ABSTRACT
Coronavirus disease-2019 (COVID-19) has shattered the public health delivery system of most of the countries of the world. COVID-19 displays variable clinical presentations. The severe COVID-19 represents a fulminant pathological condition and most of the patients run a downhill course if extensive medical measures are not adopted. The major challenges about COVID-19 are related to develop strategies to manage huge populations of mild and moderate cases of COVID-19 with two realistic purposes: (1) early negativity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and (2) arrest of progression of moderate COVID-19 patients from developing severe complications. Although several medications have been repurposed for these purposes, none of these have passed the test of time in global perspective. Thus, there remains a pressing need to develop new and novel innovative management strategies for these patients as new variants of SARS-CoV-2 have been destroying the normal public health delivery system of different countries from time to time. The study presented here has checked the safety and efficacy of a herbal medication, leaves of Euphorbia neriifolia Linn (E. neriifolia), in mild and moderate COVID-19 patients. Sixty patients (30 mild COVID-19 and 30 moderate COVID-19) were enrolled in the study. Fifteen mild COVID-19 patients received standard of care (SOC) management, and the remaining 15 patients received SOC plus E. neriifolia. The moderate COVID-19 patients similarly received either SOC (N = 15) or SOC plus E. neriifolia (N = 15). Although there were marked diversity regarding biochemical parameters of these patients at entry, the moderate COVID-19 patients receiving E. neriifolia showed decrease in C-reactive protein and D-dimer and increase in oxygen saturation 7 days after trial commencement. However, these improvements were not detected in moderate COVID-19 patients receiving SOC. Hospital staying was significantly lower in both mild and moderate COVID-19 patients receiving SOC plus E. neriifolia than those receiving only SOC. Taken together, it may be proposed that usage of E. neriifolia may have beneficial effects regarding management for COVID-19 patients, especially for those in developing and resource-constrained countries, although a conclusive statement may not be given due to small sample size. This herbal medication is also pertinent in the context of emergence of OMICRON variant of COVID-19 as the overload of SARS-CoV-2-infecetd patients may be addressed considerably by this medication without hospitalization, if proper communication between patients and physicians can be ensured. How to cite this article Pramanik MEA, Miah MMZ, Ahmed I, et al. Euphorbia neriifolia Leaf Juice on Mild and Moderate COVID-19 Patients: Implications in OMICRON Era. Euroasian J Hepato-Gastroenterol 2022;12(1):10–18.
ABSTRACT
Novel Coronavirus with its highly transmittable characteristics is rapidly spreading, endangering millions of human lives and the global economy. To expel the chain of alteration and subversive expansion, early and effective diagnosis of infected patients is immensely important. Unfortunately, there is a lack of testing equipment in many countries as compared with the number of infected patients. It would be desirable to have a swift diagnosis with identification of COVID-19 from disease genes or from CT or X-Ray images. COVID-19 causes flus, cough, pneumonia, and lung infection in patients, wherein massive alveolar damage and progressive respiratory failure can lead to death. This paper proposes two different detection methods - the first is a Gene-based screening method to detect Corona diseases (Middle East respiratory syndrome-related coronavirus, Severe acute respiratory syndrome coronavirus 2, and Human coronavirus HKU1) and differentiate it from Pneumonia. This novel approach to healthcare utilizes disease genes to build functional semantic similarity among genes. Different machine learning algorithms - eXtreme Gradient Boosting, Naïve Bayes, Regularized Random Forest, Random Forest Rule-Based Model, Random Ferns, C5.0 and Multi-Layer Perceptron, are trained and tested on the semantic similarities to classify Corona and Pneumonia diseases. The best performing models are then ensembled, yielding an accuracy of nearly 93%. The second diagnosis technique proposed herein is an automated COVID-19 diagnostic method which uses chest X-ray images to classify Normal versus COVID-19 and Pneumonia versus COVID-19 images using the deep-CNN technique, achieving 99.87% and 99.48% test accuracy. Thus, this research can be an assistance for providing better treatment against COVID-19.