Hepatitis B virus (HBV) genotyping plays an important role in the clinical management of chronic hepatitis B (CHB) patients. However, the current nucleic acid based techniques are expensive, time-consuming, and inconvenient. Here, we developed a novel DNA-independent HBV genotyping tool based on a one-step fluorescent lateral flow immunoassay (LFIA). Epitope-targeting immunization and screening techniques were used to develop HBV genotype specific monoclonal antibodies (mAbs). These mAbs were used to develop a multitest LFIA with a matched scanning luminoscope for HBV genotyping (named the GT-LFIA). The performance of this novel assay was carefully evaluated in well-characterized clinical cohorts. The GT-LFIA, which can specifically differentiate HBV genotypes A, B, C, and D in a pretreatment-free single test, was successfully developed using four genotype specific mAbs. The detection limits of the GT-LFIA for HBV genotypes A, B, C, and D were 2.5-10.0 IU HBV surface antigen/mL, respectively. Among the sera from 456 CHB patients, 439 (96.3%; 95% confidence interval (CI), 94.1-97.8%) were genotype-differentiable by the GT-LFIA and 437 (99.5%; 95% CI, 98.4-99.9%) were consistent with viral genome sequencing. In the 21 patients receiving nucleos(t)ide analogue therapy, for end-of-treatment specimens that were HBV DNA undetectable and were not applicable for DNA-dependent genotyping, the GT-LFIA presented genotyping results that were consistent with those obtained in pretreatment specimens by viral genome sequencing and the GT-LFIA. In conclusion, the novel GT-LFIA is a convenient, fast, and reliable tool for differential HBV genotyping, especially in patients with low or undetectable HBV DNA levels.
Enhancer-promoter interactions (EPIs) play an important role in transcriptional regulation. Recently, machine learning-based methods have been widely used in the genome-scale identification of EPIs due to their promising predictive performance. In this paper, we propose a novel method, termed EPI-DLMH, for predicting EPIs with the use of DNA sequences only. EPI-DLMH consists of three major steps. First, a two-layer convolutional neural network is used to learn local features, and an bidirectional gated recurrent unit network is used to capture long-range dependencies on the sequences of promoters and enhancers. Second, an attention mechanism is used for focusing on relatively important features. Finally, a matching heuristic mechanism is introduced for the exploration of the interaction between enhancers and promoters. We use benchmark datasets in evaluating and comparing the proposed method with existing methods. Comparative results show that our model is superior to currently existing models in multiple cell lines. Specifically, we found that the matching heuristic mechanism introduced into the proposed model mainly contributes to the improvement of performance in terms of overall accuracy. Additionally, compared with existing models, our model is more efficient with regard to computational speed.
Heterogeneous information networks (HINs) currently play an important role in daily life. HINs are applied in many fields, such as science research, e-commerce, recommendation systems, and bioinformatics. Particularly, HINs have been used in biomedical research. Algorithms have been proposed to calculate the correlations between drugs and targets and between diseases and genes. Recently, the interaction between drugs and human genes has become an important subject in the research on drug efficacy and human genomics. In previous studies, numerous prediction methods using machine learning and statistical prediction models were proposed to explore this interaction on the biological network. In the current work, we introduce a representation learning method into the biological heterogeneous network and use the representation learning models metapath2vec and metapath2vec++ on our dataset. We combine the adverse drug reaction (ADR) data in the drug–gene network with causal relationship between drugs and ADRs. This article first presents an analysis of the importance of predicting drug–gene relationships and discusses the existing prediction methods. Second, the skip-gram model commonly used in representation learning for natural language processing tasks is explained. Third, the metapath2vec and metapath2vec++ models for the example of drug–gene-ADR network are described. Next, the kernelized Bayesian matrix factorization algorithm is used to complete the prediction. Finally, the experimental results of both models are compared with Katz, CATAPULT, and matrix factorization, the prediction visualized using the receiver operating characteristic curves are presented, and the area under the receiver operating characteristic values for three varying algorithm parameters are calculated.
In view of the complex procedure of nucleic acid extraction, there exists a huge challenge for the widespread use of point-of-care diagnostics for nucleic acid testing. To achieve point-of-care applications in a more rapid and cost-efficient manner, we designed a snake pipe-shaped microfluidic chip so as to accomplish reagents-prestored, time-saving, operation-simple nucleic acid extraction. All reagents needed for this process, including lysis buffer, wash buffer, elution buffer, and so on, were preloaded in the snake pipe and securely isolated by membrane valves, without the need for using any specialized equipment. By an integrated chip and a powerful ultrasonic, this device could complete virus nucleic acid extraction from sophisticated serum samples in less than 1 min. We used hepatitis B virus (HBV) and human immunodeficiency virus (HIV) mixed with different sources of serum as samples to be extracted. The coefficient of variation of HBV and HIV extraction on-chip was 1.32% and 2.74%, respectively, and there were no significant differences between on-chip and commercial instrument extraction (P > 0.05, α = 0.05) in different dilution ratios, which showed that the extraction device we established had excellent stability and sensitivity.
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