Comparative Analysis of Macular and Optic Disc Perfusion Pre and Post Silicone Oil Removal: A Machine Learning Approach.

In the realm of ophthalmic surgeries, silicone oil is often utilized as a tamponade agent for repairing retinal detachments, but it necessitates subsequent removal. This study harnesses the power of machine learning to analyze the macular and optic disc perfusion changes pre and post-silicone oil removal, using Optical Coherence Tomography Angiography (OCTA) data. Building upon the foundational work of prior research, our investigation employs Gaussian Process Regression (GPR) and Long Short-Term Memory (LSTM) networks to create predictive models based on OCTA scans. We conducted a comparative analysis focusing on the flow in the outer retina and vessel density in the deep capillary plexus (superior-hemi and perifovea) to track perfusion changes across different time points. Our findings indicate that while machine learning models predict the flow in the outer retina with reasonable accuracy, predicting the vessel density in the deep capillary plexus (particularly in the superior-hemi and perifovea regions) remains challenging. These results underscore the potential of machine learning to contribute to personalized patient care in ophthalmology, despite the inherent complexities in predicting ocular perfusion changes.

Analyzing Sequence Data with Markov Chain Models in Scientific Experiments.

Virtual reality-based instruction is becoming an important resource to improve learning outcomes and communicate hands-on skills in science laboratory courses. Our study attempts first to investigate whether a Markov chain model can predict the students' performance in conducting an experiment and whether simulations improve learner achievement in handling lab equipment and conducting science experiments in physical labs. In the present study, three cohorts of graduate students are trained on a microscopy experiment using different teaching methodologies. The effectiveness of the teaching strategies is evaluated by observing the sequences of students' actions, while engaging in the microscopy experiment in real-lab situations. The students' ability in performing the science experiment is estimated by sequential analysis using a Markov chain model. According to the Markov chain analysis, the students who are trained via a virtual reality software exhibit a higher probability to perform the steps of the experiment without difficulty and without assistance than their fellow students who attend more traditional training scenarios. Our study indicates that a Markov chain model is a powerful tool that can lead to a dynamic evaluation of the students' performance in science experiments by tracing the students' knowledge states and by predicting their innate abilities.

Implementation and Evaluation of a Three-Dimensional Virtual Reality Biology Lab versus Conventional Didactic Practices in Lab Experimenting with the Photonic Microscope.

This study presents the integration of three different teaching scenarios, during biology laboratory lessons, with the overall aim of exploring the potential predominant effectiveness of teaching and improvement of students' learning, by the use of the three-dimensional virtual reality educational tool Onlabs, versus more traditional didactic practices. A sample of 83, fourth year, undergraduate students of the Primary Education Department of Patras' University in Greece, were equally separated into three cognitively balanced groups to be educated on the light microscopy experiment by three different educational scenarios. Students' conceptual understanding in the domain of microscopy, was evaluated during all learning procedure with Pre and Post tests, whereas their skill to handle properly a real light microscope in the wet biology lab was summatively assessed via a specially designed work sheet. Results of the present study provide evidence in favor of the virtual reality application. © 2019 International Union of Biochemistry and Molecular Biology, 48(1):21-27, 2020.

Expression of inducible nitric-oxide synthase and intracellular protein tyrosine nitration in vascular smooth muscle cells: role of reactive oxygen species.

A significant increase in the induction of inducible nitric-oxide synthase (iNOS) protein expression and in the levels of nitrite plus nitrate was observed in rat aortic smooth muscle cells (RASMCs) stably transfected with catalase (RASMC-2C2) as compared with empty vector-transfected RASMC-V4 cells after exposure to cytokines and lipopolysaccharide. The increased expression of iNOS protein in the RASMC-2C2 cells was associated with a significant activation of nuclear transcription factor kappaB, one of the transcriptional regulators of iNOS expression. The induction of iNOS was also accompanied by increased protein tyrosine nitration in both cell types as revealed by immunocytochemical staining and high pressure liquid chromatography with on-line electrospray ionization tandem mass spectrometry. Nitrotyrosine formation was inhibited by 1400W, an iNOS inhibitor, by 4-(2-aminoethyl) benzenesulfonyl fluoride, an inhibitor of NADPH oxidase, and by the superoxide dismutase mimetic M40403, but not by the peroxidase inhibitor 4-aminobenzoic hydrazide. Electron microscopy using affinity-purified anti-nitrotyrosine antibodies revealed labeling at the cytosolic side of the rough endoplasmic reticulum membranes, in the nucleus, occasionally in mitochondria, and consistently within the fibrillar layer underneath the plasma membrane. Collectively, the data in this model system indicate that hydrogen peroxide, by inhibiting the activation of nuclear transcription factor kappaB, prevents iNOS expression, whereas superoxide contributes in a precise pattern of intracellular protein tyrosine nitration.