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Background Coronavirus disease 2019 (COVID-19) is an emerging serious global health problem. Gastrointestinal symptoms are common in COVID-19 patients, and severe acute respiratory syndrome coronavirus 2 RNA has been detected in stool specimens. However, the relationship between the gut microbiome and disease remains to be established. Methods We conducted a cross-sectional study of 30 patients with COVID-19, 24 patients with influenza A(H1N1), and 30 matched healthy controls (HCs) to identify differences in the gut microbiota by 16S ribosomal RNA gene V3–V4 region sequencing. Results Compared with HCs, COVID-19 patients had significantly reduced bacterial diversity; a significantly higher relative abundance of opportunistic pathogens, such as Streptococcus , Rothia , Veillonella , and Actinomyces ; and a lower relative abundance of beneficial symbionts. Five biomarkers showed high accuracy for distinguishing COVID-19 patients from HCs with an area under the curve (AUC) up to 0.89. Patients with H1N1 displayed lower diversity and different overall microbial composition compared with COVID-19 patients. Seven biomarkers were selected to distinguish the 2 cohorts (AUC = 0.94). Conclusions The gut microbial signature of patients with COVID-19 was different from that of H1N1 patients and HCs. Our study suggests the potential value of the gut microbiota as a diagnostic biomarker and therapeutic target for COVID-19, but further validation is needed. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) infection is one of the leading causes of chronic liver diseases and hepatocellular carcinoma (HCC). Golgi protein 73 (GP73), a resident Golgi membrane protein, is a novel serum biomarker for the diagnosis of liver diseases and HCC. Although previous studies have demonstrated that HCV upregulates GP73 expression and GP73 promotes HCV secretion through its interaction with apolipoprotein E (ApoE), the exact mechanism underlying GP73 regulates HCV secretion remains unclear. In this study, we demonstrated that GP73 mediates the interaction of ApoE with HCV NS5A protein to promote HCV secretion. We revealed that GP73 is colocalized with HCV replication complex in infected-Huh7.5.1 cells. Further studies demonstrated that GP73 interacted with both NS5A and ApoE proteins. Furthermore, knockdown of GP73 significantly reduced the binding of NS5A with ApoE, and the production of virus particles in culture supernatant. Taken together, our studies revealed that GP73 promotes HCV secretion by directly mediating the interaction of ApoE with HCV replication complex through binding with HCV NS5A. [ABSTRACT FROM AUTHOR]

Aim: The objective of this study was to identify proteomic biomarkers specific for rheumatoid arthritis (RA) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) in combination with weak cationic exchange (WCX) magnetic beads. Methods: Serum samples from 50 patients with RA and 110 disease controls (50 SLE and 60 SS) and 51 healthy individuals were analyzed. The samples were randomly divided into a training set or test set to develop a diagnostic model for RA. Results: A total of 83 protein peaks were identified to be related with RA, in which four of the peaks with mass-charge ratio (m/z) at 8133.85, 5844.60, 13 541.3 and 14 029.0 were selected to establish a model for diagnosis of RA. This classification model could separate patients with RA from diseased and healthy controls with sensitivity of 84.0% and specificity of 92.5%, and its accuracy was confirmed in the blind testing set with high sensitivity and specificity of 80.0% and 93.3%, respectively. Conclusions: This study suggested that potential serum biomarkers for RA diagnosis could be discovered by MALDI-TOF-MS. The classification tree model set up in this study might be used as a novel diagnostic tool for RA. [ABSTRACT FROM AUTHOR]

Primary Sjögren's syndrome (pSS) is a systemic autoimmune disease that lacks sensitive and specific diagnostic methods. The aim of this study was to identify potential biomarkers specific for pSS and to establish a diagnostic model. Serum samples from patients with pSS, disease controls (DC, patients with systemic lupus erythematosus (SLE), rheumatoid arthritis (RA)), and healthy controls (HC)) were randomly divided into a training set (35 pSS, 50 DC, and 26 HC) and a testing set (25 pSS, 50 DC, and 25 HC). Weak cationic exchange (WCX) magnetic beads were used to differentially capture serum proteins prior to proteomic analysis. Proteomic mass spectra were generated by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). One hundred differential M/Z peaks associated with pSS were identified, and the m/z peaks at 8,133.85, 11,972.8, 2,220.81, and 4,837.66 were used to establish a diagnostic model for pSS. This diagnostic model was able to distinguish pSS from non-pSS controls with a sensitivity of 77.1 % and a specificity of 85.5 %, and its efficacy was confirmed in our blinded testing set with good sensitivity and specificity of 95.5 and 88 %, respectively. The results indicated that the proteomic fingerprinting model was effective in the diagnosis of pSS. [ABSTRACT FROM AUTHOR]

To improve treatment and reduce the mortality from cancer, a key task is to detect the disease as early as possible. To achieve this, many new technologies have been developed for biomarker discovery and validation. This review provides an overview of omics technologies in biomarker discovery and cancer detection, and highlights recent applications and future trends in cancer diagnostics. Although the present omic methods are not ready for immediate clinical use as diagnostic tools, it can be envisaged that simple, fast, robust, portable and cost-effective clinical diagnosis systems could be available in near future, for home and bedside use. [Copyright &y& Elsevier]