A dual-labeled fluorescence quenching lateral flow assay based on one-pot enzyme-free isothermal cascade amplification for the rapid and sensitive detection of pathogens.

Rapid detection of nucleic acids is integral for clinical diagnostics, especially if a major public-health emergency occurs. However, such detection cannot be carried out efficiently in remote areas limited by medical resources. Herein, a dual-labeled fluorescence resonance energy transfer (FRET) lateral flow assay (LFA) based on one-pot enzyme-free cascade amplification was developed for rapid, convenient, and sensitive detection of open reading frame (ORF)1ab of severe acute respiratory syndrome-coronavirus-2. The catalyzed hairpin assembly (CHA) reaction of two well-designed hairpin probes was initiated by a target sequence and generated a hybridization chain reaction (HCR) initiator. Then, HCR probes modified with biotin were initiated to produce long DNA nanowires. After two-level amplification, the cascade-amplified product was detected by dual-labeled lateral flow strips. Gold nanoparticles (AuNPs)-streptavidin combined with the product and then ran along a nitrocellulose membrane under the action of capillary force. After binding with fluorescent microsphere-labeled-specific probes on the T line, a positive signal (red color) could be observed. Meanwhile, AuNPs could quench the fluorescence of the T line, and an inverse relationship between fluorescence intensity and the concentration of the CHA-HCR-amplified product was formed. The proposed strategy achieved a satisfactory limit of detection of 2.46 pM for colorimetric detection and 174 fM for fluorescent detection, respectively. Benefitting from the features of being one-pot, enzyme-free, low background, high sensitivity, and selectivity, this strategy shows great potential in bioanalysis and clinical diagnostics upon further development.

A cascade amplification strategy based on rolling circle amplification and hybridization chain reaction for ultrasensitive detection of pathogens.

Rapid and accurate detection of a variety of pathogens is very important for the prevention, control, and diagnosis of infectious diseases. Herein, an ultrasensitive nucleic acid isothermal cascade amplification technique based on rolling circle amplification (RCA) coupled with hybridization chain reaction (HCR) was developed for ORF1ab (opening reading frame 1a/b) for SARS-CoV-2 detection. In this scheme, the ORF1ab sequence hybridized with a padlock probe to trigger RCA reaction. Specifically, the recognition site for a unique nicking enzyme was incorporated into the padlock probe to cut the RCA products into short intermediate amplicons, which contain dual HCR initiation sites and can be directly used as primers for HCR. HCR probes, H1 and H2, labeled with FAM (FAM-H1 and FAM-H2) spontaneously participated in the HCR and formed a long nicked dsDNA. Additional probes were quenched by graphene oxide (GO) via π-stacking to decrease the background signal. Meanwhile, the fluorescence signal can be strongly amplified by the synergistic effect of FAM and SYBR green I. The proposed RCA-HCR method can be used to detect ORF1ab at concentrations as low as 7.65 fM. Moreover, the reliability of the RCA-HCR method in serum samples has also been validated. Satisfactory recoveries ranging from 85% to 113% for ORF1ab can be obtained. Therefore, this facile and ultrasensitive RCA-HCR assay provides a new promising tool for ORF1ab analysis and can be extended to the detection of various kinds of pathogens and genetic biomarkers.

Recent advances in cascade isothermal amplification techniques for ultra-sensitive nucleic acid detection.

Nucleic acid amplification techniques have always been one of the hot spots of research, especially in the outbreak of COVID-19. From the initial polymerase chain reaction (PCR) to the current popular isothermal amplification, each new amplification techniques provides new ideas and methods for nucleic acid detection. However, limited by thermostable DNA polymerase and expensive thermal cycler, PCR is difficult to achieve point of care testing (POCT). Although isothermal amplification techniques overcome the defects of temperature control, single isothermal amplification is also limited by false positives, nucleic acid sequence compatibility, and signal amplification capability to some extent. Fortunately, efforts to integrating different enzymes or amplification techniques that enable to achieve intercatalyst communication and cascaded biotransformations may overcome the corner of single isothermal amplification. In this review, we systematically summarized the design fundamentals, signal generation, evolution, and application of cascade amplification. More importantly, the challenges and trends of cascade amplification were discussed in depth.

Advancements in Testing Strategies for COVID-19.

The SARS-CoV-2 coronavirus, also known as the disease-causing agent for COVID-19, is a virulent pathogen that may infect people and certain animals. The global spread of COVID-19 and its emerging variation necessitates the development of rapid, reliable, simple, and low-cost diagnostic tools. Many methodologies and devices have been developed for the highly sensitive, selective, cost-effective, and rapid diagnosis of COVID-19. This review organizes the diagnosis platforms into four groups: imaging, molecular-based detection, serological testing, and biosensors. Each platform's principle, advancement, utilization, and challenges for monitoring SARS-CoV-2 are discussed in detail. In addition, an overview of the impact of variants on detection, commercially available kits, and readout signal analysis has been presented. This review will expand our understanding of developing advanced diagnostic approaches to evolve into susceptible, precise, and reproducible technologies to combat any future outbreak.

CoSinGAN: Learning COVID-19 Infection Segmentation from a Single Radiological Image.

Computed tomography (CT) images are currently being adopted as the visual evidence for COVID-19 diagnosis in clinical practice. Automated detection of COVID-19 infection from CT images based on deep models is important for faster examination. Unfortunately, collecting large-scale training data systematically in the early stage is difficult. To address this problem, we explore the feasibility of learning deep models for lung and COVID-19 infection segmentation from a single radiological image by resorting to synthesizing diverse radiological images. Specifically, we propose a novel conditional generative model, called CoSinGAN, which can be learned from a single radiological image with a given condition, i.e., the annotation mask of the lungs and infected regions. Our CoSinGAN is able to capture the conditional distribution of the single radiological image, and further synthesize high-resolution (512 × 512) and diverse radiological images that match the input conditions precisely. We evaluate the efficacy of CoSinGAN in learning lung and infection segmentation from very few radiological images by performing 5-fold cross validation on COVID-19-CT-Seg dataset (20 CT cases) and an independent testing on the MosMed dataset (50 CT cases). Both 2D U-Net and 3D U-Net, learned from four CT slices by using our CoSinGAN, have achieved notable infection segmentation performance, surpassing the COVID-19-CT-Seg-Benchmark, i.e., the counterparts trained on an average of 704 CT slices, by a large margin. Such results strongly confirm that our method has the potential to learn COVID-19 infection segmentation from few radiological images in the early stage of COVID-19 pandemic.

Drr4covid: Learning Automated COVID-19 Infection Segmentation From Digitally Reconstructed Radiographs.

Automated infection measurement and COVID-19 diagnosis based on Chest X-ray (CXR) imaging is important for faster examination, where infection segmentation is an essential step for assessment and quantification. However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of (Accuracy: 0.949, AUC: 0.987, F1-score: 0.947) and a segmentation score of (Accuracy: 0.956, AUC: 0.980, F1-score: 0.955) on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at https://github.com/PengyiZhang/DRR4Covid.