The increasing threat from antibiotic-resistant bacteria has necessitated the development of novel methods to counter bacterial infections. In this context, the application of metallic nanoparticles (NPs), especially gold (Au) and silver (Ag), has emerged as a promising strategy due to their remarkable antibacterial properties. This review examines research published between 2006 and 2023, focusing on leading journals in nanotechnology, materials science, and biomedical research. The primary applications explored are the efficacy of Ag and Au NPs as antibacterial agents, their synthesis methods, morphological properties, and mechanisms of action. An extensive review of the literature on NPs synthesis, morphology, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and effectiveness against various Gram(+/−) bacteria confirms the antibacterial efficacy of Au and Ag NPs. The synthesis methods and characteristics of NPs, such as size, shape, and surface charge, are crucial in determining their antibacterial activity, as these factors influence their interactions with bacterial cells. Furthermore, this review underscores the urgent necessity of standardizing synthesis techniques, MICs, and reporting protocols to enhance the comparability and reproducibility of future studies. Standardization is essential for ensuring the reliability of research findings and accelerating the clinical application of NP-based antimicrobial approaches. This review aims to propel NP-based antimicrobial strategies by elucidating the properties that enhance the antibacterial activity of Ag and Au NPs. By highlighting their inhibitory effects against various bacterial strains and relatively low cytotoxicity, this work positions Ag and Au NPs as promising materials for developing antibacterial agents, making a significant contribution to global efforts to combat antibiotic-resistant pathogens.

Microplastics are ubiquitous in the environment and represent a serious risk to ecosystems and human health. This study describes a prototype sensor designed to detect microplastics by analyzing samples with multiple wavelengths of light. The system uses samples of commercially available microplastics and includes passive low-pass filters to reduce noise for accurate readings. The prototype measures light attenuation and produces color spectra that helps identify microplastics. Its design focuses on precision, electronic control, and optical sensing, making it an affordable option for environmental monitoring. Early experiments demonstrate that the prototype is sensitive and reliable under a variety of conditions. Future improvements may add machine learning and fuzzy logic to increase accuracy and expand its use.

Popcorn maize (Zea mays everta) exhibits complex morphologies that challenge structural analysis. This study assessed the fidelity of the three-dimensional (3D) reconstruction and printing of four popcorn morphologies, unilateral, bilateral, multilateral, and mushroom, by integrating structured-light 3D scanning and (DIA), which can support the construction of food replicas. Morphometric parameters (projected area, perimeter, Feret diameter, circularity, and roundness) and fractal descriptors (fractal dimension, lacunarity, and entropy) were quantified as the relative ratios of printed/real parameters (P/R) to compare real flakes with their 3D-printed counterparts. Results revealed the lowest mean errors for Feret diameter (6%) and projected area (10%), while deviations in circularity and roundness were more pronounced in mushroom flakes. With respect to the actual mean values of the morphological parameters, real flakes showed slightly larger perimeter values (86 mm for real and 82 mm for printed objects) and a higher fractal dimension (1.36 for real and 1.33 for printed), indicating greater texture irregularity, whereas the projected area remained highly comparable (225 mm² in real/229 mm² in printed). These parameters reinforced that the overall morphological fidelity remained high (P/R = 0.9–1.0), despite localized deviations in circularity and fractal descriptors. Less complex morphologies (unilateral and bilateral) demonstrated higher structural fidelity (P/R = 0.95), whereas multilateral and mushroom types showed greater variability due to surface irregularity. Fractal dimension and lacunarity effectively described textural complexity, highlighting the role of flake geometry and moisture in determining expansion patterns and printing accuracy. Principal Component Analysis confirmed that circularity and fractal indicators are critical descriptors for distinguishing morphological fidelity. Overall, the findings demonstrated that 3D scanning and printing provided reliable physical replicas of irregular food structures as popcorn flakes supporting their application in food engineering.

Microplastics (MPs) are pervasive in marine ecosystems, yet the molecular mechanisms underlying their sublethal effects on zooplankton remain poorly understood. Here, we integrated transcriptomic screening with sequence homology analysis, structural modeling, and molecular dynamics (MD) simulations to examine protein–polymer interaction behavior in the tropical euphausiid Euphausia distinguenda. Transcriptomic analysis identified three MP-responsive proteins; two isoforms (P1, P2) were consistently downregulated following exposure to polyethylene microspheres, while a third candidate (P3) was upregulated but excluded from modeling due to insufficient homology. Structural models of P1 and P2 were generated using AlphaFold2 (ColabFold implementation) and evaluated through comparative structural analyses. MD simulations (100 ns) were conducted using representative polymer oligomers LDPE used as a polyethylene analog and PET and PC included as contrast polymers, which revealed pronounced protein-specific differences and polymer-dependent modulation of interaction stability. Nonbonded Lennard–Jones and Coulomb interaction energies indicated substantially greater intermolecular contact stabilization for P2 than for P1 across all polymers, with LDPE yielding the lowest mean polymer RMSD for P2, and PET for P1. Together, these results indicate that MP–protein interactions in E. distinguenda are consistent with a dominant role of protein structural coherence and dynamic surface compatibility rather than by static structural complementarity or specific binding affinity and provide a reproducible basis for exploring molecular-scale responses to MP exposure in ecologically relevant zooplankton.

Chronic neuroinflammation driven by microglial activation is a pathological hallmark of neurodegenerative diseases, and the NF-$\kappa$B/iNOS signaling axis plays a central role in propagating this damage. NF-$\kappa$B-mediated iNOS transcription generates excessive nitric oxide, causing oxidative neuronal injury. The medicinal mushroom Hericium erinaceus produces cyathane diterpenoid erinacines and isoindolinone erinacerins, both reported to attenuate neuroinflammation; however, the molecular basis of their interactions with iNOS and NF-$\kappa$B remains poorly characterized. We screened 21 erinacerins and 18 erinacines against both targets using validated molecular docking, then subjected top-ranked candidates and negative controls to 100 ns molecular dynamics simulations, MM-PBSA binding free energy calculations (±SEM), per-residue energy decomposition, backbone RMSD, and ligand–protein minimum distance analyses, with quercetin as reference. The analysis revealed scaffold-dependent target selectivity: erinacerins exhibited preferential stability with iNOS (erinacerin L: RMSD 0.185 nm), whereas erinacines formed more stable complexes with NF-$\kappa$B (erinacines G and J: RMSD < 0.36 nm). Minimum-distance monitoring confirmed that the elevated ligand RMSD in iNOS predominantly reflected surface relocation rather than dissociation. Erinacine S emerged as the most promising dual-target candidate ($\Delta G_{bind}$: −24.31 ± 0.16 and −14.24 ± 0.11 kcal/mol for iNOS and NF-$\kappa$B, respectively), over twofold stronger than quercetin for iNOS. Negative controls revealed that docking-based ranking was target-dependent in its discriminative capacity, underscoring the need for MD-based refinement. These results identify erinacine S as a priority candidate for experimental validation.

To manage residues from various marine brown algae (Sargassum spp.) accumulated as environmental liabilities on Mexican beaches, the optimal conditions for bioethanol production were determined. For this purpose, primary fermentation was carried out using Saccharomyces spp., using the algae as a carbon source. Subsequently, a refining process was performed using a distiller designed to enhance bioalcohol yield. Finally, the applicability of the bioethanol in gasoline-powered vehicles was assessed through a vapor component mixing system coupled to the engine (FSD). Vehicle studies demonstrated 93% engine efficiency using the FSD and bioethanol. This indicates a 15-20% enhancement in efficiency under urban driving conditions. Furthermore, it was observed that efficiency improved (approximately 30%) on highways per liter of gasoline blended with 20% alcohol by volume (in gas phase), depending on engine conditions and other factors affecting the engine function, like altitude, road conditions, and upward mountain slopes.