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Abstract Background Lung cancer (LC) is one of the most devastating diseases worldwide, there is growing studies confirm the role of impaired lung function in LC susceptibility. Moreover, gut microbiota dysbiosis is associated with LC severity. Whether alterations in gut microbiota and metabolites are associated with long-term lung dysfunction in LC patients remain unclear. Our study aimed to analyze the risk factors in LC patients with impaired pulmonary function based on the characteristics of the gut microbiome and metabolites. Methods Fecal samples from 55 LC patients and 28 benign pulmonary nodules patients were collected. Pulmonary ventilation function was graded according to the American Thoracic Society/ European Respiratory Society (ATS/ERS) method. LC patients were divided into 3 groups, including 20 patients with normal lung ventilation, 23 patients with mild pulmonary ventilation dysfunction and 12 patients with moderate or above pulmonary ventilation dysfunction. The fecal samples were analyzed using 16 S rRNA gene amplicon sequencing and metabolomics. Results The gut microbiome composition between LC patients and benign pulmonary nodules patients presented clearly differences based on Partial Least Squares Discriminant Analysis (PLS-DA). Pulmonary ventilation function was positively correlated with LC tumor stage, the richness and diversity of the gut microbiota in LC patients with moderate or above pulmonary ventilation dysfunction increased significantly, characterized by increased abundance of Subdoligranulum and Romboutsia. The metabolomics analysis revealed 69 differential metabolites, which were mainly enriched in beta-Alanine metabolism, styrene degradation and pyrimidine metabolism pathway. The area under the curve (AUC) combining the gut microbiome and metabolites was 90% (95% CI: 79-100%), indicating that the two species and four metabolites might regarded as biomarkers to assess the prediction of LC patients with impaired pulmonary function. Conclusions Our results showed that microbiome and metabolomics analyses provide important candidate to be used as clinically diagnostic biomarkers and therapeutic targets related to lung cancer with impaired pulmonary function.

BackgroundLung cancer is the leading malignant disease and cause of cancer-related death worldwide. Most patients with lung cancer had insignificant early symptoms so that most of them were diagnosed at an advanced stage. In addition to factors such as smoking, pollution, lung microbiome and its metabolites play vital roles in the development of lung cancer. However, the interaction between lung microbiota and carcinogenesis is lack of systematically characterized and controversial. Therefore, the purpose of this study was to excavate the features of the lung microbiota and metabolites in patients and verify potential biomarkers for lung cancer diagnosis.MethodsLung tissue flushing solutions and bronchoalveolar lavage fluid samples came from patients with lung cancer and non-lung cancer. The composition and variations of the microbiota and metabolites in samples were explored using muti-omics technologies including 16S rRNA amplicon sequencing, metagenomics and metabolomics.ResultsThe metabolomics analysis indicated that 40 different metabolites, such as 9,10-DHOME, sphingosine, and cysteinyl-valine, were statistically significant between two groups (VIP > 1 and P < 0.05). These metabolites were significantly enriched into 11 signal pathways including sphingolipid, autophagy and apoptosis signaling pathway (P < 0.05). The analysis of lung microbiota showed that significant changes reflected the decrease of microbial diversity, changes of distribution of microbial taxa, and variability of the correlation networks of lung microbiota in lung cancer patients. In particular, we found that oral commensal microbiota and multiple probiotics might be connected with the occurrence and progression of lung cancer. Moreover, our study found 3 metabolites and 9 species with significantly differences, which might be regarded as the potential clinical diagnostic markers associated with lung cancer.ConclusionsLung microbiota and metabolites might play important roles in the pathogenesis of lung cancer, and the altered metabolites and microbiota might have the potential to be clinical diagnostic markers and therapeutic targets associated with lung cancer.

A welding robot is an advanced piece of machinery in industry, especially in the field of industrial automation. As a complex system, it has a high failure probability because it manipulates, controls and performs complicated objects and tasks. Therefore, more attention has recently focused on the reliability of a welding robot. Reliability is very important because its performance will affect the overall system safety directly. In this paper we attempt to make the welding robot to be reliable and stable by using a fuzzy Failure Mode Effects and Criticality Analysis (FMECA) method. It is applied to determine the key to reliability improvement and maintenance of the welding robot system according to a failure model of hazard classes. Firstly, Failure Mode Effects Analysis (FMEA) of the key parts is carried out to obtain the failure modes. Secondly, the evaluation rules of the failure modes are discussed for experts scoring. Thirdly, the criticality of each failure mode is analyzed by fuzzy comprehensive evaluations. The analysis results are consistent with an actual situation and can verify the effectiveness of the presented method. [ABSTRACT FROM AUTHOR]