Evaluating effectiveness of immersive virtual reality in promoting students’ learning and engagement: a case study of analytical biotechnology engineering course

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

Upper mesophotic reef fish assemblages at Bahía de Banderas, Mexico

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

In vitro analysis of postbiotic antimicrobial activity against Candida Species in a minimal synthetic model simulating the gut mycobiota in obesity

Abstract Gut fungal imbalances, particularly increased Candida spp., are linked to obesity. This study explored the potential of Lactiplantibacillus plantarum cell-free extracts (postbiotics) to modulate the growth of Candida albicans and Candida kefyr, key members of the gut mycobiota. A minimal synthetic gut model was employed to evaluate the effects of Lactiplantibacillus plantarum postbiotics on fungal growth in mono- and mixed cultures. Microreactors were employed for culturing, fungal growth was quantified using CFU counting, and regression analysis was used to evaluate the effects of postbiotics on fungal growth. Postbiotics at a concentration of 12.5% significantly reduced the growth of both Candida species. At 24 h, both C. albicans and C. kefyr in monocultures exhibited a decrease in growth of 0.11 log CFU/mL. In contrast, mixed cultures showed a more pronounced antifungal effect, with C. albicans and C. kefyr reductions of 0.62 log CFU/mL and 0.64 log CFU/mL, respectively. Regression analysis using the Gompertz model supported the antifungal activity of postbiotics and revealed species-specific differences in growth parameters. These findings suggest that L. plantarum postbiotics have the potential to modulate the gut mycobiota by reducing Candida growth, potentially offering a therapeutic approach for combating fungal overgrowth associated with obesity

Unraveling the Complex Interactions: Machine Learning Approaches to Predict Bacterial Survival against ZnO and Lanthanum-Doped ZnO Nanoparticles

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