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Journal of Environmental Engineering (United States) ; 149(6), 2023.
Article in English | Scopus | ID: covidwho-2248079

ABSTRACT

In recent years, the emergence of COVID-19 has created disastrous health effects worldwide. Doxycycline, a member of the tetracycline group, has been prescribed as a treatment companion for attending this catastrophe. Due to extensive use and high solubility, a significant amount of un-metabolized doxycycline has been found to reach water bodies within a short time, and consumption of this water may lead to the development of fatal resistance in organisms and create health problems. Therefore, it has become necessary to develop suitable technologies from a geoenvironmental point of view to remove these unwanted antibiotics from wastewater. In this context, locally obtainable silty-sandy soil was explored as a low-cost material in a constructed wetland with Chrysopogon zizanioides (vetiver sp.) for phytoremediation to mitigate doxycycline spiked wastewater. The obtained soil hydraulic conductivity was 1.63×10-7 m/s. Batch adsorption tests conducted on silty-sandy soil, vetiver leaf, and vetiver root provided maximum removal efficiencies of 90%, 72%, and 80% percent, respectively, at optimal sorbent doses of 10 g/L, 17 g/L, and 16 g/L, and contaminant concentrations of 25 mg/L, 20 mg/L, and 23 mg/L, with a 30-min time of contact. The Freundlich isotherm was the best fit, indicative of sufficient sorption capacity of all the adsorbents for doxycycline. The best match in the kinetic research was pseudo-second-order kinetics. A one dimensional vertical column test with the used soil on doxycycline revealed a 90% breakthrough in 24 h for a soil depth of 30 mm. Studies on a laboratory-scale wetland and numerically modeled yielded removal of around 92% by the selected soil and about 98% combined with Chrysopogon zizanioides for 25 mg/L of initial doxycycline concentration, which is considered quite satisfactory. Simulated results matched the laboratory tests very well. The study is expected to provide insight into remedies for similar practical problems. © 2023 American Society of Civil Engineers.

2.
Annals of International Medical and Dental Research ; 8(2):128-134, 2022.
Article in English | CAB Abstracts | ID: covidwho-1935071

ABSTRACT

Background: Acute respiratory distress syndrome requiring invasive mechanical ventilation may occur in COVID-19 patients. Barotrauma causes clinically severe pneumothorax, necessitating a chest tube thoracostomy. Acute respiratory syndrome coronavirus 2 is aerosolized during the process, hence specific precautions must be taken to minimize exposure risks to health care workers. Objectives: The objective of the study to diagnosis of Tube thoracostomy during the COVID-19 pandemic to detect and diagnose patients who are positive with the virus. Material & Methods: In Bangladesh, researchers from a tertiary care hospital's thoracic surgery section did a retrospective analysis. In total, we had 34 participants. All COVID-19 cases requiring thoracic surgery consultation and management that were admitted to the ICU between July 2020 and January 2022 were included in this study. Iatrogenic pneumothorax and other critical cases not associated with COVID-19 were also eliminated.

3.
Cancer Research ; 81(13):1, 2021.
Article in English | Web of Science | ID: covidwho-1377273
4.
Studies in Systems, Decision and Control ; 358:551-565, 2021.
Article in English | Scopus | ID: covidwho-1340321

ABSTRACT

The devastating outbreak of the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus) also known as COVID-2019 has succeeded in introducing the danger to the worldwide living. Now COVID-19 is among the main potentially deadly issues in the world. Rapid and precise identification of COVID-19 infection is important to diagnose, make informed assumptions, and assure that patients receive care that aims to save people’s health and life. The entire community is making enormous endeavors to tackle the spreading of such a horrible epidemic in forms of communications true, economy, information sources, safety equipment, existence-risk treatments, and many on this and many other tools. Coronavirus triggers a vast range of viral disease;however, it is a virus of the kind RNA which can affect all humans and animals. Coronavirus is now identified in this chapter uses a form of deep learning which is a sub-branch of artificial intelligence. This chapter suggests making use of the deep learning algorithms with such a view to recognizing its daily incremental behavior and predicting the potential accessibility of COVID-2019 throughout civilizations using real-time data knowledge. COVID-19 Quick diagnosis and It is necessary to identify high-risk patients with the worst diagnosis for early treatment and optimization of medical services. And the algorithm of deep learning helps to do that and helps to combat the COVID-19 and the deep learning algorithms are more time saving;less expensive;easy to operate. COVID-19 research through deep learning involves the patient’s x-rays of the lungs and the fundamental concept is to identify the ultrasound as impaired or usual COVID. In general, the issue is a collection of identification algorithms whereby we identify Standard normal v/s COVID-19 cases. There are several benefits and drawbacks to utilizing Deep Learning to solve these circumstances. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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