Yocanxóchitl Perfecto-Avalos

Oil-in-water nanoemulsions (NE) can increase the water solubility of plant-derived bioactive molecules as drug candidates. Machine learning-guided NE design can prevent the expensive, time-consuming trial-and-error process. NE composition data was aggregated into a dataset; a predictive machine learning model identified improved self-nanoemulsifying system formulations (olive oil and combinations of Tween 20, Tween 80, glycerol, and soy lecithin). Predictive power was assessed by estimating successful self-nanoemulsification through transmittance and Dynamic Light Scattering. NEs were loaded with an organic extract containing anacardic acid. Encapsulation efficiency was measured by UHPLC. Antiproliferative activity was evaluated on human hepatic cancer (Hep G2) and normal-like human embryonic kidney (HEK-293) cell lines. The model showed an accuracy of 81%. The best-performing formulation, consisting of 10% olive oil, 60% Tween 20, and 30% glycerol, exhibited an average particle size of 162.8 ± 26 nm, a polydispersity index of 0.234 ± 0.03, and high encapsulation efficiency. While HEK-293 cells remained unaffected, naked NE exhibited a selective growth inhibitory effect on the Hep G2 cell line. Loaded NE increased the cytotoxic effect on Hep G2 (IC50: 5.9 ± 1.27 µM). Machine learning-guided NE formulation was a successful carrier for the plant extract and the molecule of interest, providing a proof of concept for how artificial intelligence can shorten the development pipeline for NE drug delivery systems.

In this work, extracts from the pulp, peel, and seed of Manilkara zapota were obtained via lyophilization and oven drying. Bromatological analyses were performed to investigate variabilities in the nutritional content of fruits after nine post-harvest days. The phytochemical content of fruits was assessed by gas chromatography flame ionization detector (GC-FID), and their biological performance was studied using in vitro antibacterial and antioxidant assays (DPPH and ABTS) and in vivo toxicity models. Molecular docking was implemented to evaluate the interaction between polar compounds from chicozapote fruits with receptors involved in the pathogenesis of bacterial strains. Results revealed that water or soluble solids content did not vary after post-harvest. It was demonstrated that lyophilization or oven-drying approaches influenced the insoluble, total dietary fiber and digestible carbohydrates among samples. According to GC-FID analysis, it was observed that lyophilization and oven-drying methods also altered the content of myristic and pentadecanoic acids among the obtained extracts. It was noted that the antibacterial and antioxidant activities of extracts were weak due to their MIC (>1000 μg/mL) and IC₅₀ (>2000 μg/mL) values. Still, the toxicity of extracts was poor against Artemia salina nauplii. In silico evaluation unveiled that polar compounds in M. zapota fruits possess a high binding affinity towards the DNA gyrase B of the cultured strains. This study expands the scientific evidence regarding the influence of distinct extraction methods on the nutritional and nutraceutical content of native fruits and the importance of considering additional approaches to enhance their bioactivities.

This paper explores the introduction of problem-based learning (PBL) in Biomedical Engineering at Tecnologico de Monterrey, comparing experiences from 2014 to 2019 between medical and biomedical engineering students. Medical students consistently outperform biomedical engineering counterparts, attributed to higher admission standards. Despite minor improvements in biomedical engineering grades, the differences remain statistically insignificant. Initial challenges for students involve adapting to a self-directed learning model. Biomedical engineers excel in cell biology compared to biochemistry courses. Students recognize PBL's benefits in acquiring theoretical knowledge and facing intellectual challenges, emphasizing its value in biomedical engineering education. We conclude that PBL implementation should be considered in other engineering courses.

There is no information on the species associated with the mesophotic reefs of Banderas Bay, located in the central Mexican Pacific. This study analysed the reef fish assemblage from three depths (50, 60 and 70 m) in three sampling sites of the southern submarine canyon of the Bay: Los Arcos, Bajo de Emirio and Majahuitas. Several analyses were performed to test the hypothesis that there are important differences in fish abundance and species composition between sites and depths. Twenty-two species of bony fishes grouped in 14 families were recorded. PERMANOVA results showed that there were no significant differences in fish diversity parameters between sites, indicating a certain uniformity in their distribution. However, nine species were exclusive to one site and depth (five singleton species with only one individual recorded and four unique species recorded only once). On the other hand, there were significant differences between depths, mainly between 50 and 70 m. Diversity decreases with depth and species composition changes. SIMPER, Shade Plot and NMDS analysis show the most representative species at each depth, with at least half of the species (11) recorded only at 50 m and four species at the deeper levels (60 - 70 m). The observed assemblage includes several of the most caught species in the shallow water artisanal fishery, which is the most traditional and common type of fishery in the Bay. In addition, the Pomacanthus zonipectus (Cortés angelfish) is of particular interest, as it has a special protection status in the official Mexican standard (NOM-059-SEMARNAT, 2010) due to its use as an ornamental species in aquaria. We hypothesised that the mesophotic zone may serve as a refuge for these fishes, so we propose that the information obtained is an important basis for new research aimed at the sustainable management of fisheries in the area.

The rise in antibiotic-resistant bacteria is a global health challenge. Due to their unique properties, metal oxide nanoparticles show promise in addressing this issue. However, optimizing these properties requires a deep understanding of complex interactions. This study incorporated data-driven machine learning to predict bacterial survival against lanthanum-doped ZnO nanoparticles. The effect of incorporation of lanthanum ions on ZnO was analyzed. Even with high lanthanum concentration, no significant variations in structural, morphological, and optical properties were observed. The antibacterial activity of La-doped ZnO nanoparticles against Gram-positive and Gram-negative bacteria was qualitatively and quantitatively evaluated. Nanoparticles induce 60%, 95%, and 55% bacterial death against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, respectively. Algorithms such as Multilayer Perceptron, K-Nearest Neighbors, Gradient Boosting, and Extremely Random Trees were used to predict the bacterial survival percentage. Extremely Random Trees performed the best among these models with 95.08% accuracy. A feature relevance analysis extracted the most significant attributes to predict the bacterial survival percentage. Lanthanum content and particle size were irrelevant, despite what can be assumed. This approach offers a promising avenue for developing effective and tailored strategies to reduce the time and cost of developing antimicrobial nanoparticles.

We conducted an immersive virtual reality (IVR) intervention in an analytical biotechnology course to evaluate its effectiveness in promoting student learning and engagement. The objective was to assess the impact of an IVR tool on learning infrared (IR) spectroscopy and academic engagement. The IVR experience was integrated into the course sessions, which included theoretical framework, IR spectra exercises, an individual quiz, and competition games. Students were immersed in a first-person IVR experience simulating an analytical room, where they performed sample analysis and operated a virtual IR spectrometer. The quiz and survey assessed their understanding and perception of the IVR experience. When compared to control groups, IVR intervention group achieved higher scores on midterm exam questions related to IR spectroscopy and reported greater academic engagement according to the satisfaction survey. The findings highlighted the effectiveness of IVR in enhancing learning outcomes, curiosity, motivation, and engagement among students. Moreover, our research provides empirical evidence of IVR’s positive impact on student learning and engagement in the context of biotechnology engineering. Based on the study’s implications, educators in engineering fields should consider integrating IVR as an instructional tool, particularly for laboratory-related topics with limited equipment accessibility and overcrowded courses. Future research should explore the generalizability of these findings across different subjects and educational levels. Additionally, investigating the underlying factors influencing the effectiveness of IVR in promoting academic engagement would further enhance the understanding and implementation of this technology in educational settings.