Effect of 0.5% povidone-iodine on the nasopharyngeal and oropharyngeal viral loads in patients with COVID-19: A double-blind placebo-controlled randomized clinical trial.

Background and Objective: The povidone-iodine (PvP-I) nasal antiseptic has been shown to completely inactivate the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in vitro at variable concentrations. This study was performed to investigate the effect of 0.5% PvP-I nasal drops and oral gargles on the nasopharyngeal and oropharyngeal viral loads in SARS-CoV-2-positive patients. Methods: This was a double-blind, placebo-controlled, randomized clinical trial among patients aged ≥18 years with reverse-transcriptase polymerase chain reaction confirmed in the mild to moderate category of SARS-CoV-2 infection. A total of 32 patients were randomly assigned to receive either freshly prepared 0.5% PvP-I solution or distilled water in the form of supervised self-administered 4-5 nasal drops, followed by 20 ml for gargling for at least 30 seconds. The main outcome measure was the mean change in viral titer and Ct values in the nasopharyngeal and oropharyngeal samples at baseline, 5 minutes, and 3 hours post intervention. Results: The mean change in viral titers across the time duration for the test group when compared with the control group was not statistically significant (P = 0.109). However, the mean change in Ct value was found to be borderline statistically significant (P = 0.042). Noticeable differences were noted among the mean viral titers and Ct values in the intervention group when plotted against the time of testing as compared to the control group. PvP-I solution at 0.5% dilution was well tolerated, and no evident side effects were reported. Conclusions: This study shows that 0.5% PvP-I has an effect on reducing nasopharyngeal and oropharyngeal viral loads in COVID-19 patients. This can be of substantial aid for the primary care physicians, especially for the practitioners in remote and resource poor areas.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images.

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.