Navrachana University

This article explores the acoustic properties of a bio-composite material derived from coconut plant waste and potato starch material. The material consists of coir fiber as reinforcement and starch as a binding agent, produced using the hot press technique. To improve structural durability, a glass fiber layer was added to one side of the test sample. The study used the impedance tube method to measure the sound absorption coefficient of the newly developed material. It investigated the effects of material composition, thickness, and structural arrangement relative to the sound source on sound absorption. The results reveal that these factors significantly influence sound absorption properties. The fibrous structure achieved the highest noise reduction coefficient when facing the sound source with maximum thickness. The maximum sound absorption coefficient, ranging from 0.35 to 0.4, was observed in the higher frequency range, while the lower frequency range showed more variability in results. Starch-based composites are known for their environment friendly nature and find its applications in various sectors including packaging, consumer goods and automotive too.

Agrochemicals (AGs) are known for their ability to have a negative impact on the health of non-target species, despite the fact that they are meant to protect agricultural plants from harmful pests. Catla catla (Hamilton, 1822) gill cells (ICG) were exposed to four AGs: insecticide (Imidacloprid (IMI)), fungicide (Curzate (CZ)), herbicide (pyrazosulfuron ethyl (PE)), and fertilizer micronutrients (MN) with sublethal concentrations 1/20th, 1/10th, and 1/5th of IC50, described here as low dose (LD), medium dose (MD), and high dose (HD), respectively. A significant dose-dependent increase in the nuclear abnormalities such as micronuclei formation, bi-nucleated, and lobbed nucleated cells was observed in ICG cells treated with AGs. Of all the AGs, maximum alterations were observed with the HD of IMI followed by CZ, PE, and MN. Concurrently, the genotoxicity was determined by performing comet assays with high dose of all AGs. The gene expression of dnmt and cyp p450 were also studied through q-PCR in ICG cells. The significant increase in expression as well as alteration in cyp p450 and dnmt sequence was reported in ICG cells exposed to HD of IMI. This suggests that IMI has a genotoxic effect and may lead to epigenetic alterations.

This paper reflects the behavior of KHg(CN) 2 (SCN) in an extreme temperature and pressure environment showing positive thermal expansion above room temperature and positive compressibility.

Cancer, a prevalent disease worldwide, is often caused by genetic and epigenetic changes. Functional foods, especially probiotics and prebiotics, offer promising therapeutic potential for cancer prevention and treatment by modulating the gut microbiota and enhancing host immune responses. This review examines the mechanistic perspectives of functional foods, focusing on their role in colon cancer and overall human health. Probiotics, such as Lactobacillus and Bifidobacterium, enhance gut health by colonizing the intestinal tract, stabilizing microflora, and promoting immune homeostasis. Prebiotics, including inulin and fructo-oligosaccharides, support beneficial gut bacteria and improve mineral absorption, thus contributing to cancer prevention. The synergistic effects of probiotics, prebiotics, and nutraceuticals, which are rich in bioactive compounds such as polyphenols and terpenoids, are discussed in the context of cancer therapy. In addition, the role of postbiotics, such as short-chain fatty acids and exopolysaccharides, is investigated in promoting apoptosis and reducing tumour growth. The review highlights the need for continued research to fully elucidate the molecular mechanisms and optimize the therapeutic use of functional foods in cancer therapy care, emphasizing their potential to improve treatment outcomes and the patient’s quality of life.

Effective solid waste classification is crucial for efficient waste management and environmental sustainability. Addressing this challenge is essential for improving urban quality of life. This study develops an accurate and efficient model for automatically classifying various types of solid waste. We leverage a large, annotated dataset of waste objects from diverse sources and apply state-of-theart deep learning techniques, including advanced architectures such as Xception, DenseNet, and EfficientNet. While previous studies have used computer vision for waste classification, our approach demonstrates the significant effectiveness of deep learning methods in solving this problem. By improving classification accuracy, our model aims to optimize waste management practices, contributing to a cleaner and more sustainable urban environment.

The use of synthetic insecticides has been crucial in the management of insect pests however the extensive use of insecticides can result in the development of resistance. Callosobruchus chinensis is a highly destructive pest of stored grains, it’s a major feeder and infests a range of stored grains that are vital to both global food security and human nutrition. We extensively investigated gene expression changes of adults in response to deltamethrin to decipher the mechanism behind the insecticide resistance. The analysis of gene expression revealed 25,343 unigenes with a mean length of 1,435 bp. All the expressed genes were identified, and analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Exposure to deltamethrin (4.6 ppm) causes 320 differentially expressed genes (DEGs), of which 280 down-regulated and 50 up-regulated. The transcriptome analysis revealed that DEGs were found to be enriched in pathways related to xenobiotics metabolism, signal transduction, cellular processes, organismal systems and information processing. The quantitative real-time PCR was used to validate the DEGs encoding metabolic detoxification. To the best of our knowledge, these results offer the first toxicity mechanisms enabling a more comprehensive comprehension of the action and detoxification of deltamethrin in C. chinensis.

Cancer Stem Cells (CSCs) play an important role in the development, resistance, and recurrence of many malignancies. These subpopulations of tumor cells have the potential to self-renew, differentiate, and resist conventional therapy, highlighting their importance in cancer etiology. This review explores the regulatory mechanisms of CSCs in breast, cervical, and lung cancers, highlighting their plasticity, self-renewal, and differentiation capabilities. CD44+/CD24− cells are a known marker for breast CSCs. Markers like as CD133 and ALDH have been discovered in cervical cancer CSCs. Similarly, in lung cancer, CSCs identified by CD44, CD133, and ALDH are linked to aggressive tumor behavior and poor therapy results. The commonalities between these tumors highlight the general necessity of targeting CSCs in treatment efforts. However, the intricacies of CSC activity, such as their interaction with the tumor microenvironment and particular signaling pathways differ between cancer types, demanding specialized methods. Wnt/β-catenin, Notch, and Hedgehog pathways are one of the essential signaling pathways, targeting them, may show ameliorative effects on breast, lung and cervical carcinomas and their respective CSCs. Pre-clinical data suggests targeting specific signaling pathways can eliminate CSCs, but ongoing clinical trials are on utilizing signaling pathway inhibitors in patients. In recent studies it has been reported that CAR T based targeting of specific markers may be used as combination therapy. Ongoing research related to nanobiotechnology can also play a significant role in diagnosis and treatment purpose targeting CSCs, as nanomaterials can be used for precise targeting and identification of CSCs. Further research into the targeting of signaling pathways and its precursors could prove to be right step into directing therapies towards CSCs for cancer therapy.

Epidemics are biological processes that appear suddenly as shocks. Societies attempt to negotiate such shocks through the interplay of classes, ethnicities, and institutions. In the process, does it create newer fault lines or consolidate the pre-existing ones existing within the socio-cultural and politico-economic structures? The article seeks to analyse this question. The anxiety generated by the COVID-19 pandemic and the abrupt change in lifestyles have agitated the human mind. This anxiousness ‘within’ is often found to be externalised socially, unfolding a search for the ‘other’ who can be associated with the virus and thereby stigmatised in the name of the accompanying disease. The ‘other’ thus becomes the social allegory of the biological moment. But, why do we need the ‘other’? This article deals around those lines

The construction of mechanically stabilized earth walls (MSEW) has become increasingly popular, especially in road and railway infrastructure projects. MSEW provides several advantages over typical retaining walls, including ease of construction, low cost, and low environmental impact. This study has been divided into two parts. The first part focuses on the parametric analysis of a Geogrid Reinforced Earth Wall (GREW), evaluating the influence of various parameters such as height, type of reinforcement, tensile strength of reinforcement, type of backfill, type of foundation soil, and water table fluctuation on the stability of GREW. The second part assesses the use of a rubber tire and sand mixture as a potential backfill material for GREW. This material was chosen because it has been found to outperform other typical materials such as crushed rock, sand, or gravel. The stability of GREW concerning parametric studies and using a sand and shredded rubber tire mix as backfill is analyzed for internal and external stability using the GEO-5 software. Overall, the study provides distinctive insights into the design and analysis of GREW, which might be useful for civil engineers and researchers working in retaining wall applications. The study's findings can assist in improving the safety and durability of retaining walls in various civil engineering applications.

Legumes are one of the most important economic crops cultivated for their nutritional value which includes proteins, dietary fibers, phytosterols, polyphenols, and micronutrients. However, they are challenged by many factors like water scarcity, high salinity, metal toxicity, and biotic factors like microorganisms and parasitic nematodes. To protect themselves against these potential threats, plants produce many signaling molecules like peptide elicitors called plant elicitor peptides. Plant elicitor peptides (PEPs) are a class of elicitors which elicits defense responses by activating defense pathways in terms of elevated expression of defense-related genes, hormone production, and induction of secondary messenger synthesis. They belong to a class of endogenous elicitors which lead to enhanced immunity against various abiotic and biotic stresses. These peptides are perceived by membrane receptors in the plant cells, which bind to the peptide ligands to initiate the signaling cascades. The exploration of PEP’s represents a good alternative with potential in crop protection. This chapter deals with the peptide elicitors used to combat abiotic and biotic stress in leguminous plants.

Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol‐polycaprolactone (PEG‐PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS‐loaded PEG‐PCL nanoparticles (termed as DAS@PEG‐PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug‐loaded nanoparticles' apoptotic and anti‐proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG‐PCL NPs resulted in a dose‐dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG‐PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment.

In the current study, a series of unconsolidated undrained triaxial tests, consolidated undrained triaxial tests, bender element tests, and cyclic triaxial tests were performed on micaceous sand specimens at different mica contents (0 to 30%). All the specimens were prepared at 95% maximum dry density of their respective mica-sand combinations to simulate the soil mass conditions in railway/road embankments. The experimental results showed a large reduction in the angle of internal friction as mica content increased from 0 to 30% in unconsolidated undrained tests (total stress). Consolidated undrained triaxial testing reported the decrease in shear strength with the increase in mica content (effective stress). A large decrease in shear modulus was observed with the increase in mica content from 0 to 30% in cyclic triaxial testing. The damping ratio increased with the increase in mica content. The small strain shear modulus obtained from bender element tests showed a significant decrease with the increase in mica content.

Marine macroalgae are emerging candidates for mitigating life style disorders. The vast history of their utilization from South Asian countries provides strong evidence for their selective suitability in routine diet. The ever increasing awareness for alternative and herbal medicine have led to the explorations into these unique marine resources that are enriched with medically valuable bioactives. The types of bioactive pharmaceuticals include peptides as well as unique sulfated carbohydrates in addition to the secondary metabolites such as polyphenols which make these biomass highly valuable commodity compared to the terrestrial plants. The chapter summarises introductory remarks on the ecological role of marine macroalgae followed by their key therapeutic roles of various bioactive metabolites as mentioned above that include metabolic disorders, microbial infections and the recently unraveled role in modulation of gut-microbiome. The chapter also discusses the key limitations in the development of marine based therapeutants such as the possible toxicity, cultivation practices and supply-chain and environmental influences. The chapter is concluded discussing the future prospects in the area.

Cancer remains a significant cause of mortality in the world, with increasing prevalence worldwide. There are numerous treatments ranging from surgery to chemotherapy and radiotherapy, but since cancer is a heterogeneous disease, only few patients possibly respond to treatments. However, it opens a huge space for the advent of targeted therapies such as hormone therapy, immunotherapy, and target-specific drugs. Hormonal therapy using hormone agonists/antagonists or hormone receptor inhibitors—called the next-generation hormonal agents—hits distinct hormonal pathways that are involved in breast, prostate and ovarian cancer. Preliminary results show that through combination of drugs, it is possible that the synergistic effects may actually lead to better survival than with the use of single drugs. With manageable adverse effects, hormonal therapy offers much hope for treatment of this rather challenging malignancy of the hormone-sensitive cancers, especially in combination with other treatments.

Imbalanced class distributions pose a prevalent challenge in numerous classification problems, requiring effective strategies for learning from such skewed data. Traditional machine learning algorithms often struggle with imbalanced datasets, as they tend to bias their classification functions toward the majority class, resulting in suboptimal performance for minority classes. In our research, we propose a novel approach to address this challenge specifically tailored for Support Vector Machines (SVM), a well-established family of learning algorithms. Our method leverages a kernel trick to enhance the SVM’s classification capabilities on imbalanced datasets named KTI. It aims to streamline the classification process by incorporating adaptive data transformations within the algorithm itself, offering a more efficient and integrated solution for handling imbalanced data. Experimental evaluations conducted on diverse real-world datasets demonstrate the superior performance of our proposed strategy compared to existing methods, showcasing its potential for practical applications in classification tasks with skewed class distributions.

Poor dietary habits and a lack of understanding are contributing to the rapid global increase in the number of diabetic people. Therefore, a framework that can accurately forecast a large number of patients based on clinical details is needed. Artificial intelligence (AI) is a rapidly evolving field, and its implementations to diabetes, a worldwide pandemic, have the potential to revolutionize the strategy of diagnosing and forecasting this chronic condition. Algorithms based on artificial intelligence fundamentals have been developed to support predictive models for the risk of developing diabetes or its complications. In this review, we will discuss AI-based diabetes prediction. Thus, AI-based new-onset diabetes prediction has not beaten the statistically based risk stratification models, in traditional risk stratification models. Despite this, it is anticipated that in the near future, a vast quantity of well-organized data and an abundance of processing power will optimize AI's predictive capabilities, greatly enhancing the accuracy of diabetic illness prediction models.

A number of new compounds have been synthesized by the authors containing fluorinated thiazolidin-4-one ring. With the aim to assess the anti-cancer potential of all the synthesized derivatives,theywere computationally tested against 1T46 C-Kit Tyrosine Kinase protein. Almost all of the evaluated derivatives showed decent affinity towards the protein, with favourable binding poses through hydrogen bonding, halogen binding and pi-sigma bonding. The amino acid lysine at position 623 in the protein chain exhibited hydrogen bond formation with each compound, along with other amino acids. Furthermore, the in silico ADME predictions suggest that the majority of the synthesized compounds exhibit favourable drug-like characteristics, with low potential for adverse effects and toxicity. The molecules possessing oxygen-containing functionalities such as –NO2, -OCF3, -OCF2CF2H and –OH have been shown to be able to cross the Human Intestinal lining. The fluorine-containing moieties such as difluoro, trifluoro, -CF3, chloro-fluoro, and difluorobenzylamino were predicted in order to cross BBB (Blood-Brain-Barrier). Current study has revealed that the synthesized compounds show promising anticancer potential.