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Cyclooxygenase-2 (COX-2) is a key enzyme involved in overexpression in several human cancerous diseases including breast cancer. By performing efficient virtual screening in a series of active molecules or compounds from the Maybridge, NCI (National Cancer Institute), and Enamine databases, potential identification of COX-2 inhibitors could lead to new prognostic strategies in the treatment of breast cancer. Based on a 50% structural similitude, compounds were chosen as the inductive model of COX-2 inhibitions from these databases. Selected compounds were filtered and tested with Lipinski’s rule of five followed by absorption, distribution, metabolism, and excretion (ADME) properties. Subsequently, molecular docking was performed to achieve accuracy in screening and also to find an interactive mechanism between hit compounds with their respective binding sites. Simultaneously, molecular simulations of top-scored compounds were selected and coded such as Maybridge_55417, NCI_30552, and Enamine_62410. Chosen compounds were analyzed and interpreted with COX-2 affinity. Results endorsed that hydrophobic affinity and optimum hydrogen bonds were the forces driven in the interactive mechanism of in silico hits compounds with COX-2 and can be used as efficient alternative therapeutic agents targeting deleterious breast cancer. With these in silico findings, compounds identified may prevent the action of the COX-2 enzyme and thereby diminish the incidence of breast cancer.

Significant changes in society were emphasized as being required to achieve Sustainable Development Goals, a need which was further intensified with the emergence of the pandemic. The prospective society should be directed towards sustainable development, a process in which technology plays a crucial role. The proposed study discusses the technological potential for attaining the Sustainable Development Goals via disruptive technologies. This study further analyzes the outcome of disruptive technologies from the aspects of product development, health care transformation, a pandemic case study, nature-inclusive business models, smart cities and villages. These outcomes are mapped as a direct influence on Sustainable Development Goals 3, 8, 9 and 11. Various disruptive technologies and the ways in which the Sustainable Development Goals are influenced are elaborated. The investigation into the potential of disruptive technologies highlighted that Industry 5.0 and Society 5.0 are the most supportive development to underpin the efforts to achieve the Sustainable Development Goals. The study proposes the scenario where both Industry 5.0 and Society 5.0 are integrated to form smart cities and villages where the prospects of achieving Sustainable Development Goals are more favorable due to the integrated framework and Sustainable Development Goals’ interactions. Furthermore, the study proposes an integrated framework for including new age technologies to establish the concepts of Industry 5.0 and Society 5.0 integrated into smart cities and villages. The corresponding influence on the Sustainable Development Goals are also mapped. A SWOT analysis is performed to assess the proposed integrated approach to achieve Sustainable Development Goals. Ultimately, this study can assist the industrialist, policy makers and researchers in envisioning Sustainable Development Goals from technological perspectives.

Angiogenesis is crucial to the development of cancer because it allows the transport of oxygen, nutrients, and growth factors as well as the spread of tumors to distant organs. Inhibitors of angiogenesis prevent the formation of blood vessels that allow tumor cells to shrink, rather than promote tumor growth. Chitosan acts as a carrier for many drugs, since the compound has various properties such as biodegradable, less toxicity, more stable, simple, easy to prepare, and biocompatible. The aim of the current study was to evaluate the efficacy of chitosan nanoparticles encapsulated with troxerutin (Chi-Trox NPs) against angiogenesis and cancer in ova chick chorioallantoic membrane (CAM) model. Chi-Trox NPs were synthesized using a nanoprecipitation method and were characterized by various analyses. 24 hours’ fertilized eggs (6 eggs/group) were treated with native Trox and Chi-Trox NPs for 5 days. The antiangiogenic activity was evaluated by morphometric, histopathological, immunohistochemical (CD104 and vimentin), and mRNA expression of MMP and FGF2 using RT-PCR. The anticancer activity was evaluated by histopathological, immunohistochmical (CD44), and mRNA expression of FGF2 and MMP. The synthesized chitosan NPs were successfully encapsulated with troxerutin, and the loading efficiency of chitosan NPs was found to be 86.4±0.12% and 13.2±0.16% respectively. Morphometric analysis of Chi-Trox NPs showed a considerable decrease in the number of blood vessels compared with control and native Trox. The histopathological observation of CAM confirmed that Chi-Trox NPs induce a significant reduction in inflammatory cells and the thickness of blood capillaries compared to control and native Trox. The immunohistochemical evaluation of CAM revealed Chi-Trox decreased CD104, vimentin and CD44 protein levels were compared with control and native Trox. Furthermore, the mRNA expression levels of FGF2 and MMP were significantly downregulated compared to their native forms. From the obtained results, Chi-Trox NPs possess significant inhibition of angiogenesis and can be used as therapeutic agents for cancer in the future.

BACKGROUND: Body mass index (BMI) may not accurately predict cardiometabolic risk due to confounders like age, gender, relatively high lean mass, and the \"thin-fat phenotype\" prevalent in south Asian populations. Fat-to-lean mass ratio (FTLM), which assesses the balance between fat and lean body mass, may provide a more complete assessment of cardiometabolic health. MATERIALS AND METHODS: This cross-sectional analytical study investigated the relationship between FTLM ratio, BMI, and heart rate variability (HRV) in apparently healthy male adults. 88 participants recruited through convenience sampling underwent anthropometric assessments, bioimpedance body composition analysis, and HRV testing. Pearson's or Spearman's correlation and linear regression analyses were performed where appropriate to assess the relationship between FTLM ratio, BMI, and HRV. RESULTS: Both BMI and FTLM showed significant positive correlation with normalized LF power and LF-HF ratio and a negative correlation with normalized HF power, RMSSD, and pNN50. However, FTLM ratio showed a stronger association with HRV parameters than BMI and could explain a greater percentage of the variability in LF-HF ratio (32% compared to 18.4%, P < 0.001). CONCLUSION: Assessment of both fat and lean mass, expressed as a ratio, is a better index of quantifying adiposity and predicting the influence of altered body composition on cardiometabolic health.

Wind energy is an abundant renewable energy resource that is extensively used worldwide in recent years. The present work proposes a new Multi-Objective Optimization (MOO) based genetic algorithm (GA) model for a wind energy system. The proposed algorithm consists of non-dominated sorting which focuses to maximize the power extraction of the wind turbine and the lifetime of the battery. Also, the performance characteristics of the wind turbine and battery energy storage system (BESS) are analyzed specifically torque, current, voltage, state of charge (SOC), and internal resistance. The complete analysis is carried out in the MATLAB/Simulink platform. The simulated results are compared with existing optimization techniques such as single-objective, multi-objective, and non-dominating sorting GA II (Genetic Algorithm-II). From the observed results, the NSGA III optimization algorithm offers superior performance notably higher turbine power output with higher torque rate, lower speed variation, and lesser degradation rate of the battery. This result attested to the fact that the proposed optimization tool can extract a higher rate of power from a self-excited induction generator (SEIG) when compared with a conventional optimization tool.

Malakoplakia is a rare inflammatory disorder primarily affecting the urinary tract, characterized by granulomatous reactions and Michaelis-Gutmann bodies. It typically presents with nonspecific urinary symptoms, mimicking malignancies radiologically and histologically, thereby complicating diagnosis. This case describes an uncommon presentation in an 18-year-old male with hematuria and dysuria, initially misdiagnosed as a urothelial neoplasm. Accurate diagnosis required extensive histopathological analysis, revealing classic malakoplakia features. Management included endoscopic resection and ureteral stenting. This emphasizes the diagnostic challenges posed by malakoplakia, the importance of careful multidisciplinary evaluation, detailed histopathology for correct identification, and the need for early intervention and follow-up to avoid renal complications.

Microbial fuel cells (MFCs) are a cost-effective and environmentally friendly alternative energy method. MFC technology has gained much interest in recent decades owing to its effectiveness in remediating wastewater and generating bioelectricity. The microbial fuel cell generates energy mainlybecause of oxidation-reduction reactions. In this reaction, electrons were transferred between two reactants. Bioinformatics is expanding across a wide range of microbial fuel cell technology. Electroactive species in the microbial community were evaluated using bioinformatics methodologies in whole genome sequencing, RNA sequencing, transcriptomics, metagenomics, and phylogenetics. Technology advancements in microbial fuel cells primarily produce power from organic and inorganic waste from various sources. Reduced chemical oxygen demand and waste degradation are two added advantages for microbial fuel cells. From plants, bacteria, and algae, microbial fuel cells were developed. Due to the rapid advancement of sequencing techniques, bioinformatics approaches are currently widely used in the technology of microbial fuel cells. In addition, they play an important role in determining the composition of electroactive species in microorganisms. The metabolic pathway is also possible to determine with bioinformatics resources. A computational technique that reveals the nature of the mediators and the substrate was also used to predict the electrochemical properties. Computational strategies were used to tackle significant challenges in experimental procedures, such as optimization and understanding microbiological systems. The main focus of this review is on utilizing bioinformatics techniques to improve microbial fuel cell technology.

Amritotharanam Kashyam, a specific Ayurvedic drug, was the focus of the current inquiry to evaluate its efficacy. For liver and digestive-related issues, this medication is suggested. This was obtained from a standard Ayurvedic vendor in Chennai (India), and GC-MS analysis was carried out according to the standard procedure. A few critical biomolecules include benzoic acid, hexadecanoic acid, 6,9-octadecadienoic acid, 9-octadecenoic acid, methyl ester (E)-, heptadecanoic acid, 16-methyl, methyl ester, methyl 18-methylnonadecanoate, tetracosanoic acid, distearin, hexadecanoic acid, and 1-(hydroxymethyl)-1,2-ethanediol ester. The obtained biomolecules exhibited some significant therapeutic functions, including acidification, inhibition of arachidonic acid formation, increase in the aromatic amino acid decarboxylase, suppression of uric acid generation, inhibitors of catechol-O-methyltransferase, urine acidifiers, etc. The anticancer and antiviral potential of these phytocompounds were investigated using molecular docking and dynamics. The phytocompounds pharmacokinetic characteristics were investigated using ADME analysis. Through docking and dynamics simulation, in silico tests demonstrated the phytocompounds' inhibitory efficiency against the target proteins. These functions reasonably relate to the medicinal function of Amritotharanam Kashyam. The MTT assay findings demonstrated this medication’s anticancer effects. The ability to be an effective drug is demonstrated by its antioxidant, anti-inflammatory, and membrane-stabilizing properties.

Cellulose nanocrystals (CNC) were prepared by formic acid hydrolysis and TEMPO- (2,2,6,6-tetramethyl-piperidine-1-oxyl-) mediated oxidation. The prepared CNCs were reinforced into biopolymers chitosan (CHI), alginate (ALG), and gelatin (GEL) to obtain “CNC-ALG-GEL” and “CNC-CHI-GEL” hydrogels. The synthesized hydrogels were characterized for physicochemical, thermal, and structural characterization using Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), thermal gravity analysis (TGA), and X-ray diffraction (XRD) analyses. Notably, the reinforcement of CNC has not altered the molecular structure of a biopolymer as revealed by FT-IR analysis. The hydrogels reinforced with CNC have shown better thermal stability and miscibility as revealed by thermal gravity analysis. The physicochemical, thermal, and structural characterization revealed the chemical interaction and electrostatic attraction between the CNC and biopolymers. The biocompatibility was investigated by evaluating the viability of the L929 fibroblast cell, which represents good biocompatibility and nontoxic nature. These hydrogels could be implemented in therapeutic biomedical research and regenerative medicinal applications.

The synthesis of silver nanoparticles has been gaining more attention in recent years due to their small size and high stability. For this study, silver nanoparticles were biosynthesized from leaf extract of the medicinal plant (N. arbor-tristis). Vitally, the shrub with tremendous medicinal usage was diversely observed in South Asia and South East Asia. The synthesized silver nanoparticles were characterized by color visualization, ultraviolet-visible spectrophotometry (UV-Vis), Fourier-transform infrared spectroscopy (FTIR), field emission-scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), and dynamic light scattering (DLS) technique. A sharp peak at 427 nm for biosynthesized nanoparticles was obtained using UV-Vis, which represents surface plasmon resonance. Thus, characterization techniques showed the green synthesis of AgNPs leads to the fabrication of spherical shape particles with a size of 67 nm. Furthermore, AgNPs were subjected to antibiofilm studies against Candida albicans and it was observed that 0.5 μg mL−1 of AgNPs significantly reduced 50% of biofilm formation. These biosynthesized nanoparticles also showed a considerable reduction in viability of HeLa cells at 0.5 μg mL−1. The morphological changes induced by AgNPs were observed by AO/EB staining. The toxic effect of AgNPs was studied using brine shrimp as a model system. Therefore, it is envisaged that further investigation with these AgNPs can replace toxic chemicals, assist in the development of biomedical implants that can prevent biofilm formation, and avoid infections due to C. albicans.