Computational Risk Assessment of Persistence, Bioaccumulation, and Toxicity of Novel Flame-Retardant Chemicals.

Novel brominated flame retardants (NBFRs) have emerged as chemicals of environmental concern, as they have been widely used as an alternative to polybrominated diphenyl ethers (PBDEs). Considering the similar structural features of NBFRs and PBDEs necessitates a comprehensive investigation to understand the physicochemical relationships of these compounds and their ability to alter biological functions. In this study, we investigated the persistent nature of NBFRs in terms of thyroid-disrupting potential by understanding the structure-stability aspects using density functional theory (DFT)-based reactivity parameters and interactions via molecular docking and molecular dynamics (MD) simulations. The results indicate that the DFT-based stability descriptor (chemical hardness) is associated with the persistent nature of NBFRs. The computed molecular interaction profile revealed prominent interactions between thyroid receptor-ß (TR-ß) and NBFRs. Stable trajectory and interactions with TR-ß were obtained with ATE, p-TBX, PBT, PBEB, and TBBPA-DBPE during 100 ns of MD simulation. The results of these studies have suggested that the presence of a higher number of halogenated atoms increases the stability vis-à-vis the persistence and endocrine disruption potential of NBFRs.

Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.

The use of artificial intelligence (AI) and the Internet of Things (IoT), which is a developing technology in medical applications that assists physicians in making more informed decisions regarding patients' courses of treatment, has become increasingly widespread in recent years in the field of healthcare. On the other hand, the number of PET scans that are being performed is rising, and radiologists are getting significantly overworked as a result. As a direct result of this, a novel approach that goes by the name "computer-aided diagnostics" is now being investigated as a potential method for reducing the tremendous workloads. A Smart Lung Tumor Detector and Stage Classifier (SLD-SC) is presented in this study as a hybrid technique for PET scans. This detector can identify the stage of a lung tumour. Following the development of the modified LSTM for the detection of lung tumours, the proposed SLD-SC went on to develop a Multilayer Convolutional Neural Network (M-CNN) for the classification of the various stages of lung cancer. This network was then modelled and validated utilising standard benchmark images. The suggested SLD-SC is now being evaluated on lung cancer pictures taken from patients with the disease. We observed that our recommended method gave good results when compared to other tactics that are currently being used in the literature. These findings were outstanding in terms of the performance metrics accuracy, recall, and precision that were assessed. As can be shown by the much better outcomes that were achieved with each of the test images that were used, our proposed method excels its rivals in a variety of respects. In addition to this, it achieves an average accuracy of 97 percent in the categorization of lung tumours, which is much higher than the accuracy achieved by the other approaches.

The dual role of phytochemicals on SARS-CoV-2 inhibition by targeting host and viral proteins.

BACKGROUND: The severe acute respiratory syndrome-2019 has affected more than 190 million people around the world and caused severe crises throughout the globe. Due to rapid mutation in the viral genome, its became important to simultaneously improvise the host immunity while targeting viral proteins to reduce the severity of infection. AIM: The current computational work focuses on multi-level rigorous screening of 47 medicinal plant-based phytochemicals for discovering effective phytochemical inhibitors against the host and viral targets. EXPERIMENTAL PROCEDURE: A total of 586 phytochemicals were analyzed in detail based on their drug-likeness, pharmacological properties, and structure-based activity against the viral proteins (Spike glycoprotein, Papain-like protease, and Main protease) and host proteins (ACE2, Importin-subunit α-5, and ß-1). Phytochemicals showing higher binding affinity with the dual capacity to target both the categories of proteins were further analyzed by profiling of their chemical reactivity using Density-Functional Theory (DFT) based quantum chemical methods. Finally, detailed molecular dynamics simulations were performed to analyze the interactions of the complexes. RESULTS AND CONCLUSION: The results revealed that the selected phytochemicals from Andrographis paniculata, Aconitum heterophyllum, Costus speciosus and Inula racemosa may have the capacity to act with prominent affinity towards the host and viral proteins. Therefore, the combination of active phytochemicals of these plants may prove to be more beneficial and can be used for developing the potential phytotherapeutic intervention.

Computational risk assessment framework for the hazard analysis of bisphenols and quinone metabolites.

Bisphenol A (BPA) is a widely used chemical in plastics but its proven harmful effects has led to the replacement and production of its analogs that might also induce hazard as well as associated risks. To elucidate the adverse impact of the BPA analogs, a comprehensive computational framework is developed which applies toxicogenomics aligned with Density Functional Theory (DFT) and Molecular Dynamics (MD) based approaches to understand the toxic potential of quinone metabolites of Bisphenol F (BPF) and 3,3'-dimethylbisphenol A (DMBPA). The obtained results indicate a similar chemical reactivity profile for these metabolites of bisphenols to BPA metabolite. MD simulation revealed that the quinone metabolites tend to interact with the DNA comprising hydrogen bonding, van der Waals forces, and electrostatic interactions as an onset for covalent binding to adduct formation. Structural analysis suggests that interactions with DC9, DG10, DG16, DA17, DA18, and DT19 play a crucial role in stabilizing the quinone metabolite in the interactive pocket of DNA. These observations are demonstrating that BPF and DMBPA have the potential to impose genotoxicity via forming the quinone metabolite adducts. Combination of DFT and MD-based computational approaches providing a structure-activity-toxicity spectrum of chemicals can serve for the purpose of risk assessment.

Environmental survival of SARS-CoV-2 - A solid waste perspective.

The advent of COVID-19 has kept the whole world on their toes. Countries are maximizing their efforts to combat the virus and to minimize the infection. Since infectious microorganisms may be transmitted by variety of routes, respiratory and facial protection is required for those that are usually transmitted via droplets/aerosols. Therefore this pandemic has caused a sudden increase in the demand for personal protective equipment (PPE) such as gloves, masks, and many other important items since, the evidence of individual-to-individual transmission (through respiratory droplets/coughing) and secondary infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). But the disposal of these personal protective measures remains a huge question mark towards the environmental impact. Huge waste generation demands proper segregation according to waste types, collection, and recycling to minimize the risk of infection spread through aerosols and attempts to implement measures to monitor infections. Hence, this review focuses on the impact of environment due to improper disposal of these personal protective measures and to investigate the safe disposal methods for these protective measures by using the safe, secure and innovative biological methods such as the use of Artificial Intelligence (AI) and Ultraviolet (UV) lights for killing such deadly viruses.

Analogue discovery of safer alternatives to HCQ and CQ drugs for SAR-CoV-2 by computational design.

COVID-19 outbreak poses a severe health emergency to the global community. Due to availability of limited data, the selection of an effective treatment is a challenge. Hydroxychloroquine (HCQ), a chloroquine (CQ) derivative administered for malaria and autoimmune diseases, has been shown to be effective against both Severe Acute Respiratory Syndrome (SARS-CoV-1) and SARS-CoV-2. Apart from the known adverse effects of these drugs, recently the use of CQ and HCQ as a potential treatment for COVID-19 is under flux globally. In this study, we focused on identifying a more potent analogue of HCQ and CQ against the spike protein of SAR-CoV-2 that can act as an effective antiviral agent for COVID-19 treatment. Systematic pharmacokinetics, drug-likeness, basicity predictions, virtual screening and molecular dynamics analysis (200 ns) were carried out to predict the inhibition potential of the analogous compounds on the spike protein. This work identifies the six potential analogues, out of which two compounds, namely 1-[1-(6-Chloroquinolin-4-yl) piperidin-4-yl]piperidin-3-ol and (1R,2R)-2-N-(7-Chloroquinolin-4-yl)cyclohexane-1,2-diamine interact with the active site of the spike protein similar to HCQ and CQ respectively with augmented safety profile.

Integrated QSAR and Adverse Outcome Pathway Analysis of Chemicals Released on 3D Printing Using Acrylonitrile Butadiene Styrene.

Additive manufacturing commonly known as 3D printing has numerous applications in several domains including material and biomedical technologies and has emerged as a tool of capabilities by providing fast, highly customized, and cost-effective solutions. However, the impact of the printing materials and chemicals present in the printing fumes has raised concerns about their adverse potential affecting humans and the environment. Thus, it is necessary to understand the properties of the chemicals emitted during additive manufacturing for developing safe and biocompatible fibers having controlled emission of fumes including its sustainable usage. Therefore, in this study, we have developed a computational predictive risk-assessment framework on the comprehensive list of chemicals released during 3D printing using the acrylonitrile butadiene styrene (ABS) filament. Our results showed that the chemicals present in the fumes of the ABS-based fiber used in additive manufacturing have the potential to lead to various toxicity end points such as inhalation toxicity, oral toxicity, carcinogenicity, hepatotoxicity, and teratogenicity. Moreover, because of their absorption, distribution in the body, metabolism, and excretion properties, most of the chemicals exhibited a high absorption level in the intestine and the potential to cross the blood-brain barrier. Furthermore, pathway analysis revealed that signaling like alpha-adrenergic receptor signaling, heterotrimeric G-protein signaling, and Alzheimer's disease-amyloid secretase pathway are significantly overrepresented given the identified target proteins of these chemicals. These findings signify the adversities associated with 3D printing fumes and the necessity for the development of biodegradable and considerably safer fibers for 3D printing technology.