ABSTRACT
The recently identified coronavirus pneumonia, which was later given the name COVID-19, is a virus that can be fatal and has affected more than 300,000 individuals around the world. Because there is currently no antiviral therapy or vaccine that has been granted approval by the FDA to cure or prevent this sickness, an automatic method for disease identification is required because of the fast global distribution of this exceedingly contagious and lethal virus. A unique machine learning strategy for automatically detecting this ailment was discovered. Machine learning approaches should be applied in essential jobs in infectious illnesses. As a result, our major aim is to use computer vision algorithms to identify COVID-19 without the need for human interaction. This paper suggested using image processing to classify objects and make early detections using X-ray pictures. Features are extracted for this region using a variety of techniques, including (LBP), (HOG), and use K-Nearest Neighbor algorithm (KNN) for classification, with training percentages of 50%, 60%, 70%, 80%, and 90%. Experiments indicated that using the suggested approach to identify X-ray photos of corona patients, it is feasible to diagnose the disease using X-ray images by training the device on the image data set (about 2,400 photos). The results were tested on the average of the samples taken (random 2000 images) each time and the measurement of multiple training ratios (50%, 60%, 70%, 80%, and 90%). The experimental findings revealed remarkable prediction accuracy in all investigated scenarios, ranging from 85% to 99%. © 2022 IEEE.
ABSTRACT
Background: COVID-19 is a novel acute respiratory disease that first emerged in 2019 and exploded into a worldwide pandemic. Symptoms range from dry cough, fatigue, fever, and pneumonia in mild cases to acute respiratory distress in critical cases. Case Report: The aim of the present case study is to illustrate the benefits of using select antibiotics and dietary supplements to treat a mild case of COVID-19. Conclusion: A combination of azithromycin, cefixime, vitamin C, vitamin D, and zinc may potentially be beneficial for the treatment of moderate COVID-19 symptoms.
ABSTRACT
OBJECTIVE: Coronavirus 2019 (COVID-19) has now been declared as a worldwide pandemic. Currently, no drugs have been endorsed for its treatment; in this manner, a pressing need has been developed for any antiviral drugs that will treat COVID-19. Coronaviruses require the SARS-CoV-2 3CL-Protease (3CL-protease) for cleavage of its polyprotein to yield a single useful protein and assume a basic role in the disease progression. In this study, we demonstrated that punicalagin, the fundamental active element of pomegranate in addition to the combination of punicalagin with zinc (Zn) II, appear to show powerful inhibitory activity against SARS-CoV-2. MATERIALS AND METHODS: The 3CL protease assay kit was used to quantify 3CL protease action. The tetrazolium dye, MTS, was used to evaluate cytotoxicity. RESULTS: Punicalagin showed inhibitory action against the 3CL-protease in a dose-dependent manner, and IC50 was found to be 6.192 µg/ml for punicalagin. Punicalagin (10 µg/mL) demonstrated a significant inhibitory activity toward 3CL-protease activity (p < 0.001), yet when punicalagin is combined with zinc sulfate monohydrate (punicalagin/Zn-II) extremely strong 3CL-protease activity (p < 0.001) was obtained. The action of 3CL-protease with punicalagin/Zn-II was decreased by approximately 4.4-fold in contrast to only punicalagin (10 µg/mL). Likewise, we did not notice any significant cytotoxicity caused by punicalagin, Zn-II, or punicalagin/Zn-II. CONCLUSIONS: We suggest that these compounds could be used as potential antiviral drugs against COVID-19.