RESUMEN
Rotavirus A (RVA) is a common cause of diarrhea in newborn pigs, leading to significant economic losses. RVA is considered a major public health concern due to genetic evolution, high prevalence, and pathogenicity in humans and animals. The objective of this study was to identify and characterize RVA in swine farms in Chile. A total of 154 samples (86 oral fluids and 68 fecal samples) were collected, from 22 swine farms. 58 (38%) samples belonging to 14 farms were found positive for RVA by real-time RT-PCR. The samples with low Ct values (21) and the two isolates were selected for whole genome sequencing. Nearly complete genomes were assembled from both isolates and partial genomes were assembled from five clinical samples. BLAST analysis confirmed that these sequences are related to human and swine-origin RVA. The genomic constellation was G5/G3-P[7]-I5-R1-C1-M1-A8-N1-T1-E1-H1. Phylogenetic analysis showed that VP4, VP1, VP2, NSP2, NSP3, NSP4, and NSP5 sequences were grouped in monophyletic clusters, suggesting a single introduction. The phylogenies for VP7, VP6, VP3, and NSP1 indicated two different origins of the Chilean sequences. The phylogenetic trees showed that most of the Chilean RVA sequences are closely related to human and swine-origin RVA detected across the world. The results highlight the potential zoonotic nature of RVA circulating in Chilean swine farms. Therefore, it is important to continue RVA whole genome sequencing globally to fully understand its complex epidemiology and early detection and characterization of zoonotic strains.
RESUMEN
The SARS-Coronavirus-2 (SARS-CoV-2) infectious disease, COVID-19, has spread rapidly, resulting in a global pandemic with significant mortality. The combination of early diagnosis via rapid screening, contact tracing, social distancing and quarantine has helped to control the pandemic. The absence of real time response and diagnosis is a crucial technology shortfall and is a key reason why current contact tracing methods are inadequate to control spread. In contrast, current information technology combined with a new generation of near-real time tests offers consumer-engaged smartphone-based "lab-in-a-phone" internet-of-things (IoT) connected devices that provide increased pandemic monitoring. This review brings together key aspects required to create an entire global diagnostic ecosystem. Cross-disciplinary understanding and integration of both mechanisms and technologies for effective detection, incidence mapping and disease containment in near real-time is summarized. Available measures to monitor and/or sterilize surfaces, next-generation laboratory and smartphone-based diagnostic approaches can be brought together and networked for instant global monitoring that informs Public Health policy. Cloud-based analysis enabling real-time mapping will enable future pandemic control, drive the suppression and elimination of disease spread, saving millions of lives globally. A new paradigm is introduced - scaled and multiple diagnostics for mapping and spreading of a pandemic rather than traditional accumulation of individual measurements. This can do away with the need for ultra-precise and ultra-accurate analysis by taking mass measurements that can relax tolerances and build resilience through networked analytics and informatics, the basis for novel swarm diagnostics. These include addressing ethical standards, local, national and international collaborative engagement, multidisciplinary and analytical measurements and standards, and data handling and storage protocols.
