The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic's dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.
Microplastics (particles smaller than 5 mm) are among the most common pollutants in aquatic habitats because they may develop to high densities and can interact with both the abiotic and biotic environments. There is less information available on microplastics in the freshwater systems than there is in the marine environment. This study aims to shed light on the abundance and spatial distribution of microplastics in the Brahmaputra River (Mymensingh) through the utilization of the wet peroxide oxidation isolation technique, supplemented with sodium chloride, to examine fish and sediment specimens collected between December 21, 2022 and January 12, 2023. A total of 26 and 189 microplastic particles were identified in the fish and sediment samples, respectively. Microplastics (MPs) concentrations in fish gut ranged from 0.5 ± 0.7 to 1.67 ± 0.58 MPs individual-1. The most prevalent shape found in fish stomachs was fiber (46%), and the most common color was transparent (32%). Sizes 0.5-1 mm (1.6 ± 0.74) had the most microplastics. This study found that fishes from the demersal (3.25 ± 1.7) zone had more MPs than the benthopelagic (2.5 ± 0.58) and pelagic (1.5 ± 0.7) zones. Omnivorous fishes (54%) consumed more microplastics than carnivorous (31%, 2.6 ± 0.58) and herbivorous fishes (15%,1.33 ± 0.94). Microplastic consumption had a moderate correlation with fish body weight (r = 0.34), length (r = 0.46), and gastrointestinal content (r = 0.45). The MPs per kilogram of Brahmaputra River bed sediment ranged from 8 to 31, with a mean abundance of 18.9 ± 7.01 particles kg-1. The most common shape identified in this study was fragments (52%) and 33% of sediment microplastics were blue in color. Microplastics were most abundant in the 1-3 m-meter size class. Fourier transform infrared spectroscopy (FTIR) showed that polypropylene (PP) was the most prevalent MP in both fish (34%) and sediment (40%) samples. In this study, the Pollution load index (PLI) for each sampling site is <10, with the highest value found for station 2(1.97 ± 0.49), regarded as risk category I. This study's results will be useful for future research on microplastics in freshwater environments. PRACTITIONER POINTS: Abundance and distribution of microplastics were determined from the longest river of Bangladesh. The structural properties of microplastics were characterized using ATR-FTIR spectroscopy. Pollution load index (PLI) of microplastics was investigated.