Repurposing of SARS-CoV-2 compounds against Marburg Virus using MD simulation, mm/GBSA, PCA analysis, and free energy landscape.

The significant mortality rate associated with Marburg virus infection made it the greatest hazard among infectious diseases. Drug repurposing using in silico methods has been crucial in identifying potential compounds that could prevent viral replication by targeting the virus's primary proteins. This study aimed at repurposing the drugs of SARS-CoV-2 for identifying potential candidates against the matrix protein VP40 of the Marburg virus. Virtual screening was performed where the control compound, Nilotinib, showed a binding score of -9.99 kcal/mol. Based on binding scores, hit compounds 9549298, 11960895, 44545852, 51039094, and 89670174 were selected that had a lower binding score than the control. Subsequent molecular dynamics (MD) simulation revealed that compound 9549298 consistently formed a hydrogen bond with the residue Gln290. This was observed both in molecular docking and MD simulation poses, indicating a strong and significant interaction with the protein. 11960895 had the most stable and consistent RMSD pattern exhibited in 100 ns simulation, while 9549298 had the most identical RMSD plot compared to the control molecule. MM/PBSA analysis showed that the binding free energy (ΔG) of 9549298 and 11960895 was lower than the control, with -30.84 and -38.86 kcal/mol, respectively. It was observed by the PCA (principal component analysis) and FEL (free energy landscape) analysis that compounds 9549298 and 11960895 had lesser conformational variation. Overall, this study proposed 9549298 and 11960895 as potential binders of VP40 MARV that can cause its inhibition, however it inherently lacks experimental validation. Furthermore, the study proposes in-vitro experiments as the next step to validate these computational findings, offering a practical approach to further explore these compounds' potential as antiviral agents.Communicated by Ramaswamy H. Sarma.

Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies.

Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.

Use of biogenic silver nanoparticles on the cathode to improve bioelectricity production in microbial fuel cells.

To date, research on microbial fuel cells (MFCs) has. focused on the production of cost-effective, high-performance electrodes and catalysts. The present study focuses on the synthesis of silver nanoparticles (AgNPs) by Pseudomonas sp. and evaluates their role as an oxygen reduction reaction (ORR) catalyst in an MFC. Biogenic AgNPs were synthesized from Pseudomonas aeruginosa via facile hydrothermal synthesis. The physiochemical characterization of the biogenic AgNPs was conducted via scanning electron microscopy (SEM), X-ray diffraction (XRD), and UV-visible spectrum analysis. SEM micrographs showed a spherical cluster of AgNPs of 20-100 nm in size. The oxygen reduction reaction (ORR) ability of the biogenic AgNPs was studied using cyclic voltammetry (CV). The oxygen reduction peaks were observed at 0.43 V, 0.42 V, 0.410 V, and 0.39 V. Different concentrations of biogenic AgNPs (0.25-1.0 mg/cm2) were used as ORR catalysts at the cathode in the MFC. A steady increase in the power production was observed with increasing concentrations of biogenic AgNPs. Biogenic AgNPs loaded with 1.0 mg/cm2 exhibited the highest power density (PDmax) of 4.70 W/m3, which was approximately 26.30% higher than the PDmax of the sample loaded with 0.25 mg/cm2. The highest COD removal and Coulombic efficiency (CE) were also observed in biogenic AgNPs loaded with 1.0 mg/cm2 (83.8% and 11.7%, respectively). However, the opposite trend was observed in the internal resistance of the MFC. The lowest internal resistance was observed in a 1.0 mg/cm2 loading (87 Ω), which is attributed to the high oxygen reduction kinetics at the surface of the cathode by the biogenic AgNPs. The results of this study conclude that biogenic AgNPs are a cost-effective, high-performance ORR catalyst in MFCs.

Cyclodextrin nanoparticles in targeted cancer theranostics.

The field of cancer nanotheranostics is rapidly evolving, with cyclodextrin (CD)-based nanoparticles emerging as a promising tool. CDs, serving as nanocarriers, have higher adaptability and demonstrate immense potential in delivering powerful anti-cancer drugs, leading to promising and specific therapeutic outcomes for combating various types of cancer. The unique characteristics of CDs, combined with innovative nanocomplex creation techniques such as encapsulation, enable the development of potential theranostic treatments. The review here focuses mainly on the different techniques administered for effective nanotheranostics applications of CD-associated complex compounds in the domain of cancer treatments. The experimentations on various loaded drugs and their complex conjugates with CDs prove effective in in vivo results. Various cancers can have potential nanotheranostics cures using CDs as nanoparticles along with a highly efficient process of nanocomplex development and a drug delivery system. In conclusion, nanotheranostics holds immense potential for targeted drug delivery and improved therapeutic outcomes, offering a promising avenue for revolutionizing cancer treatments through continuous research and innovative approaches.

Deep Transfer Learning Technique for Multimodal Disease Classification in Plant Images.

Rice (Oryza sativa) is India's major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

Built-In Calibration Standard and Decision Support System for Controlling Structured Data Storage Systems Using Soft Computing Techniques.

Several research that assesses, or assess computer systems has been undertaken in previous decades. Choosing an appropriate DBMS system in a computer application, though, was never completely arbitrary, based on the professional study. Developing a viable answer for such a challenge depending on business goals and needs from judgment necessitates a thorough study on information access as well as comprehensive professional evaluation. The research presents a DSS to help non-kinetics discover their proper DBMS solutions and otherwise information retention types faster. This suggested DSS is unique in that it uses MoSCoW to evaluate criterion weighting or deliberate, as well as assessment frameworks for quantifying overall levels of quasi criterion and ISO/IEC qualitative features to show the link between criterion based upon industry specialists' expertise. Companies that produce programs have difficulty integrating new innovations, such as Internet computing or information management platforms, into their operations. Because computer engineers and top programmers are usually specialists in their field, users need to consult or train other professionals. Therefore, computer development is an appropriate area for implementing judgment support technologies that can proactively help this prediction to choose the best technologies for their products. We offer a decision support system (DSS) to assist in the selection of the best appropriate information architecture. According to both example reports and specialists, this technique improves visibility into the choice method gives a deeper prioritized choice range than if customers have conducted their study individually, or saves the duration and expense of the judicial procedure.

Prospects and Challenges of Using Machine Learning for Academic Forecasting.

The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events. Many gaps in the traditional forecasting techniques have greatly been bridged by the use of artificial intelligence-based machine learning algorithms thereby aiding timely decision-making by education stakeholders. ML algorithms are deployed by educational institutions to predict students' learning behaviours and academic achievements, thereby giving them the opportunity to detect at-risk students early and then develop strategies to help them overcome their weaknesses. However, despite the benefits associated with the ML approach, there exist some limitations that could affect its correctness or deployment in forecasting academic events, e.g., proneness to errors, data acquisition, and time-consuming issues. Nonetheless, we suggest that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.

Analysis of Agriculture and Food Supply Chain through Blockchain and IoT with Light Weight Cluster Head.

By 2050, the world's population will have increased by 34%, to more than 9 billion people, needing a 70% increase in food production. Prepare more dishes with fewer ingredients. Therefore, the critical goal of manufacturers is to increase production while being ecologically benign. Supply chain systems that do not enable direct farmer-to-consumer connection and rising input costs influence data collection, security, and sharing. Constraints on data security, manipulation, and single-point failure are unfulfilled due to a lack of centralized IoT agricultural infrastructure. To address these issues, the article proposes a blockchain-based IoT model. This study also shows one-of-a-kind energy savings. The decentralization of data storage improves the supply chain's transparency and quality through blockchain technology, thus farmers can engage more efficiently. Blockchain technology improves supply chain traceability and security. This article provides a transparent, decentralized blockchain tracking solution and proposes an intelligent model protocol for several Internet of Things (IoT) devices that monitor crop development and the agricultural environment. A new approach has resolved the bulk of the supply chain difficulties. Smart contracts were utilized to organize all transactions in decentralized supply networks. The use of blockchain technology improves transaction quality, and customers may verify the legitimacy of an item's authenticity and legality by using the system. A total of 100 IoT nodes were distributed randomly to each 500 m2 cluster farm. The Internet of Things nodes were used to assess soil moisture, temperature, and crop disease. Network stability period and network life of the proposed method show 90.4% accuracy. The food supply chain will be more efficient and trustworthy with an intelligent model. The immutability of ledger technology and smart contract support further increases supply chain security, privacy, transparency, and trust among all stakeholders in the multi-party system. By 2050, the world's population will need a 70% increase in food production. The food supply chain will be more efficient and trustworthy with an intelligent model. This article provides a transparent, decentralized, and intelligent model protocol for several Internet of Things (IoT) devices.

Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.

Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.

Molecular docking and simulation studies of natural compounds of Vitex negundo L. against papain-like protease (PLpro) of SARS CoV-2 (coronavirus) to conquer the pandemic situation in the world.

The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is ß-coronavirus that is responsible for the pandemic coronavirus disease 2019 (COVID-19) all over the world. The rapid spread of the novel SARS CoV-2 worldwide is raising a significant global public health issue with nearly 61.86 million people infected and 1.4 million deaths. To date, no specific drugs are available for the treatment of COVID-19. The inhibition of proteases essential for the proteolytic treatment of viral polyproteins is a conventional therapeutic strategy for conquering viral infections. In the study, molecular docking approach was used to screen potential drug compounds among the phytochemicals of Vitex negundo L. against COVID-19 infection. Molecular docking analysis showed that oleanolic acid forms a stable complex and other phyto-compounds ursolic acid, 3ß-acetoxyolean-12-en-27-oic acid and isovitexin of V. negundo natural compounds form a less-stable complex. When compared with the control the synergistic interaction of these compounds shows inhibitory activity against papain-like protease (PLpro) of SARS CoV-2 (COVID-19). The molecular dynamics (MD) simulation (50 ns) were performed on the complexes of PLpro and the phyto-compounds viz. oleanolic acid, ursolic acid, 3ß-acetoxyolean-12-en-27-oic acid and isovitexin followed by the binding free energy calculations using MM-GBSA and these molecules have stable interactions with PLpro protein binding site. The MD simulation study provides more insight into the functional properties of the protein-ligand complex and suggests that these molecules can be considered as a potential drug molecule against COVID-19. In this pandemic situation, these herbal compounds provide a rich resource to produce new antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.

Potential inhibitors of SARS-CoV-2 (COVID 19) proteases PLpro and Mpro/ 3CLpro: molecular docking and simulation studies of three pertinent medicinal plant natural components.

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) - coronavirus disease 2019 (COVID-19) has raised a severe global public health issue and creates a pandemic situation. The present work aims to study the molecular -docking and dynamic of three pertinent medicinal plants i.e. Eurycoma harmandiana, Sophora flavescens and Andrographis paniculata phyto-compounds against SARS-COV-2 papain-like protease (PLpro) and main protease (Mpro)/3-chymotrypsin-like protease (3CLpro). The interaction of protein targets and ligands was performed through AutoDock-Vina visualized using PyMOL and BIOVIA-Discovery Studio 2020. Molecular docking with canthin-6-one 9-O-beta-glucopyranoside showed highest binding affinity and less binding energy with both PLpro and Mpro/3CLpro proteases and was subjected to molecular dynamic (MD) simulations for a period of 100ns. Stability of the protein-ligand complexes was evaluated by different analyses. The binding free energy calculated using MM-PBSA and the results showed that the molecule must have stable interactions with the protein binding site. ADMET analysis of the compounds suggested that it is having drug-like properties like high gastrointestinal (GI) absorption, no blood-brain barrier permeability and high lipophilicity. The outcome revealed that canthin-6-one 9-O-beta-glucopyranoside can be used as a potential natural drug against COVID-19 protease.