Purpose: To detect the presence of viral RNA of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in conjunctival swab specimens of coronavirus disease-19 (COVID-19) patients. Methods: Forty-five COVID-19 patients positive for real-time reverse transcription-polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasopharyngeal swab with or without ocular manifestations were included in the study. The conjunctival swab of each patient was collected by an ophthalmologist posted for COVID duty. Results: Out of 45 patients, 35 (77.77%) were males and the rest were females. The mean age was 31.26 ± 12.81 years. None of the patients had any ocular manifestations. One (2.23%) out of 45 patients was positive for RT-PCR SARS-CoV-2 in the conjunctival swab. Conclusion: This study shows that SARS-CoV-2 can be detected in conjunctival swabs of confirmed cases of COVID-19 patients. Though the positivity rate of detecting SARS-CoV-2 in conjunctival swabs is very less, care should be exercised during the ocular examination of patients of COVID-19.

Objective To estimate the burden of active infection and anti-SARS-CoV-2 IgG antibodies in Karnataka state jointly and to assess variation across geographical regions and risk groups. Methods A cross-sectional survey of 16416 people covering three risk groups was done between 3-16 September 2020 using the state of Karnataka’s infrastructure of 290 healthcare facilities across all 30 districts. Participants were further classified into risk subgroups and were sampled using stratified sampling. All participants were subjected to simultaneous detection of SARS-CoV-2 IgG using a commercial ELISA kit, SARS-CoV-2 antigen using a rapid antigen detection test (RAT), and reverse transcription-polymerase chain reaction (RT-PCR) for RNA detection. Maximum-likelihood estimation was used for joint estimation of the adjusted IgG, active, and total prevalence (either IgG or active or both), while multinomial regression identified predictors. Results Overall adjusted total prevalence of COVID-19 in Karnataka was 27.7% (95% CI: 26.1 to 29.3), IgG 16.8% (15.5 to 18.1) and active infection fraction 12.6% (11.5 to 13.8). Case-to-infection ratio 1:40, and infection fatality rate 0.05%. Influenza-like-symptoms or contact with COVID-19 positive patient are good predictors of active infection. RAT kits had higher sensitivity (68%) in symptomatics compared to 47% asymptomatic. Conclusion Our sentinel-based population survey is the first comprehensive survey to provide accurate estimates of the COVID-19 burden. Our findings provide a reasonable approximation of the population immunity threshold levels. Leveraging existing surveillance platforms, coupled with syndromic approach and sampling framework, renders our model replicable.

The rapid identification of bacterial pathogens in clinical samples like blood, urine, pus, and sputum is the need of the hour. Conventional bacterial identification methods like culturing and nucleic acid-based amplification have limitations like poor sensitivity, high cost, slow turnaround time, etc. Raman spectroscopy, a label-free and noninvasive technique, has overcome these drawbacks by providing rapid biochemical signatures from a single bacterium. Raman spectroscopy combined with chemometric methods has been used effectively to identify pathogens. However, a robust approach is needed to utilize Raman features for accurate classification while dealing with complex data sets such as spectra obtained from clinical isolates, showing high sample-to-sample heterogeneity. In this study, we have used Raman spectroscopy-based identification of pathogens from clinical isolates using a deep transfer learning approach at the single-cell level resolution. We have used the data-augmentation method to increase the volume of spectra needed for deep-learning analysis. Our ResNet model could specifically extract the spectral features of eight different pathogenic bacterial species with a 99.99% classification accuracy. The robustness of our model was validated on a set of blinded data sets, a mix of cultured and noncultured bacterial isolates of various origins and types. Our proposed ResNet model efficiently identified the pathogens from the blinded data set with high accuracy, providing a robust and rapid bacterial identification platform for clinical microbiology.

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