Quick, simple and accurate diagnosis of suspected COVID-19 is very important for the screening and therapy of patients. Although several methods were performed in clinical practice, however, the IgM and IgG diagnostic value evaluation was little performed. 57 suspected COVID-19 infection patients were enrolled in our study. 24 patients with positive and 33 patients with negative nucleic acid test. The positive rate of COVID-19 nucleic acid was 42.10%. The positive detection rate of combination of IgM and IgG for patients with COVID-19 negative and positive nucleic acid test was 72.73% and 87.50%. The results were significantly higher than the nucleic acid or IgM, IgG single detection. hsCRP in the COVID-19 nucleic acid negative group showed significantly higher than the positive groups (P=0.0298).AST in the COVID-19 IgM negative group showed significantly lower than the positive groups (P=0.0365). We provided a quick, simple, accurate aided detection method for the suspected patients and on-site screening in close contact with the population.

Background Recently, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) technology has been applied to the exploration of biomarkers for early cancer diagnosis, but more effort is required to identify a single sensitive and specific biomarker. For early diagnosis, a proteomic profile is the gold standard, but inconvenient for clinical use since the profile peaks are quantitative. It would therefore be helpful to find a minimized profile, comprising fewer peaks than the original using an existing algorithm and compare it with other traditional statistical methods. Methods In the present study, principal component analysis (PCA) in the ClinProt-Tools of MALDI-TOF MS was used to establish a mini-optimized proteomic profile from gastric cancer patients and healthy controls, and the result was compared with t-test and Flexanalysis software. Results Eight peaks were selected as the mini-optimized proteomic profile to help differentiate between gastric cancer patients and healthy controls. The peaks at m/z 4212 were regarded as the most important peak by the PCA algorithm. The peaks at m/z 1866 and 2863 were identified as deriving from complement component C3 and apolipoprotein A1, respectively. Conclusions PCA enabled us to identify a mini-optimized profile consisting of significantly differentiating peaks and offered the clue for further research.

No abstract

Background: Nucleic acid detection and CT scanning have been reported in COVID-19 diagnosis. Here, we aimed to investigate the clinical significance of IgM and IgG testing for the diagnosis of highly suspected COVID-19.Methods: A total of 63 patients with suspected COVID-19 were observed, 57 of whom were enrolled (24 males and 33 females). The selection was based on the diagnosis and treatment protocol for COVID-19 (trial Sixth Edition) released by the National Health Commission of the People's Republic of China. Patients were divided into positive and negative groups according to the first nucleic acid results from pharyngeal swab tests. Routine blood tests were detected on the second day after each patient was hospitalized. The remaining serum samples were used for detection of novel coronavirus-specific IgM/IgG antibodies.Results: The rate of COVID-19 nucleic acid positivity was 42.10%. The positive detection rates with a combination of IgM and IgG testing for patients with COVID-19 negative and positive nucleic acid test results were 72.73 and 87.50%, respectively.Conclusions: We report a rapid, simple, and accurate detection method for patients with suspected COVID-19 and for on-site screening for close contacts within the population. IgM and IgG antibody detection can identify COVID-19 after a negative nucleic acid test. Diagnostic accuracy of COVID-19 might be improved by nucleic acid testing in patients with a history of epidemic disease or with clinical symptoms, as well as CT scans when necessary, and serum-specific IgM and IgG antibody testing after the window period.

Non-obstructive azoospermia (NOA) is one of the most important causes of male infertility. It is mainly characterized by the absence of sperm in semen repeatedly or the number of sperm is small and not fully developed. At present, its pathogenesis remains largely unknown. The goal of this study is to identify hub genes that might affect biomarkers related to spermatogenesis. Using the clinically significant transcriptome and single-cell sequencing data sets on the Gene Expression Omnibus (GEO) database, we identified candidate hub genes related to spermatogenesis. Based on them, we performed Gene Ontology (GO) functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, protein-protein interaction (PPI) network analysis, principal component analysis (PCA), cell cluster analysis, and pseudo-chronological analysis. We identified a total of 430 differentially expressed genes, of which three have not been reported related to spermatogenesis (C22orf23, TSACC, and TTC25), and the expression of these three hub genes was different in each type of sperm cells. The results of the pseudo-chronological analysis of the three hub genes indicated that TTC25 was in a low expression state during the whole process of sperm development, while the expression of C22orf23 had two fluctuations in the differentiating spermatogonia and late primary spermatocyte stages, and TSACC showed an upward trend from the spermatogonial stem cell stage to the spermatogenesis stage. Our research found that the three hub genes were different in the trajectory of sperm development, indicating that they might play important roles in different sperm cells. This result is of great significance for revealing the pathogenic mechanism of NOA and further research.