SARS-CoV-2 RNA Detection by a Cellphone-Based Amplification-Free System with CRISPR/CAS-Dependent Enzymatic (CASCADE) Assay.

CRISPR (Clustered regularly interspaced short palindromic repeats)-based diagnostic technologies have emerged as a promising alternative to accelerate delivery of SARS-CoV-2 molecular detection at the point of need. However, efficient translation of CRISPR-diagnostic technologies to field application is still hampered by dependence on target amplification and by reliance on fluorescence-based results readout. Herein, an amplification-free CRISPR/Cas12a-based diagnostic technology for SARS-CoV-2 RNA detection is presented using a smartphone camera for results readout. This method, termed Cellphone-based amplification-free system with CRISPR/CAS-dependent enzymatic (CASCADE) assay, relies on mobile phone imaging of a catalase-generated gas bubble signal within a microfluidic channel and does not require any external hardware optical attachments. Upon specific detection of a SARS-CoV-2 reverse-transcribed DNA/RNA heteroduplex target (orf1ab) by the ribonucleoprotein complex, the transcleavage collateral activity of the Cas12a protein on a Catalase:ssDNA probe triggers the bubble signal on the system. High analytical sensitivity in signal detection without previous target amplification (down to 50 copies µL-1) is observed in spiked samples, in ≈71 min from sample input to results readout. With the aid of a smartphone vision tool, high accuracy (AUC = 1.0; CI: 0.715 - 1.00) is achieved when the CASCADE system is tested with nasopharyngeal swab samples of PCR-positive COVID-19 patients.

Recurrent COVID-19 including evidence of reinfection and enhanced severity in thirty Brazilian healthcare workers.

BACKGROUND: There is growing concern about individuals reported to suffer repeat COVID-19 disease episodes, these in a small number of cases characterised as de novo infections with distinct sequences, indicative of insufficient protective immunity even in the short term. METHODS: Observational case series and case-control studies reporting 33 cases of recurrent, symptomatic, qRT-PCR positive COVID-19. Recurrent disease was defined as symptomatic recurrence after symptom-free clinical recovery, with release from isolation >14 days from the beginning of symptoms confirmed by qRT-PCR. The case control study-design compared this group of patients with a control group of 62 patients randomly selected from the same COVID-19 database. RESULTS: Of 33 recurrent COVID-19 patients, 26 were female and 30 were HCW. Mean time to recurrence was 50.5 days which was associated with being a HCW (OR 36.4 (p <0.0001)), and blood type A (OR 4.8 (p = 0.002)). SARS-CoV-2 antibodies were signifcantly lower in recurrent patients after initial COVID-19  (2.4 ±â€¯0.610; p<0.0001) and after recurrence (6.4 ±â€¯11.34; p = 0.007).  Virus genome sequencing identified reinfection by a different isolate in one patient. CONCLUSIONS: This is the first detailed case series showing COVID-19 recurrence with qRT-PCR positivity. For one individual detection of phylogenetically distinct genomic sequences in the first and second episodes confirmed bona fide renfection, but in most cases the data do not formally distinguish between reinfection and re-emergence of a chronic infection reservoir. These episodes were significantly associated with reduced Ab response during initial disease and argue the need for ongoing vigilance without an assumption of protection after a first episode.

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.

Ion torrent-based nasopharyngeal swab metatranscriptomics in COVID-19.

Herein, we describe the detection of a SARS-CoV-2 genome through metatranscriptome next-generation sequencing directly from the nasopharyngeal swab of a suspected case of local transmission of Covid-19, in Brazil. Depletion of human ribosomal RNA and use of an optimized in-house developed bioinformatics strategy contributed to successful detection of the virus.