OBJECTIVE: The mortality rate for critically ill COVID-19 cases was more than 80%. Nonetheless, research about the effect of common respiratory diseases on critically ill COVID-19 expression and outcomes is scarce. DESIGN: We performed proteomic analyses on airway mucus obtained by bronchoscopy from patients with severe COVID-19, or induced sputum from patients with chronic obstructive pulmonary disease (COPD), asthma, and healthy controls. RESULTS: Of the total identified and quantified proteins, 445 differentially expressed proteins (DEPs) were found in different comparison groups. In comparison with COPD, asthma, and controls, 11 proteins were uniquely present in COVID-19 patients. Apart from DEPs associated with COPD versus controls and asthma versus controls, there was a total of 59 DEPs specific to COVID-19 patients. Finally, the findings revealed that there were 8 overlapping proteins in COVID-19 patients, including C9, FGB, FGG, PRTN3, HBB, HBA1, IGLV3-19, and COTL1. Functional analyses revealed that most of them were associated with complement and coagulation cascades, platelet activation, or iron metabolism, and anemia-related pathways. CONCLUSIONS: This study provides fundamental data for identifying COVID-19-specific proteomic changes in comparison with COPD and asthma, which may suggest molecular targets for specialized therapy.

ECMO combined with prone positioning strategies in COVID-19 respiratory distress syndrome.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has already become a global pandemic as a public health emergency of international concern. Previous evidence from similar patient populations proved that carefully selected patients with severe ARDS who did not benefit from conventional treatment might be successfully supported with Veno-Venous extracorporeal membrane oxygenation (V-V ECMO). We now share the case reports of COVID-19 patients with ECMO combined prone position strategies.

[Indoor simulation on dew formation on plant leaves].

Dew forming on plant leaves through water condensation plays a significant ecological role in arid and semi-arid areas as an ignorable fraction of water resources. In this study, an artificial intelligent climate chamber and an automatic temperature-control system for leaves were implemented to regulate the ambient temperature, the leaf surface temperature and the leaf inclination for dew formation. The impact of leaf inclination, ambient temperature and dew point-leaf temperature depression on the rate and quantity of dew accumulation on leaf surface were analyzed. The results indicated that the accumulation rate and the maximum volume of dew on leaves decreased with increasing the leaf inclination while increased with the increment of dew point-leaf temperature depression, ambient temperature and relative humidity. Under the horizontal configuration, dew accumulated linearly on leaf surface over time until the maximum volume (0.80 mm) was reached. However, dew would fall down after reaching the maximum volume when the leaf inclination existed (45 degrees or 90 degrees), significantly slowing down the accumulative rate, and the zigzag pattern for the dynamic of dew accumulation appeared.