ABSTRACT
The host response to SARS-CoV-2 infection provide insights into both viral pathogenesis and patient management. The host-encoded microRNA (miRNA) response to SARS-CoV-2 infection, however, remains poorly defined. Here we profiled circulating miRNAs from ten COVID-19 patients sampled longitudinally and ten age and gender matched healthy donors. We observed 55 miRNAs that were altered in COVID-19 patients during early-stage disease, with the inflammatory miR-31-5p the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) independently classified COVID-19 cases with an accuracy of 99.9%. In a ferret COVID-19 model, the three-miRNA signature again detected SARS-CoV-2 infection with 99.7% accuracy, and distinguished SARS-CoV-2 infection from influenza A (H1N1) infection and healthy controls with 95% accuracy. Distinct miRNA profiles were also observed in COVID-19 patients requiring oxygenation. This study demonstrates that SARS-CoV-2 infection induces a robust host miRNA response that could improve COVID-19 detection and patient management.
Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/genetics , MicroRNAs/genetics , SARS-CoV-2 , Adult , Aged , Animals , COVID-19/blood , Case-Control Studies , Diagnosis, Differential , Disease Models, Animal , Female , Ferrets , Gene Expression , Host Microbial Interactions/genetics , Humans , Influenza A Virus, H1N1 Subtype , Longitudinal Studies , Male , MicroRNAs/blood , Middle Aged , Orthomyxoviridae Infections/diagnosis , Orthomyxoviridae Infections/genetics , Pandemics , Supervised Machine LearningABSTRACT
On global scale, the current situation of pandemic is symptomatic of increased incidences of contagious diseases caused by pathogens. The faster spread of these diseases, in a moderately short timeframe, is threatening the overall population wellbeing and conceivably the economy. The inadequacy of conventional diagnostic tools in terms of time consuming and complex laboratory-based diagnosis process is a major challenge to medical care. In present era, the development of point-of-care testing (POCT) is in demand for fast detection of infectious diseases along with "on-site" results that are helpful in timely and early action for better treatment. In addition, POCT devices also play a crucial role in preventing the transmission of infectious diseases by offering real-time testing and lab quality microbial diagnosis within minutes. Timely diagnosis and further treatment optimization facilitate the containment of outbreaks of infectious diseases. Presently, efforts are being made to support such POCT by the technological development in the field of internet of medical things (IoMT). The IoMT offers wireless-based operation and connectivity of POCT devices with health expert and medical centre. In this review, the recently developed POC diagnostics integrated or future possibilities of integration with IoMT are discussed with focus on emerging and re-emerging infectious diseases like malaria, dengue fever, influenza A (H1N1), human papilloma virus (HPV), Ebola virus disease (EVD), Zika virus (ZIKV), and coronavirus (COVID-19). The IoMT-assisted POCT systems are capable enough to fill the gap between bioinformatics generation, big rapid analytics, and clinical validation. An optimized IoMT-assisted POCT will be useful in understanding the diseases progression, treatment decision, and evaluation of efficacy of prescribed therapy.
Subject(s)
Biosensing Techniques/instrumentation , Communicable Diseases/diagnosis , Internet of Things , Point-of-Care Testing , Animals , Artificial Intelligence , Biosensing Techniques/methods , COVID-19/diagnosis , Coronavirus Infections/diagnosis , Dengue/diagnosis , Equipment Design , HIV Infections/diagnosis , Hemorrhagic Fever, Ebola/diagnosis , Humans , Influenza, Human/diagnosis , Malaria/diagnosis , Orthomyxoviridae Infections/diagnosis , Zika Virus Infection/diagnosisABSTRACT
The COVID-19 pandemic has highlighted the importance of rapid, cost effective, accurate, and non-invasive testing for viral infections. Volatile compounds (VCs) have been suggested for several decades as fulfilling these criteria. However currently very little work has been done in trying to diagnose viral infections using VCs. Much of the work carried out to date involves the differentiation of bacterial and viral sources of infection and often the detection of bacterial and viral co-infection. However, this has usually been done in vitro and very little work has involved the use of human participants. Viruses hijack the host cell metabolism and do not produce their own metabolites so identifying virus specific VCs is at best a challenging task. However, there are proteins and lipids that are potential candidates as markers of viral infection. The current understanding is that host cell glycolysis is upregulated under viral infection to increase the available energy for viral replication. There is some evidence that viral infection leads to the increase of production of fatty acids, alkanes, and alkanes related products. For instance, 2,3-butandione, aldehydes, 2,8-dimethyl-undecane and n-propyl acetate have all been correlated with viral infection. Currently, the literature points to markers of oxidative stress (e.g. nitric oxide, aldehydes etc) being the most useful in the determination of viral infection. The issue, however, is that there are also many other conditions that can lead to oxidative stress markers being produced. In this review a range of (mainly mass spectrometric) methods are discussed for viral detection in breath, including breath condensate. Currently MALDI-ToF-MS is likely to be the preferred method for the identification of viral strains and variants of those strains, however it is limited by its need for the viral strains to have been sequenced and logged in a database.