A non-invasive ultrasensitive diagnostic approach for COVID-19 infection using salivary label-free SERS fingerprinting and artificial intelligence.

Clinical diagnostics for SARS-CoV-2 infection usually comprises the sampling of throat or nasopharyngeal swabs that are invasive and create patient discomfort. Hence, saliva is attempted as a sample of choice for the management of COVID-19 outbreaks that cripples the global healthcare system. Although limited by the risk of eliciting false-negative and positive results, tedious test procedures, requirement of specialized laboratories, and expensive reagents, nucleic acid-based tests remain the gold standard for COVID-19 diagnostics. However, genetic diversity of the virus due to rapid mutations limits the efficiency of nucleic acid-based tests. Herein, we have demonstrated the simplest screening modality based on label-free surface enhanced Raman scattering (LF-SERS) for scrutinizing the SARS-CoV-2-mediated molecular-level changes of the saliva samples among healthy, COVID-19 infected and COVID-19 recovered subjects. Moreover, our LF-SERS technique enabled to differentiate the three classes of corona virus spike protein derived from SARS-CoV-2, SARS-CoV and MERS-CoV. Raman spectral data was further decoded, segregated and effectively managed with the aid of machine learning algorithms. The classification models built upon biochemical signature-based discrimination method of the COVID-19 condition from the patient saliva ensured high accuracy, specificity, and sensitivity. The trained support vector machine (SVM) classifier achieved a prediction accuracy of 95% and F1-score of 94.73%, and 95.28% for healthy and COVID-19 infected patients respectively. The current approach not only differentiate SARS-CoV-2 infection with healthy controls but also predicted a distinct fingerprint for different stages of patient recovery. Employing portable hand-held Raman spectrophotometer as the instrument and saliva as the sample of choice will guarantee a rapid and non-invasive diagnostic strategy to warrant or assure patient comfort and large-scale population screening for SARS-CoV-2 infection and monitoring the recovery process.

Molecular characterization of hepatitis A virus from a large outbreak from Kerala, India.

BACKGROUND & OBJECTIVES: Hepatitis A is highly prevalent in India and mainly presents as a sporadic disease. This study investigated an outbreak of viral hepatitis at Medical College Hospital area, Kottayam, Kerala state, India during January 2005. METHODS: Blood (133), faecal (1), sewage (4), and water samples (13) were collected. Sera were tested for IgG- and IgM-anti-HAV and IgM antibodies against hepatitis E (IgM-anti-HEV). Sewage, faeces and water samples were tested for HAV RNA in nested RT-PCR and HAV RNA positive samples were further processed for RNA quantitation using Real Time PCR. RESULTS: Of the 1180 total cases, 540 were reported from Medical college area. Two deaths were reported among doctors. Patients from the community gave a previous history of visit to medical college hospital area. The sewage treatment plant at the campus was non-functional since 1990 and the untreated sewage was constantly overflowing and getting mixed with a canal. At the time of the study, all the water sources were superchlorinated. HAV RNA was present in the faeces of hepatitis A patient (1.36 x 10(7) copies/ml), sewage tank (2.57 x 10(3) copies/ml and the canal (<100 copies/ml). None of the 13 water samples concentrated 10,000-fold and the soil sample showed presence of HAV RNA. Phylogenetic analysis based on 5'-non-coding and P2 regions showed HAV-genotype IIIA in all samples. INTERPRETATION & CONCLUSION: The aetiological agent of the present outbreak was found to be HAV. Epidemic hepatitis A (genotype-IIIA) is emerging in Indian adults, emphasizing the need for definite policy for control.