Indirect signal amplification strategy with universal probes-based lateral flow immunoassay for rapid quantitative detection of Fumonisin B1.

Fumonisin B1 (FB1) is a serious threat to the health of humans and animals. Herein, a lateral flow immunoassay based on the universal detection probes (goat anti-mouse IgG@Eu ) that...

Pan-Piscine Orthoreovirus (PRV) Detection Using Reverse Transcription Quantitative PCR

Piscine orthoreovirus (PRV) infects farmed and wild salmon and trout species in North America, South America, Europe, and East Asia. PRV groups into three distinct genotypes (PRV-1, PRV-2, and PRV-3) that can vary in distribution, host specificity, and/or disease potential. Detection of the virus is currently restricted to genotype specific assays such that surveillance programs require the use of three assays to ensure universal detection of PRV. Consequently, herein, we developed, optimized, and validated a real-time reverse transcription quantitative PCR assay (RT-qPCR) that can detect all known PRV genotypes with high sensitivity and specificity. Targeting a conserved region at the 5′ terminus of the M2 segment, the pan-PRV assay reliably detected all PRV genotypes with as few as five copies of RNA. The assay exclusively amplifies PRV and does not cross-react with other salmonid viruses or salmonid host genomes and can be performed as either a one- or two-step RT-qPCR. The assay is highly reproducible and robust, showing 100% agreement in test results from an inter-laboratory comparison between two laboratories in two countries. Overall, as the assay provides a single test to achieve highly sensitive pan-specific PRV detection, it is suitable for research, diagnostic, and surveillance purposes.

A targeted approach with nanopore sequencing for the universal detection and identification of flaviviruses

Nucleic acid test (NAT), most typically quantitative PCR, is one of the standard methods for species specific flavivirus diagnosis. Semi-comprehensive NATs such as pan-flavivirus PCR which covers genus Flavivirus are also available; however, further specification by sequencing is required for species level differentiation. In this study, a semi-comprehensive detection system that allows species differentiation of flaviviruses was developed by integration of the pan-flavivirus PCR and Nanopore sequencing. In addition, a multiplexing method was established by adding index sequences through the PCR with a streamlined bioinformatics pipeline. This enables defining cut-off values for observed read counts. In the laboratory setting, this approach allowed the detection of up to nine different flaviviruses. Using clinical samples collected in Vietnam and Brazil, seven different flaviviruses were also detected. When compared to a commercial NAT, the sensitivity and specificity of our system were 66.7% and 95.4%, respectively. Conversely, when compared to our system, the sensitivity and specificity of the commercial NAT were 57.1% and 96.9%, respectively. In addition, Nanopore sequencing detected more positive samples (n = 8) compared to the commercial NAT (n = 6). Collectively, our study has established a semi-comprehensive sequencing-based diagnostic system for the detection of flaviviruses at extremely affordable costs, considerable sensitivity, and only requires simple experimental methods.

Noise Doesn't Lie: Towards Universal Detection of Deep Inpainting

Deep image inpainting aims to restore damaged or missing regions in an image with realistic contents. While having a wide range of applications such as object removal and image recovery, deep inpainting techniques also have the risk of being manipulated for image forgery. A promising countermeasure against such forgeries is deep inpainting detection, which aims to locate the inpainted regions in an image. In this paper, we make the first attempt towards universal detection of deep inpainting, where the detection network can generalize well when detecting different deep inpainting methods. To this end, we first propose a novel data generation approach to generate a universal training dataset, which imitates the noise discrepancies exist in real versus inpainted image contents to train universal detectors. We then design a Noise-Image Cross-fusion Network (NIX-Net) to effectively exploit the discriminative information contained in both the images and their noise patterns. We empirically show, on multiple benchmark datasets, that our approach outperforms existing detection methods by a large margin and generalize well to unseen deep inpainting techniques. Our universal training dataset can also significantly boost the generalizability of existing detection methods.

Universal Detection of Mia Antigen and Frequencies of Glycophorin Hybrids among Blood Donors in Taiwan by Human Monoclonal Antibodies against Mia (MNS7), Mur (MNS10), and MUT (MNS35) Antigens

Glycophorin hybrids such as GP.Mur are common in Southeast Asians. In Taiwan, clinically significant alloantibodies to the GP.Mur phenotype are the most important issue in blood banks. A large-scale screening of glycophorin hybrids in the Taiwanese population is urgently needed to ensure transfusion safety. Four clones of human hybridomas that secrete anti-Mia, anti-MUT, and anti-Mur were established by fusing human B-lymphocytes and myeloma cells (JMS-3). The specificity of each monoclonal antibody (MoAb) was characterized. Three MoAbs were applied on an Automated Pretransfusion Blood Testing Analyzer (PK7300/PK7400) for donor screening. Genotyping was performed to determine the detailed subgrouping of glycophorin hybrids. Four MoAbs are IgM antibodies. Anti-Mia (377T) binds to 46DXHKRDTYA54, 48HKRDTYAAHT57 peptides, and anti-Mia (367T) binds to 43QTNDXHKRD51 peptides (X indicates T, M, or K). Anti-Mur is reactive with 49KRDTYPAHTA58 peptides. Anti-MUT is reactive with 47KHKRDTYA54. A total of 78,327 donors were screened using three MoAbs, and 3690 (4.71%) were GP.Mur, 20 (0.025%) were GP.Hut, and 18 (0.022%) were GP.Vw. When the Mia antigen was introduced as routine screening, the frequency of Mi(a+) among blood donors in Taiwan was 4.66% (67,348/1,444,541). Mia antigen was implemented as a routine blood testing, and the results were labeled on all red blood cell (RBC) units.