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.

En este trabajo se presenta un desarrollo para mejorar el desempeño del algoritmo de optimización metaheurístico nombrado Particle Swarm Optimization (PSO). El algoritmo PSO está inspirado en el comportamiento que demuestran los grupos de individuos en la naturaleza, como ejemplo podemos mencionar las parvadas y los cardúmenes. Cada individuo o partícula, de forma análoga en un proceso matemático; es considerado como una posible solución y en ellos se contempla, como información relevante, su posición y la velocidad. La velocidad de cada partícula es modificada al multiplicarse por un parámetro nombrado factor de inercia y es este parámetro que proponemos modificar para mejorar el desempeño del algoritmo. La modificación del factor de inercia se desarrolla de dos maneras, decremento lineal y decremento caótico. Se han considerado las funciones de referencia Eggholder y Six-Hump Camelback, para determinar la mejora en el desempeño del algoritmo PSO. Los resultados presentados en este trabajo indican un mejor desempeño al aplicar el decremento de tipo caótico al factor de inercia.

This work deals with the presentation of a spiking neural network as a means for efficiently solving the reduction of dimensionality of data in a nonlinear manner. The underneath neural model, which can be integrated as neuromorphic hardware, becomes suitable for intelligent processing in edge computing within Internet of Things systems. In this sense, to achieve a meaningful performance with a low complexity one-layer spiking neural network, the training phase uses the metaheuristic Artificial Bee Colony algorithm with an objective function from the principals in the machine learning science, namely, the modified Stochastic Neighbor Embedding algorithm. To demonstrate this fact, complex benchmark data were used and the results were compared with those generated by a reference network with continuous-sigmoid neurons. The goal of this work is to demonstrate via numerical experiments another method for training spiking neural networks, where the used optimizer comes from metaheuristics. Therefore, the key issue is defining the objective function, which can relate optimally the information at both sides of the spiking neural network. Certainly, machine learning techniques have advanced in defining efficient loss functions that can become suitable objective function candidates in the metaheuristic training phase. The practicality of these ideas is shown in this article. We use MSE values for evaluating the relative quality of the results and also co-ranking matrices.

This work presents an automatic system for the semantic segmentation of cutaneous macules using an architecture based on an Autoencoder. The dataset used was obtained from Kaggle and manually segmented by the author for the purpose of generating ground truth masks. The proposed architecture incorporates Inception V1 modules in the encoder and Batch Normalization layers with Leaky ReLU activation functions in the decoder, which improves training stability and reduces computational consumption. The model was evaluated using the Dice and IoU metrics, achieving values of 0.91 and 0.84 respectively, on a validation set. The results obtained suggest that automatic segmentation can support early melanoma diagnosis by highlighting regions of interest relevant for subsequent morphological and textural analysis.