Symeon Savvopoulos

Background: Severe COVID-19 is a global health concern despite continuous vaccination campaigns because current therapies, such as dexamethasone and remdesivir, do not considerably improve immune function, especially in high-risk individuals. SARS-CoV-2–specific T cells (CoV-2-STs) from vaccinated or convalescent donors are a promising new treatment that can enhance clinical outcomes and viral-specific immunity. CoV-2-STs improve T cell proliferation and recovery without raising safety concerns, according to randomized studies. Targeting patients for immunotherapy is made more difficult by the variability in COVID-19 progression brought on by variables like age and comorbidities. In order to further enable precision medicine and patient care, machine learning techniques are being used to analyze clinical data, predict disease severity, and optimize treatment. However, their use in guiding the treatment of novel therapies like CoV-2-STs using early cellular immunology data is limited and requires improvement. Objective: The purpose of this research was to stratify high-risk individuals using early immunological and clinical indicators, and to develop a prediction tool to enable individualized treatment decisions, either with standard of care (SoC) or with a combination of CoV-2-STs and SoC. Methods: A randomized phase 1‐2 trial enrolled 87 patients with severe COVID-19 (CoV-2-STs+SoC: n=57; SoC only: n=30). We performed a post hoc machine learning analysis. Clinical and biomarker data from days 0 and 5 were analyzed longitudinally. Shrinkage linear discriminant analysis was used to create arm-specific prognostic models, and stratified cross-validation assessed performance (area under the receiver operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, F ₁ -score, and Brier score). To investigate arm-swap scenarios, Monte Carlo simulations (4 variance models) generated hypothetical early-treatment trajectories. Results: Day 5 analysis revealed a significant difference in CD3+, CD8+, CD56+, and CoV-2-STs between the 2 treatment groups (CoV-2-STs+SoC vs SoC). At Day 60, 64.9% (37/57) of patients with CoV-2-STs+SoC survived, compared with 40% (12/30) of patients with only SoC, resulting in a crude odds ratio of 2.8 (95% CI 1.1‐6.9) for recovery. SoC-only models had an area under the curve between 0.72 and 0.76, while CoV-2-STs+SoC models had an area under the curve between 0.86 and 0.88. Area under the precision-recall curve values for CoV-2-STs+SOC models were 0.74‐0.78, with sensitivity ranging from 0.89 to 0.91 and specificity from 0.83 to 0.87, compared with SoC-only models with a sensitivity of approximately 0.95 and a specificity of 0.58‐0.62. Simulation studies indicate that CoV-2-STs+SoC may benefit up to 30% (approximately 9/30) of patients receiving SoC alone. Misclassifying candidates with CoV-2-STs+SoC as SoC-only could increase critical outcomes by up to approximately 22%. Conclusions: A robust computational tool for severe COVID-19 risk stratification and treatment selection is presented. Precision medicine and early treatment outcome prediction are supported by clinical and immunological data integration. Prospective studies are needed to confirm its clinical utility.

Systemic interactions between distant organs, such as the oral-brain axis, remain largely unknown. This study aimed to shed light on a specific aspect of these oral-brain interactions, by focusing on the brain, in the form of psychological traits, the oral microbiome, and metabolism (BMM) with respect to the influence of smoking on the oral microbiota. We analyzed three datasets covering microbiome composition, psychological traits, and metabolic pathways in smokers and non-smokers. We used Canonical Correlation Analysis (CCA) to reduce the dimensionality of the three different datasets into scalar variables, and network analysis to identify the interactions between important features of the BMM system. Nonlinear regression revealed steady state interactions, showing tight correlations between metabolic pathways and microbiome composition. Smoking weakens the directed interaction between metabolism and psychological traits. Sensitivity analysis demonstrated the system’s robustness to small perturbations, but large disruptions could destabilize it, potentially leading to oral pathologies or psychological conditions. These findings highlight smoking’s complex role in BMM interactions and its implications for managing psychological conditions in smokers.

Understanding the reactivity and kinetics of feedstock particles is critical for process optimization, reactor design, and industrial gasifier scaling. This study investigates the impact of particle size on the gasification behavior of coal, focusing on the temperature and gas species concentration profiles during the process. Coal particles of varying sizes (1 mm, 4 mm, 6 mm, and 8 mm) were subjected to high-temperature gasification in a furnace maintained at 1073 K and under limited air supply. Temperature profiles are analyzed. The results indicate that smaller particles (4 mm) achieved higher peak temperatures suggesting faster reaction rates, while larger particles (6mm and 8mm) exhibited slower and lower peak temperatures. We also employed a sophisticated mathematical model integrating key equations for energy balance, heat flux, and char mass balance to simulate the residence time, core temperature and gas concentrations of O2, CO, and CO2 of the coal. Experimental data and model estimates indicate that thermal conductivity increases with particle size, while activation energy decreases at lower temperatures, suggesting higher reaction rates for smaller particles. Based on the validated numerical model, the sharper and faster reactions observed in the gas species concentration profiles for smaller particles indicate quicker gasification. The results show good alignment between experimental data and model predictions. These findings underscore the critical influence of particle size on thermal dynamics and reaction kinetics during coal gasification, highlighting the need for optimized particle size distribution to maximize gasification efficiency.

This study investigates waste brine freeze desalination within a designed, cooled, jacketed crystallizer system. The study combines experimental findings with numerical simulation to interpret the feasibility of this approach. In the experimental phase, actual waste brine samples were collected from several seawater reverse osmosis (RO) units across the Arabian Gulf and analyzed for their ion composition. Furthermore, synthetic concentrated NaCl solution freezing inside the jacketed crystallizer by radial freezing from outer to center with stirring is carried out. The experimental results confirm that stirring enhances desalination efficiency, and it is found that 30 rpm’s efficiency outperforms 60 rpm. Moreover, a computational fluid dynamics (CFD) model of the crystallizer was also developed to understand heat and mass transfer during the freezing process under various conditions. The developed model captures the salinity growth trend, although discrepancies likely result from imperfect insulation at the top and bottom of the crystallizer. The results highlight the crucial role of stirring in homogenizing concentrated brine, effectively preventing the formation of highly concentrated layers at the freezing front. This research underscores the promise of seawater/waste brine freeze desalination as a sustainable and efficient solution for global freshwater scarcity.

Background: Type 2 diabetes mellitus (T2DM) are 90% of diabetes cases, and its prevalence and incidence, including comorbidities, are rising worldwide. Clinically, diabetes and associated comorbidities are identified by biochemical and physical characteristics including glycemia, glycated hemoglobin (HbA1c), and tests for cardiovascular, eye and kidney disease. Objectives: Diabetes may have a common etiology based on inflammation and oxidative stress that may provide additional information about disease progression and treatment options. Thus, identifying high-risk individuals can delay or prevent diabetes and its complications. Design: In patients with or without hypertension and cardiovascular disease, as part of progression from no diabetes to T2DM, this research studied the changes in biomarkers between control and prediabetes, prediabetes to T2DM, and control to T2DM, and classified patients based on first-attendance data. Control patients and patients with hypertension, cardiovascular, and with both hypertension and cardiovascular diseases are 156, 148, 61, and 216, respectively. Methods: Linear discriminant analysis is used for classification method and feature importance, This study examined the relationship between Humanin and mitochondrial protein (MOTSc), mitochondrial peptides associated with oxidative stress, diabetes progression, and associated complications. Results: MOTSc, reduced glutathione and glutathione disulfide ratio (GSH/GSSG), interleukin-1β (IL-1β), and 8-isoprostane were significant ( P < .05) for the transition from prediabetes to t2dm, highlighting importance of mitochondrial involvement. complement component 5a (c5a) is a biomarker associated with disease progression and comorbidities, gsh gssg, monocyte chemoattractant protein-1 (mcp-1), 8-isoprostane being most important biomarkers. Conclusions: Comorbidities affect the hypothesized biomarkers as diabetes progresses. Mitochondrial oxidative stress indicators, coagulation, and inflammatory markers help assess diabetes disease development and provide appropriate medications. Future studies will examine longitudinal biomarker evolution.