Aims: The purpose of this paper was to determine the lacrimal concentration of IL-1α and MMP-9 in patients with active ocular rosacea before and after systemic treatment with azithromycin or doxycycline. Methods: After 4 weeks of therapy with azithromycin (500 mg/day, 3 days a week PO) or doxycycline (200 mg/day PO), lacrimal samples were analyzed using an enzyme-linked immunosorbent assay multiplex. Results: There was a significant difference between baseline IL-1α (37.9 pg/mL) and MMP-9 (26.7 ng/mL) in rosacea eyes compared to controls (0.001 pg/mL for IL-1α and 0.2 ng/mL for MMP-9) (p < 0.001). IL-1α decreased from 47.0 pg/mL before azithromycin to 23.5 pg/mL after treatment (p = 0.024), but not after doxycycline therapy. On the contrary, baseline MMP-9 tear levels (10.28 ng/mL) decreased after treatment (8.36 pg/mL) with doxycycline (p = 0.054) but not with azithromycin. There was a strong clinical correlation of higher baseline IL-1α tear levels between patients who responded to doxycycline therapy and those who failed (p = 0.043). Patients unresponsive to azithromycin had significantly higher baseline MMP-9 levels than those with doxycycline (p = 0.040). Conclusions: While IL-1α levels decreased after azithromycin therapy, MMP-9 did so after doxycycline treatment. Baseline cytokine tear levels tend to be markedly elevated in patients with antibiotic failure, suggesting their potential role as therapeutic biomarkers for the disease.

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.

Diffuse large B-cell lymphoma (DLBCL) is classified into germinal center-like (GCB) and non-germinal center-like (non-GCB) cell-of-origin groups, entities driven by different oncogenic pathways with different clinical outcomes. DLBCL classification by immunohistochemistry (IHC)-based decision tree algorithms is a simpler reported technique than gene expression profiling (GEP). There is a significant discrepancy between IHC-decision tree algorithms when they are compared to GEP. To address these inconsistencies, we applied the machine learning approach considering the same combinations of antibodies as in IHC-decision tree algorithms. Immunohistochemistry data from a public DLBCL database was used to perform comparisons among IHC-decision tree algorithms, and the machine learning structures based on Bayesian, Bayesian simple, Naïve Bayesian, artificial neural networks, and support vector machine to show the best diagnostic model. We implemented the linear discriminant analysis over the complete database, detecting a higher influence of BCL6 antibody for GCB classification and MUM1 for non-GCB classification. The classifier with the highest metrics was the four antibody-based Perfecto–Villela (PV) algorithm with 0.94 accuracy, 0.93 specificity, and 0.95 sensitivity, with a perfect agreement with GEP (κ = 0.88, P < 0.001). After training, a sample of 49 Mexican-mestizo DLBCL patient data was classified by COO for the first time in a testing trial. Harnessing all the available immunohistochemical data without reliance on the order of examination or cut-off value, we conclude that our PV machine learning algorithm outperforms Hans and other IHC-decision tree algorithms currently in use and represents an affordable and time-saving alternative for DLBCL cell-of-origin identification.

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.