Food safety and shelf life are critical factors for human health and are greatly impacted by microbial spoilage. Despite the widespread use of synthetic preservatives, interest in natural alternatives has grown due to their possible health risks. The plant bioactives as natural alternatives are being explored for extending shelf life of food either via direct addition or in terms of edible coating and packaging films. The Ficus spp., having a great ethnobotanical importance, have been used for a long time in traditional medicine. Its rich phytochemical profile exhibits a strong antimicrobial and antifungal property to combat various food-spoilage microorganisms, such as Escherichia coli, Staphylococcus aureus, Pseudomonas spp., Candida albicans, Aspergillus flavus, and Penicillium expansum. The latex based edible coatings from Ficus spp. have shown real-food utility by extending the shelf life and enhancing the quality of dried fruits. The packaging film incorporated with Ficus carica leaves extract preserved the quality of apple slices. The addition of its aqueous leaf extract into the milk extends shelf life of pasteurized buffalo milk from 5 to 16 days without altering its properties. Despite the potential antimicrobial effects many of the Ficus plants such as Ficus virens, Ficus palmata, Ficus auriculata, and Ficus benghalensis still have not explored in food packaging or food preservation. These challenges can be addressed via various interdisciplinary approaches such as metabolomics, pharmacology, nanotechnology, and computational biology. Additionally, compound stability, standardization, and widespread utilization are some of the research gaps that need to be discussed.

Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential genes in pathogens and non-pathogenic genes. Since, identification of therapeutic targets and understanding of their structure, function, and role in pathogenesis is important in development of drug design. Therefore, this review will provide a comprehensive look at the subtractive genomics technique which was applied to various pathogens, often highlighting the effectiveness of the methodology in drug target discovery and novel therapeutics development. Tools and software such as BLAST, Roary, and AutoDock Vina are widely utilized in this methodology for various aspects such as, genome comparison, essential gene identification, clustering, subcellular localization, pathway analysis, molecular docking etc. Diseases such as tuberculosis, botulism, staphylococcal infections, ventilator-associated pneumonia, secondary meningitis, gonorrhoea, septicaemia, etc., are among the infectious diseases targeted using subtractive genomics. Comparison of basic principles, tools, and advancements use these subtractive genomics studies, will provide insight into the adaptable nature of this technique and the diversity of pathogens, which have benefited with this methodology into providing successful results. The main focus is on the genome sequencing advancements, annotation and validation through in-silico techniques, to find effective drug targets while, decreasing the possibility of toxicity in the host. We have also discussed the possibility of taking a multi-omics approach and incorporating AI and machine learning to expand on the current data and finding effective therapeutics for helping globally on health challenges.

This book explores computational approaches revolutionizing pulmonary disease studies through AI, machine learning, and multi-omics. Chapters cover AI-driven respiratory data analysis, single-cell approaches, and integrative omics for lung disease outcomes. Key areas include tuberculosis, severe asthma, idiopathic pulmonary fibrosis, and non-small cell lung cancer, integrating genomic, proteomic, and epigenomic data. Novel methods like Sequential Attribute Designator for feature selection and deep learning models for lung disease diagnosis via chest X-rays are introduced. The book addresses lung microbiome, metagenomic tools, and environmental exposure effects. By connecting translational bioinformatics with clinical applications, it highlights precision pulmonology's future through advanced omics and machine learning, advancing disease diagnosis, treatment, and research.

BACKGROUND:Tuberculosis remains a substantial health threat globally, despite decades having elapsed since the identification of its causative agent, Mycobacterium tuberculosis. Approximately 35% of the global population is sub-clinically infected, leading as one of the primary causes of human mortality. The increased prevalence of drug-resistant strains of Mtb necessitates identification of important drug targets. Therefore, the aim of the study was to comparatively analyze the protein-protein interactions between the host and the pathogen (Mycobacterium tuberculosis) to uncover the conserved molecular mechanisms of infection, providing insight into strain-specific variations. METHODS:One of the major problems is the diverse spectrum of diseases caused by different Mtb. To date, most research has their attention on a specific pathogenic strain. Therefore, to screen common and effective drug targets of different strains, we compared the protein-protein interactions of four virulent strains (H37Rv, CDC1551, CAS/NITR204, and Erdman) and one a virulent strain (H37Ra) of Mtb with its human host. Here, the interolog method was adopted to identify the biomolecular-interactions between Mtb and its human host. RESULTS:As a result, an interaction network has been developed, and the target has been screened through multiple parameters, such as the highest interacting partners, virulent factors, subcellular localization, and predicted protein interactions. CONCLUSIONS:This study substantially resulted in the identification of potential drug targets, ATP synthase subunit alpha and gamma, and chaperone proteins DNAK and HTPG.

Bacteria are central to wastewater treatment processes, including activated sludge treatment, anaerobic digestion, and biofiltration, which remove organic matter, nutrients, and pathogens from wastewater. By utilizing the metabolic capabilities of bacteria, sustainable development can be achieved through waste treatment by degrading, detoxifying, and transforming various waste streams into valuable resources. Bacteria play a crucial role in bioremediation, wastewater treatment, and bioconversion processes, and offer sustainable solutions for managing organic waste, pollutants, and contaminants. By integrating microbial processes into waste management and environmental remediation practices, communities can achieve sustainable and resilient solutions for managing waste streams, protecting natural ecosystems, and promoting human well-being.

As carbon-based nanomaterials have such remarkable physical, chemical, and electrical capabilities, they have become a major focus of materials science study. A thorough examination of several carbon nanomaterial varieties, such as carbon nanotubes, graphene, fullerenes, and carbon nanodiamonds, is given in this review work. These materials all have distinctive qualities that qualify them for particular uses. This work starts by examining the synthesis processes of these nanomaterials, outlining the ways by which they are made and the variables affecting their ultimate characteristics. The specific features of each kind of carbon nanomaterial will then be briefly discussed in this study, along with their size, structure, and special physical and chemical properties. These materials have a wide range of possible uses in several fields. They are employed in the electronics industry to fabricate sensors, high-speed transistors, and other devices. Their high surface area and electrical conductivity make them useful in energy storage devices like supercapacitors and batteries. They are applied to environmental remediation and water purification in environmental science. They are employed in biomedicine for biosensing, bioimaging, and medication delivery. Notwithstanding the encouraging uses, the large-scale synthesis and functionalization of carbon nanomaterials present several difficulties. This review discusses the importance of carbon nanomaterials by studying their multifaceted properties and potential applications in industries. The novelty of this work lies in its detailed examination of the degradation and toxicity of these materials, which is essential for their safe integration into various technological and biomedical applications. By thoroughly analysing recent experimental results, this review aims to bridge the gap between fundamental research and practical applications.

RNA-Binding Proteins (RNAs) that work in gene expression, RNA processing, and translation. For this, the identification of RNA-binding regions in proteins is an important step to elucidate the mechanisms associated with these processes. In summary, this study provides a novel hybrid prediction for identifying RBPs-binding sites by combining deep learning with the random forest algorithm. Convolutional neural network (CNN) to abstract protein sequence features and graph convolutional network for structural information. A trained random forest classifier using features extracted from a vector of 300 protein residues for training examples that have known RNA-binding sites is then applied to the sample. For each of the residue in protein sequence, a probability score is computed representing greater likelihood to be an RNA-binding site. We evaluated its performance on two benchmark datasets and demonstrated its competitive accuracy, precision, recall gains over a state-of-the-art methods on commonly used benchmarks. Moreover, we conducted a feature importance analysis to discover important features in predicting RNA-binding sites. Our approach not only allows the systematic integration of tehRNA motifmap in the identification of RNA-binding sites, accelerating the discovery considerably but it also may help to obtain new biological insights quickly on a variety of life sciences data-processing problems.

Pantothenate synthetase protein plays a pivotal role in the biosynthesis of coenzyme A (CoA), which is a crucial molecule involved in a number of cellular processes including the metabolism of fatty acid, energy production, and the synthesis of various biomolecules, which is necessary for the survival of Mycobacterium tuberculosis (Mtb). Therefore, inhibiting this protein could disrupt CoA synthesis, leading to the impairment of vital metabolic processes within the bacterium, ultimately inhibiting its growth and survival. This study employed molecular docking, structure-based virtual screening, and molecular dynamics (MD) simulation to identify promising phytochemical compounds targeting pantothenate synthetase for tuberculosis (TB) treatment. Among 239 compounds, the top three (rutin, sesamin, and catechin gallate) were selected, with binding energy values ranging from −11 to −10.3 kcal/mol, and the selected complexes showed RMSD (<3 Å) for 100 ns MD simulation time. Furthermore, molecular mechanics generalized Born surface area (MM/GBSA) binding free energy calculations affirmed the stability of these three selected phytochemicals with binding energy ranges from −82.24 ± 9.35 to −66.83 ± 4.5 kcal/mol. Hence, these identified natural plant-derived compounds as potential inhibitors of pantothenate synthetase could be used to inhibit TB infection in humans.

Nowadays, the physiopathological and molecular mechanisms of multiple diseases have been identified, thus helping scientists to provide a clear answer, especially to those ambiguities related to chronic illnesses. This has been accomplished in part through the contribution of a key discipline known as bioinformatics. In this study, the bioinformatics approach was applied on four compounds identified in Centaurea tougourensis, using two axes of research: an in silico study to predict the molecular characteristics, medicinal chemistry attributes as well as the possible cardiotoxicity and adverse liability profile of these compounds. In this context, four compounds were selected and named, respectively, 2,5-monoformal-l-rhamnitol (compound 1), cholest-7-en-3.beta.,5.alpha.-diol-6.alpha.-benzoate (compound 2), 7,8-epoxylanostan-11-ol, 3-acetoxy- (compound 3), and 1H-pyrrole-2,5-dione, 3-ethyl-4-methyl- (compound 4). The second part looked into molecular docking, which objective was to evaluate the possible binding affinity between these compounds and the serotonin 5-hydroxytryptamine 2A (5-HT2A) receptor. Results indicated that compounds 1 and 4 were respecting Pfizer and giant Glaxo-SmithKline rules, while compounds 2 and 3 exhibited an optimal medicinal chemistry evolution 18 score. The structural and molecular features of almost all tested compounds could be considered optimal, indicating that these phyto-compounds may possess drug-likeness capacity. However, only compounds 1 and 4 could be considered non-cardiotoxic, but with a level of confidence more pronounced for compound 1 (80%). In addition, these four biocompounds could preferentially interact with G protein-coupled receptor, ion channel, transporters, and nuclear receptors. However, the heat map was less pronounced for compound 2. Data also indicated that these four compounds could possibly interact with serotonin 5-HT2A receptor, but in an antagonistic way. This research proved once again that plants could be crucial precursors of pharmaceutical substances, which could be helpful to enrich the international pharmacopoeia.

Pancreatic cancer is a prevalent lethal gastrointestinal cancer that generally does not show any symptoms until it reaches advanced stages, resulting in a high mortality rate. People at high risk, such as those with a family history or chronic pancreatitis, do not have a universally accepted screening protocol. Chemotherapy and radiotherapy demonstrate limited effectiveness in the management of pancreatic cancer, emphasizing the urgent need for innovative therapeutic strategies. Recent studies indicated that the complex interaction among pancreatic cancer cells within the dynamic microenvironment, comprising the extracellular matrix, cancer-associated cells, and diverse immune cells, intricately regulates the biological characteristics of the disease. Additionally, mounting evidence suggests that EVs play a crucial role as mediators in intercellular communication by the transportation of different biomolecules, such as miRNA, proteins, DNA, mRNA, and lipids, between heterogeneous cell subpopulations. This communication mediated by EVs significantly impacts multiple aspects of pancreatic cancer pathogenesis, including proliferation, angiogenesis, metastasis, and resistance to therapy. In this review, we delve into the pivotal role of EV-associated miRNAs in the progression, metastasis, and development of drug resistance in pancreatic cancer as well as their therapeutic potential as biomarkers and drug-delivery mechanisms for the management of pancreatic cancer.

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.

The utilization of microbial laccase for the biological delignification of biomass is regarded as an environmentally sustainable procedure. The present study aimed to optimize the process parameters in order to achieve improved laccase production. Mustard oil cake was utilized as a solid substrate, and Aspergillus nidulans was employed as the fungal strain. The present study investigated the impact of different physical and chemical factors on laccase production, specifically focusing on moisture content, incubation time, nutrient pH, incubation temperature, salt concentration, and additional carbon and nitrogen sources. Additionally, response surface methodology was employed to optimize both the media and process parameters. The highest observed laccase activity was around 6.99 U/ml. The best conditions for the manufacture of laccases were determined through observation. It was found that a moisture content of 100% (w/v), an incubation temperature of 28°C, a pH range of 5-6, and the addition of 6% MgSO4 (w/v) and ammonium chloride as supplementary salts in the production medium resulted in the highest production of laccases. The production medium was incubated for a duration of 96 hours. URN:NBN:sciencein.jist.2024.v12.777

Segmentation of brain tumors is a crucial step in the field of medical imaging, essential for the early identification and treatment of brain tumors. This process significantly influences patient care by facilitating timely intervention. Brain tumors originate from abnormal cell growth and proliferation that diverge from normal cell behavior, resulting in tumor development. Segmentation, the differentiating between normal brain tissue and tumor regions, is critical for precise diagnosis and effective treatment strategy. Historically, the task of segmentation has been manually performed by healthcare experts. This method is labor-intensive and difficult due to the intricate and diverse brain structure. Deep neural networks (DNNs), renowned for their prowess in image classification tasks, have been employed to overcome these obstacles. Specifically, the GTS (graph-based transductive segmentation) algorithm has been introduced to optimize magnetic resonance imaging (MRI) image segmentation. This advanced method enhances the efficiency of finding the closest optimal solutions by improving convergence, thereby significantly reducing the time required for solution identification. The GTS technique stands out for its remarkable accuracy rate of 97%, showcasing some substantial improvement over previous segmentation methods. This leap in accuracy expedites the diagnostic process and increases the likelihood of successful patient outcomes by enabling earlier and more precise tumor detection.

Strigolactones (SLs) are an important class of new-generation plant hormones that play a multitude role in various aspects of plant growth, including shoot branching, root architecture, regulation of plant development, signaling, and establishment of mycorrhizal relationships. Recent research has revealed that SLs and their analogs have multifactorial implications, including potential benefits in plant growth, responses, and developmental processes of plants by improving biotic and abiotic stress tolerance. This class of plant hormones having role in human health. Moreover, this bio-compound has a significant influence on the rhizosphere microbial population and regulates colonization and hyphal branching of arbuscular mycorrhizal fungi. SLs and their analogs have different functions, with benefits in both plant production and human health, and can affect several aspects such as crop yield and quality, disease resistance, and environmental sustainability. The current review provides a comprehensive overview of the biological activity of SLs and discusses the the involvement of strigolactone signaling in plant growth responses and biosynthesis genes in plant architecture, which contribute to traits or serve as key factors for integrated hormonal regulation, developmental control, and environmental factors that affect yield improvement in plants. Additionally, the mechanisms of SL-induced modifications in the microbial community are elaborated, with a specific focus on crosstalk with other signaling systems. This review encompasses the current trends in SL research and extends to the impact of SLs on human health, plant development, and microbial populations.

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.