Background Blood transcriptomics can be used for confirmation of tuberculosis diagnosis or sputumless triage, and a comparison of their practical diagnostic accuracy is needed to assess their usefulness. In this study, we investigated potential biomarkers to improve our understanding of the pathogenesis of active pulmonary tuberculosis (PTB) using bioinformatics methods. Methods Differentially expressed genes (DEGs) were analyzed between PTB and healthy controls (HCs) based on two microarray datasets. Pathways and functional annotation of DEGs were identified and ten hub genes were selected. They were further analyzed and selected, then verified with an independent sample set. Finally, their diagnostic power was further evaluated between PTB and HCs or other diseases. Results 62 DEGs mostly related to type I IFN pathway, IFN-γ-mediated pathway, etc. in GO term and immune process, and especially RIG-I-like receptor pathway were acquired. Among them, OAS1, IFIT1 and IFIT3 were upregulated and were the main risk factors for predicting PTB, with adjusted risk ratios of 1.36, 3.10, and 1.32, respectively. These results further verified that peripheral blood mRNA expression levels of OAS1, IFIT1 and IFIT3 were significantly higher in PTB patients than HCs (all P < 0.01). The performance of a combination of these three genes (three-gene set) had exceeded that of all pairwise combinations of them in discriminating TB from HCs, with mean AUC reaching as high as 0.975 with a sensitivity of 94.4% and a specificity of 100%. The good discernibility capacity was evaluated d via 7 independent datasets with an AUC of 0.902, as well as mean sensitivity of 87.9% and mean specificity of 90.2%. In regards to discriminating PTB from other diseases (i.e., initially considered to be possible TB, but rejected in differential diagnosis), the three-gene set equally exhibited an overall strong ability to separate PTB from other diseases with an AUC of 0.999 (sensitivity: 99.0%; specificity: 100%) in the training set, and 0.974 with a sensitivity of 96.4% and a specificity of 98.6% in the test set. Conclusion The described commonalities and unique signatures in the blood profiles of PTB and the other control samples have considerable implications for PTB biosignature design and future diagnosis, and provide insights into the biological processes underlying PTB.

Background: Hematopoietic stem cell transplantation (HSCT) is an effective method for the treatment of hematological malignancies, severe aplastic anemia, and myelodysplastic syndromes. The most common infectious complication after HSCT is cytomegalovirus (CMV) infection. The purpose of this study was to analyze the status of research related to CMV infection after HSCT by conducting a literature search for CMV, hematopoietic, and stem cell transplantation. Methods:The Science Citation Index Expanded (SCI-E) database in the Web of Science Core Collection (WOSCC) was used as the target database for our literature search. The subject search terms were CMV, hematopoietic, and stem cell transplantation, with the logical operation 'AND'. The search date range was from 1900 to June 15, 2021. We used CiteSpace software to analyze the literature. The analysis included: the annual change in the number of publications, the annual change in the number of references cited, the distribution of countries, the distribution of institutions, the distribution of journals, the distribution of authors, and the use of keywords.Results: A total of 1,476 relevant documents were retrieved. The top 5 countries for number of

BackgroundInhibitor of apoptosis‐stimulating protein of p53 (iASPP) is an evolutionarily conserved p53 inhibitor. Mechanistically, iASPP can accelerate tumorigenesis by inhibiting the transactivation function of p53. Targeting the interaction between iASPP and p53 may be a potential therapy for restoring the activity of p53 in tumors.MethodsWe constructed an iASPP‐derived peptide, called A8, that was derived from the C‐terminus of iASPP. Here, we transfected A8 into two wild‐type (WT) p53 cell lines, U2OS and A549, and then determined the number of apoptotic cells. The mechanism by which A8 affected apoptosis was further examined by immunoprecipitation (IP), Dual‐Luciferase reporter assays, and chromatin IP assays. Real‐time polymerase chain reaction and western blots were also used to examine the expression levels of apoptosis‐related factors.ResultsOur data demonstrate that A8 can increase apoptosis rates in WT p53 cell lines. Functional analysis suggested that A8 restored the transcriptional function and DNA binding activities of p53 toward the Bax and PUMA gene promoters. Moreover, A8 reduced cell proliferation and inhibited tumor growth in xenograft nude mice.ConclusionsThese data provide a new approach for restoring the tumor suppressor function of p53 in cancer cells that express WT p53 and therefore may serve as a novel cancer treatment strategy.