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The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

An epigenetic modification is DNA N4-methylcytosine (4mC) that affects several biological functions without altering the DNA nucleotides, including DNA conformation, cell development, replication, stability, and DNA structural changes. To prevent restriction enzyme from damaging self-DNA, 4mC performs a critical role in restriction-modification functions. Existing studies mainly focused on finding hand-crafted features to identify 4mC locations, but these methods are inefficient due to high time consuming and high costs. In our research work, we propose a 4mC-CGRU which is a deep learning-based computational model with a standard encoding method to identify the 4mC sites from DNA sequences that learned autonomous feature selection in the Rosaceae genome, particularly in Rosa chinensis (R. chinensis) and Fragaria vesca (F. vesca). The proposed model consists of a convolutional neural network (CNN) and a gated recurrent unit network (GRU)-based model for identifying 4mC sites from Fragaria vesca and Rosa chinensis in the genomes. The CNN model extracts useful features from the datasets and the GRU classifies the DNA sequences. Thus, our approach can automatically extract important features to detect relative sites from DNA sequence. The performance analysis shows that the proposed model consistently outperforms over the state-of-the-art works in detecting 4mC sites., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

The receptor-binding domain (RBD) of SARS-CoV-2 spike (S) protein plays a vital role in binding and internalization through the alpha-helix (AH) of human angiotensin-converting enzyme 2 (hACE2). Thus, it is a potential target for designing and developing antiviral agents. Inhibition of RBD activity of the S protein may be achieved by blocking RBD interaction with hACE2. In this context, inhibitors with large contact surface area are preferable as they can form a potentially stable complex with RBD of S protein and would not allow RBD to come in contact with hACE2. Peptides represent excellent features as potential anti-RBD agents due to better efficacy, safety, and tolerability in humans compared to that of small molecules. The present study has selected 645 antiviral peptides known to inhibit various viruses and computationally screened them against the RBD of SARS-CoV-2 S protein. In primary screening, 27 out of 645 peptides exhibited higher affinity for the RBD of S protein compared to that of AH of the hACE2 receptor. Subsequently, AVP1795 appeared as the most promising candidate that could inhibit hACE2 recognition by SARS-CoV 2 as was predicted by the molecular dynamics simulation. The critical residues in RBD found for protein-peptide interactions are TYR 489, GLY 485, TYR 505, and GLU 484. Peptide-protein interactions were substantially influenced by hydrogen bonding and hydrophobic interactions. This comprehensive computational screening may provide a guideline to design the most effective peptides targeting the spike protein, which could be studied further in vitro and in vivo for assessing their anti-SARS CoV-2 activity., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Emerging contaminants (ECs) in wastewater have recently attracted the attention of researchers as they pose significant risks to human health and wildlife. This paper presents the state-of-art technologies used to remove ECs from wastewater through a comprehensive review. It also highlights the challenges faced by existing EC removal technologies in wastewater treatment plants and provides future research directions. Many treatment technologies like biological, chemical, and physical approaches have been advanced for removing various ECs. However, currently, no individual technology can effectively remove ECs, whereas hybrid systems have often been found to be more efficient. A hybrid technique of ozonation accompanied by activated carbon was found significantly effective in removing some ECs, particularly pharmaceuticals and pesticides. Despite the lack of extensive research, nanotechnology may be a promising approach as nanomaterial incorporated technologies have shown potential in removing different contaminants from wastewater. Nevertheless, most existing technologies are highly energy and resource-intensive as well as costly to maintain and operate. Besides, most proposed advanced treatment technologies are yet to be evaluated for large-scale practicality. Complemented with techno-economic feasibility studies of the treatment techniques, comprehensive research and development are therefore necessary to achieve a full and effective removal of ECs by wastewater treatment plants., (Copyright © 2021 Elsevier B.V. All rights reserved.)

COVID-19 has heightened human suffering, undermined the economy, turned the lives of billions of people around the globe upside down, and significantly affected the health, economic, environmental and social domains. This study aims to provide a comprehensive analysis of the impact of the COVID-19 outbreak on the ecological domain, the energy sector, society and the economy and investigate the global preventive measures taken to reduce the transmission of COVID-19. This analysis unpacks the key responses to COVID-19, the efficacy of current initiatives, and summarises the lessons learnt as an update on the information available to authorities, business and industry. This review found that a 72-hour delay in the collection and disposal of waste from infected households and quarantine facilities is crucial to controlling the spread of the virus. Broad sector by sector plans for socio-economic growth as well as a robust entrepreneurship-friendly economy is needed for the business to be sustainable at the peak of the pandemic. The socio-economic crisis has reshaped investment in energy and affected the energy sector significantly with most investment activity facing disruption due to mobility restrictions. Delays in energy projects are expected to create uncertainty in the years ahead. This report will benefit governments, leaders, energy firms and customers in addressing a pandemic-like situation in the future., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.)

The rapid spread of COVID-19 has led to nationwide lockdowns in many countries. The COVID-19 pandemic has played serious havoc on economic activities throughout the world. Researchers are immensely curious about how to give the best protection to people before a vaccine becomes available. The coronavirus spreads principally through saliva droplets. Thus, it would be a great opportunity if the virus spread could be controlled at an early stage. The face mask can limit virus spread from both inside and outside the mask. This is the first study that has endeavoured to explore the design and fabrication of an antiviral face mask using licorice root extract, which has antimicrobial properties due to glycyrrhetinic acid (GA) and glycyrrhizin (GL). An electrospinning process was utilized to fabricate nanofibrous membrane and virus deactivation mechanisms discussed. The nanofiber mask material was characterized by SEM and airflow rate testing. SEM results indicated that the nanofibers from electrospinning are about 15-30 μm in diameter with random porosity and orientation which have the potential to capture and kill the virus. Theoretical estimation signifies that an 85 L/min rate of airflow through the face mask is possible which ensures good breathability over an extensive range of pressure drops and pore sizes. Finally, it can be concluded that licorice root membrane may be used to produce a biobased face mask to control COVID-19 spread., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Our laboratory is focused on investigating the supportive role of P53 towards the maintenance of lung homeostasis. Acute lung injury, acute respiratory distress syndrome, chronic obstructive pulmonary disease, pulmonary fibrosis, bronchial asthma, pulmonary arterial hypertension, pneumonia and tuberculosis are respiratory pathologies, associated with dysfunctions of this endothelium defender (P53). Herein we review the evolving role of P53 towards the aforementioned inflammatory disorders, to potentially reveal new therapeutic possibilities in pulmonary disease., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The tumor suppressor protein P53 is strongly involved in orchestrating cellular defenses in the diverse variety of human tissues. Anomalies to lung endothelium permeability are streaming severe consequences towards human health, often associated with fatal outcomes. Ongoing investigations suggest that P53 exerts a prominent strategic role in crucial signaling cascades, in charge of both the maintenance and defense of pulmonary endothelium against toxic intruders. The current study employs human and bovine lung microvascular cells, as well as pharmacologic and genetic P53 modulators to demonstrate the negative regulation of APE1/Ref1 by P53. Moreover, it includes real time measurements of endothelial permeability, to reveal the disruptive role of APE1/Ref1 towards endothelial integrity. Those findings supports our efforts to elucidate the highly sophisticated regulatory network that enact endothelial adaptations under the plethora of challenging environmental factors., (Copyright © 2019 Elsevier GmbH. All rights reserved.)