Profiling of phthalates, brominated, and organophosphate flame retardants in COVID-19 lockdown house dust; implication on the human health.

In this study, brominated flame retardants (BFRs), phthalates, and organophosphate flame retardants (PFRs) were analyzed in indoor household dust collected during the COVID-19 related strict lockdown (April-July 2020) period. Floor dust samples were collected from 40 households in Jeddah, Saudi Arabia. The levels of most of the analyzed chemicals were visibly high and for certain chemicals multifold high in analyzed samples compared to earlier studies on indoor dust from Jeddah. Bis (2-ethylhexyl) phthalate (DEHP) was the primary chemical in these dust samples, with a median concentration of 769,500 ng/g of dust. Tris (2-butoxy ethyl) phosphate (TBEP) and Decabromodiphenyl ether (BDE 209) contributed the highest among PFRs and BFRs with median levels of 5990 and 940 ng/g of dust, respectively. The estimated daily exposure in the worst case scenario (23,700 ng/kg bw/day) for Saudi children was above the reference dose (20,000 ng/kg bw/day) for DEHP, and the hazardous index (HI) was also >1. The long-term carcinogenic risk was above the 1 × 10-5, indicating a risk to the health of Saudi young children from getting exposed to DEHP from indoor dust. This study draws attention to the increased indoor pollution during the lockdown period when all of the daily activities by adults and children were performed indoors, which negatively impacted human health, as suggested by the calculated risk. However, the current study has limitations and warrants more monitoring studies from different parts of the world to understand the phenomenon. At the same time, this study also highlights another side of COVID-19 related to our lives.

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.

It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images.

Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.

Attitudes toward the SARS-CoV-2 Vaccine: Results from the Saudi Residents' Intention to Get Vaccinated against COVID-19 (SRIGVAC) Study.

Vaccine uptake could influence vaccination efforts to control the widespread COVID-19 pandemic; however, little is known about vaccine acceptance in Saudi Arabia. The present study aimed to assess the Saudi public's intent to get vaccinated against COVID-19 and explore the associated demographic determinants of their intentions as well as the reasons for vaccine hesitancy. A cross-sectional, web-based survey was distributed to public individuals in Saudi Arabia between 25 December 2020 and 15 February 2021. Participants were asked if they were willing to get vaccinated, and the responses, along with demographic data were entered into a multinomial logistic regression model to assess the relative risk ratio (RRR) for responding "no" or "unsure" versus "yes". Among 3048 participants (60.1% female, 89.5% Saudi), 52.9% intend to get vaccinated, 26.8% were unsure, and 20.3% refused vaccination. Vaccine hesitancy was significantly higher among females (RRR = 2.70, p < 0.0001) and those who had not been recently vaccinated for influenza (RRR = 2.63, p < 0.0001). The likelihood was lower among Saudis (RRR = 0.49, p < 0.0001), those with less than a secondary education (RRR = 0.16, p < 0.0001), perceived risks of COVID-19, and residents of the southern region (RRR = 0.46, p < 0.0001). The most often cited reasons for hesitancy were short clinical testing periods and concerns about adverse events or effectiveness. Vaccine hesitancy is mediated by many demographic factors and personal beliefs. To address vaccine-related concerns and amend deeply rooted health beliefs, communication should provide transparent information.

Molecular Docking Studies of HIV TAT and sitagliptin nano-formula as potential therapeutic targeting SARS-CoV2 protease

The outbreak of COVID-19 pandemic regarded as a major health/economic hazard. The importance of coming up with mechanisms for preventing or treating SARS-CoV-2infection has been felt across the world. This work aimed at examining the efficiency of Sitagliptin (SIT) and human immunodeficiency virus type 1 (HIV-1) trans-activator transcription peptide (TAT) against SARS-CoV-2 virus. 3CL-protease inhibition activity and docking studies were examined. According to the results, the prepared complex’s formula was as follows 1: 1 SIT: TAT molar ratio, whereas zeta potential and particle size values were at 34.17 mV and 97.19 nm, respectively. This combination did exhibit its antiviral potentiality against SARS-CoV-2 via IC50 values of 9.083 5.415, and 16.14 μM for TAT, SIT-TAT, and SIT, respectively. In addition, the complex SIT-TAT showed a significant (P < 0.001) viral-3CL-protease inhibitory effect. This was further confirmed via in silico study. Molecular docking investigation has shown promising binding affinity of the formula components towards SARS-CoV-2 main protease (3-CL).

Scaffold Hopping of α-Rubromycin Enables Direct Access to FDA-Approved Cromoglicic Acid as a SARS-CoV-2 MPro Inhibitor.

The COVID-19 pandemic is still active around the globe despite the newly introduced vaccines. Hence, finding effective medications or repurposing available ones could offer great help during this serious situation. During our anti-COVID-19 investigation of microbial natural products (MNPs), we came across α-rubromycin, an antibiotic derived from Streptomyces collinus ATCC19743, which was able to suppress the catalytic activity (IC50 = 5.4 µM and Ki = 3.22 µM) of one of the viral key enzymes (i.e., MPro). However, it showed high cytotoxicity toward normal human fibroblasts (CC50 = 16.7 µM). To reduce the cytotoxicity of this microbial metabolite, we utilized a number of in silico tools (ensemble docking, molecular dynamics simulation, binding free energy calculation) to propose a novel scaffold having the main pharmacophoric features to inhibit MPro with better drug-like properties and reduced/minimal toxicity. Nevertheless, reaching this novel scaffold synthetically is a time-consuming process, particularly at this critical time. Instead, this scaffold was used as a template to explore similar molecules among the FDA-approved medications that share its main pharmacophoric features with the aid of pharmacophore-based virtual screening software. As a result, cromoglicic acid (aka cromolyn) was found to be the best hit, which, upon in vitro MPro testing, was 4.5 times more potent (IC50 = 1.1 µM and Ki = 0.68 µM) than α-rubromycin, with minimal cytotoxicity toward normal human fibroblasts (CC50 > 100 µM). This report highlights the potential of MNPs in providing unprecedented scaffolds with a wide range of therapeutic efficacy. It also revealed the importance of cheminformatics tools in speeding up the drug discovery process, which is extremely important in such a critical situation.

Repurposing of Sitagliptin- Melittin Optimized Nanoformula against SARS-CoV-2: Antiviral Screening and Molecular Docking Studies.

The outbreak of the COVID-19 pandemic in China has become an urgent health and economic challenge. The objective of the current work was to evaluate the efficacy of the combined complex of Sitagliptin (SIT) with melittin (MEL) against SARS-CoV-2 virus. SIT-MEL nano-conjugates were optimized by a full three-factor bi-level (23) factorial design. In addition, SIT concentration (mM, X1), MEL concentration (mM, X2), and pH (X3) were selected as the critical factors. Particle size (nm, Y1) and zeta potential (mV, Y2) were assessed as responses. Characterization of the optimized formula for Fourier-transformed infrared (FTIR) was carried out. The optimized formula showed particle size and zeta potential values of 77.42 nm and 27.67 mV, respectively. When compared with SIT and MEL, the combination of SIT-MEL complex has shown anti-viral potential against isolate of SARS-CoV-2 with IC50 values of 8.439 µM with significant improvement (p < 0.001). In addition, the complex showed IC50 in vitro 3CL-protease inhibition with IC50 7.216 µM. Molecular docking has revealed that formula components have good predicted pocket accommodation of the SARS-CoV-2 3-CL protease. An optimized formulation of SIT-MEL could guarantee both enhanced delivery to the target cells and the enhanced cellular uptake with promising activities against SARS-CoV-2.

Evaluation of the Antiviral Activity of Sitagliptin-Glatiramer Acetate Nano-Conjugates against SARS-CoV-2 Virus.

The outbreak of the COVID-19 pandemic in China has become an urgent health and economic challenge. There is a current race for developing strategies to treat and/or prevent COVID-19 worldwide. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the strain of coronavirus that causes COVID-19. The aim of the present work was to evaluate the efficacy of the combined complex (nano-conjugates) of two FDA-approved drugs, sitagliptin (SIT) and glatiramer acetate (GA), against a human isolate of the SARS-CoV-2 virus. SIT-GA nano-conjugates were prepared according to a full three-factor bilevel (23) factorial design. The SIT concentration (mM, X1), GA concentration (mM, X2), and pH (X3) were selected as the factors. The particle size (nm, Y1) and zeta potential (mV, Y2) were assessed as responses. Characterization of the optimized formula for the Fourier-transform infrared (FTIR) spectroscopy and transmission electron microscopy (TEM) was carried out. In addition, the half-maximal inhibitory concentration (IC50) in Vero-E6 epithelial cells previously infected with the virus was investigated. The results revealed that the optimized formula of the prepared complex was a 1:1 SIT:GA molar ratio at a pH of 10, which met the required criteria with a desirability value of 0.878 and had a particle size and zeta potential at values of 77.42 nm and 27.67 V, respectively. The SIT-GA nano-complex showed antiviral potential against an isolate of SARS-CoV-2 with IC50 values of 16.14, 14.09, and 8.52 µM for SIT, GA, and SIT-GA nano-conjugates, respectively. Molecular docking has shown that the formula's components have a high binding affinity to the COVID 3CL protease, essential for coronavirus replication, paralleled by 3CL protease inhibition (IC50 = 2.87 µM). An optimized formulation of SIT-GA could guarantee both enhanced deliveries to target cells and improved cellular uptake. Further clinical studies are being carried out to validate the clinical efficacy of the optimized formulation against SARS-CoV-2.

Exploring the Potential of Carbon Dots to Combat COVID-19.

Viral diseases are considered as a global burden. The eradication of viral diseases is always a challenging task in medical research due to the high infectivity and mutation capability of the virus. The ongoing COVID-19 pandemic is still not under control even after several months of the first reported case and global spread. Neither a specific drug nor a vaccine is available for public use yet. In the pursuit of a promising strategy, carbon dots could be considered as potential nanostructure against this viral pandemic. This review explores the possibility of carbon nano-dots to combat COVID-19 based on some reported studies. Carbon dots are photoluminescent carbon nanoparticles, smaller than 10 nm in dimension with a very attractive photostable and biocompatible properties which can be surfaced modified or functionalized. These photoluminescent tiny particles have captured much attention owing to their functionalization property and biocompatibility. In response to this pandemic outbreak, this review attempts to summarize the potential use of carbon dots in antiviral therapy with particular emphasis on their probable role in the battlefront against COVID-19 including their possible biosensing applications.