ABSTRACT
Coronavirus disease-2019 (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), leading to a pandemic and traumatizing the world. To prevent the spread of virus, infected people were isolated as per the strict protocols. Due to the genome sequence of the virus being strikingly similar to that of SARS-CoV, many antiviral medicines previously approved to treat SARS and MERS are now being repurposed for the plausible treatment COVID-19. To combat SARS-CoV-2, a slew of experimental and clinical medicine and vaccine trials are currently underway worldwide. In the fight against COVID-19 infection, a variety of natural substances are also being searched extensively. Coumarins and chalcones are two important natural chemical classes. They can be found in a wide range of natural products and have many pharmacological effects. SARS-CoV-2 and other coronaviruses were successfully treated with these drugs, which showed significant antiviral activity. This chapter discusses the possible role of coumarins and chalcones in SARS-CoV-2 infection treatment. © 2023 Elsevier Inc. All rights reserved.
ABSTRACT
Artificial intelligence (AI), deep learning (DL), and neural networks (NN), though these words sound flashy and may leave you perplexed, represent powerful technologies that have the capabilities to transform the world. It is just now emerging how valuable these machine learning-based techniques are and how they can solve many real-world problems ranging from fraud detection, resource management to driver-less cars.One such field where the application of AI systems is progressively growing is in medical diagnosis. A lot of research is going on to enhance computer-Aided diagnosis and detection of diseases. Recent world events have tested the healthcare systems all around the world. Suppose we have sophisticated deep learning systems (DLS) that could help in faster and efficient disease detection and diagnosis;how beneficial it would be to assist both medical professionals and patients.This study explores how AI and machine learning techniques could be used for disease detection, giving COVID-19 and Diabetic Retinopathy detection examples. We present two deep learning (DL) models, one to detect COVID-19 from chest x-ray image scans and the other to detect Diabetic Retinopathy at various stages of the disease from retinal fundus images. With reasonably high accuracy, >95% for the COVID-19 detection model and >80% for the Diabetic Retinopathy detection model, these results highlight AI and deep learning potential to assist general practitioners. © 2022 IEEE.
ABSTRACT
Present study points out the impact of Lockdown on the health of the Yamuna river at Delhi stretch by comparing prelockdown and Post-lockdown period by studying the reports of pollution monitoring agencies. Delhi segment of the Yamuna is highly polluted, where alongwith domestic sewage a huge quantity of industrial waste is being discharged continuously without proper treatment. Pre lockdown (March 2020) water quality parameters at three sampling stations named as Palla, Nizammuddin Bridge and Okhla barrage U/s in Delhi were, pH were 8.7, 7.3 and 7.2, DO were 17.1 mg/L, not detected in later two sites, BOD were 7.9 mg/L, 57 mg/L and 27 mg/L and COD were 28 mg/L, 90 mg/L and 95 mg/L respectively and postlockdown period (April 2020) the pH was 7.8, 7.2 and 7.1, DO was 8.3 mg/L, 2.4 mg/L and 1.2 mg/L BOD was 2 mg/L, 5.6 mg/L and 6.1 mg/L and COD were 6 mg/L, 16 mg/L and 18 mg/L respectively. The study of these parameters at three sampling stations reveals that the lack of industrial pollutants discharging due to nationwide lockdown for COVID-19 pandemic had positive effect on water quality of this river. Water quality could be maintained by planned establishment of industries and setup of ETP with without gap between generation and treatment.