Children who have received the enterovirus A71 vaccine are still at risk for disease with infections of enteroviruses of other serotypes.
Objective: Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer-related death worldwide. The study aimed to establish a robust immune-related gene pair (IRGP) signature for predicting the prognosis of HCC patients. Methods: Two RNA-seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (GSE14520) were included in this study. We used a series of immune-related genes from the ImmPort database to construct gene pairs. Lasso penalized Cox proportional hazards regression was employed to develop the best prognostic signature. We assigned patients into two groups with low immune risk and high immune risk. Then, the prognostic ability of the signature was evaluated by a log-rank test and a Cox proportional hazards regression model. Results: After 1000 iterations, the 33-immune gene pair model obtained the highest frequency. As a result, we chose the 33 immune gene pairs to establish the immunerelated prognostic signature. As we expected, the immune-related signature accurately predicted the prognosis of HCC patients, and high-risk groups showed poor prognosis in the training datasets and testing datasets as well as in the validation datasets. Furthermore, the immune-related gene pair (IRGP) signature also showed higher predictive accuracy than three existing prognostic signatures. Conclusion: Our prognostic signature, which reflects the link between the immune microenvironment and HCC patient outcome, is promising for prognosis prediction in HCC.
The gold standard for prostate cancer (PCa) diagnosis is prostate biopsy. However, it remines controversial as an invasive mean for patients with PSA levels in the gray zone (4–10 ng/mL). This study aimed to develop strategy to reduce the unnecessary prostate biopsy. We retrospectively identified 235 patients with serum total PSA testing in the gray zone before prostate biopsy between 2014 and 2018. Age, PSA derivates, prostate volume and multiparametric magnetic imaging (mpMRI) examination were assessed as predictors for PCa and clinically significant PCa with Gleason score ≥ 7 (CSPCa). Univariate analysis showed that prostate volume, PSAD, and mpMRI examination were significant predictors of PCa and CSPCa (P < 0.05). The differences of diagnostic accuracy between mpMRI examination (AUC = 0.69) and other clinical parameters in diagnostic accuracy for PCa were not statistically significant. However, mpMRI examination (AUC = 0.79) outperformed prostate volume and PSAD in diagnosis of CSPCa. The multivariate models (AUC = 0.79 and 0.84 for PCa and CSPCa) performed significantly better than mpMRI examination for detection of PCa (P = 0.003) and CSPCa (P = 0.036) among patients with PSA level in the gray zone. At the same level of sensitivity as the mpMRI examination to diagnose PCa, applying the multivariate models could reduce the number of biopsies by 5% compared with mpMRI examination. Overall, our results supported the view that the multivariate model could reduce unnecessary biopsies without compromising the ability to diagnose PCa and CSPCa. Further prospective validation is required.
Coxsackievirus A6 emerged as one of the predominant causative agents of hand, foot and mouth disease epidemics in many provinces of China in 2013 and 2015. This virus strain accounted for 25.9% of mild and 15.2% of severe cases in 2013 and 25.8% of mild and 16.9% of severe cases in 2015.
BackgroundCandida tropicalis is considered to be the leading pathogen causing nosocomial fungemia and hepatosplenic fungal infections in patients with cancer, particularly those with leukemia. Microsatellite-based typing methods using sets of genetic markers have been developed and reported for population structure analysis of C. albicans, C. glabrata, and C. parapsilosis, but no studies have been published for genetic analysis of C. tropicalis. The objective of this study was to develop new microsatellite loci that have the ability to distinguish among C. tropicalis isolates.ResultsDNA sequences containing over 10 bi- or tri-nucleotide repeats were selected from the C. tropicalis genome database. Thirty PCR primers sets specific for the microsatellite loci were designed and tested using eight clinically independent isolates. According to the amplification efficiency, specificity, and observed polymorphisms, eight markers were selected for further population structure analysis and molecular typing. Sixty-five independent C. tropicalis isolates were genotyped using these 8 markers. Based on these analyses, six microsatellite loci were confirmed, although two loci were found to be with unstable flanking areas. The six polymorphic loci displayed 4–22 alleles and 7–27 genotypes. The discriminatory power of the six loci ranged from 0.70 to 0.95. Genotyping results obtained by microsatellite analysis were compared to PCR-fingerprinting and multi-locus sequence typing (MLST). The comparisons showed that microsatellite analysis and MLST had the similar discriminatory power for C. tropicalis, which were more powerful than PCR-fingerprinting.ConclusionsThis is the first attempt to develop new microsatellite loci for C. tropicalis. These newly developed markers will be a valuable resource for the differentiation of C. tropicalis isolates. More C. tropicalis isolates will need to be sequenced and analyzed in order to fully show the potential of these newly developed microsatellite markers.
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