We report here an innovative feature of green nanotechnology-focused work showing that mangiferin—a glucose functionalized xanthonoid, found in abundance in mango peels—serves dual roles of chemical reduction and in situ encapsulation, to produce gold nanoparticles with optimum in vivo stability and tumor specific characteristics. The interaction of mangiferin with a Au-198 gold precursor affords MGF-198AuNPs as the beta emissions of Au-198 provide unique advantages for tumor therapy while gamma rays are used for the quantitative estimation of gold within the tumors and various organs. The laminin receptor specificity of mangiferin affords specific accumulation of therapeutic payloads of this new therapeutic agent within prostate tumors

In this study, thin films of pure titanium dioxide (TiO2) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO)x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO)x. This trend reversed at x=0.20, where a decrease in roughness occurred. Interestingly, a sub

In this study, thin films of pure titanium dioxide (TiO 2 ) and titanium dioxide dual mixed with zinc oxide (ZnO) and magnesium oxide (MgO) with varying concentrations of (ZnO: MgO) x ranging from 0 to 30 wt% undoped and doped gold nanoparticles (AuNPs) were prepared on glass using the chemical spray pyrolysis (CSP) technique. The morphological, structural, and sensing properties of the prepared thin films were examined. Atomic Force Microscopy (AFM) analysis revealed that these films exhibit a consistent structure before and after doping. Initially, the roughness of these films was observed to increase upon the introduction of impurities (ZnO: MgO) x . This trend reversed at x = 0.20, where a decrease in roughness occurred. Interestin

This study analyzes the stylistic features of euphemisms in feminist discourse, based on material from the contemporary Russian press of the period (2015–2025). The central issue lies in how the linguistic devices used to soften the harshness and confrontation in discourse contribute to shaping a distinct feminine communicative style within media discourse, where the context necessitates combining the expression of the author\\'s position with maintaining a harmonious interaction with the wider audience. This study aims to uncover and classify the stylistic mechanisms of euphemism at the lexical-grammatical, syntactic, and pragmatic levels, as well as to determine its role in constructing gendered linguistic behavior. The research finding

Orange peel was used as a plant-derived medium to prepare a ZnO–calcite mixed-phase nanostructured material, which was physicochemically characterized and evaluated for DPPH radical-scavenging, α-glucosidase inhibitory, and in vitro cytotoxic activities. XRD confirmed the coexistence of hexagonal wurtzite ZnO and crystalline calcite, with ZnO apparent crystallite sizes ranging from 9.2 to 28.3 nm (mean 16.4 ± 7.3 nm). FTIR identified Zn–O vibrations, carbonate-related bands, surface hydroxyl groups, and residual organic functionalities, while FESEM revealed irregular agglomerates composed of nanoscale grains. EDX showed Zn, O, C, and Ca as the principal elements, and the material exhibited a zeta potential of -14.86 ± 0.52 mV

The complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra