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Introduction. The COVID-19 pandemic, which began in 2020 is testing economic resilience and surge capacity of healthcare providers worldwide. At the time of writing, positive detection of the SARS-CoV-2 virus remains the only method for diagnosing COVID-19 infection. Rapid upscaling of national SARS-CoV-2 genome testing presented challenges: (1) Unpredictable supply chains of reagents and kits for virus inactivation, RNA extraction and PCR-detection of viral genomes. (2) Rapid time to result of Hypothesis. Extraction-free sample processing would impact commercially available SARS-CoV-2 genome detection methods. Aim. We evaluated whether alternative commercially available kits provided sensitivity and accuracy of SARS-CoV-2 genome detection comparable to those used by regional National Healthcare Services (NHS). Methodology. We tested several detection methods and tested whether detection was altered by heat inactivation, an approach for rapid one-step viral inactivation and RNA extraction without chemicals or kits. Results. Using purified RNA, we found the CerTest VIASURE kit to be comparable to the Altona RealStar system currently in use, and further showed that both diagnostic kits performed similarly in the BioRad CFX96 and Roche LightCycler 480 II machines. Additionally, both kits were comparable to a third alternative using a combination of Quantabio qScript one-step Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) mix and Centre for Disease Control and Prevention (CDC)-accredited N1 and N2 primer/probes when looking specifically at borderline samples. Importantly, when using the kits in an extraction-free protocol, following heat inactivation, we saw differing results, with the combined Quantabio-CDC assay showing superior accuracy and sensitivity. In particular, detection using the CDC N2 probe following the extraction-free protocol was highly correlated to results generated with the same probe following RNA extraction and reported clinically (n=127; R2=0.9259). Conclusion. Our results demonstrate that sample treatment can greatly affect the downstream performance of SARS-CoV-2 diagnostic kits, with varying impact depending on the kit. We also showed that one-step heat-inactivation methods could reduce time from swab receipt to outcome of test result. Combined, these findings present alternatives to the protocols in use and can serve to alleviate any arising supply-chain issues at different points in the workflow, whilst accelerating testing, and reducing cost and environmental impact.

Purpose. While some micro-organisms, such as Staphylococcus aureus , are clearly implicated in causing tissue damage in diabetic foot ulcers (DFUs), our knowledge of the contribution of the entire microbiome to clinical outcomes is limited. We profiled the microbiome of a longitudinal sample series of 28 people with diabetes and DFUs of the heel in an attempt to better characterize the relationship between healing, infection and the microbiome. Methodology. In total, 237 samples were analysed from 28 DFUs, collected at fortnightly intervals for 6 months or until healing. Microbiome profiles were generated by 16S rRNA gene sequence analysis, supplemented by targeted nanopore sequencing. Result/Key findings. DFUs which failed to heal during the study period (20/28, 71.4 %) were more likely to be persistently colonized with a heterogeneous community of micro-organisms including anaerobes and Enterobacteriaceae (log-likelihood ratio 9.56, P=0.008). During clinically apparent infection, a reduction in the diversity of micro-organisms in a DFU was often observed due to expansion of one or two taxa, with recovery in diversity at resolution. Modelling of the predicted species interactions in a single DFU with high diversity indicated that networks of metabolic interactions may exist that contribute to the formation of stable communities. Conclusion. Longitudinal profiling is an essential tool for improving our understanding of the microbiology of chronic wounds, as community dynamics associated with clinical events can only be identified by examining changes over multiple time points. The development of complex communities, particularly involving Enterobacteriaceae and strict anaerobes, may be contributing to poor outcomes in DFUs and requires further investigation.