Erythrocyte Distribution by Surface Charge: Biophysical Characteristics of Heterogeneity, Diagnostic Significance and Application in Transfusionology and Biobanking

2025-11-12T17:57:18+02:00

Long-term storage of erythrocytes, a vital component of modern transfusion medicine, is accompanied by structural, metabolic, and biophysical alterations that reduce their functional activity. Differentiating erythrocytes by surface charge represents a promising approach for assessing viability, predicting post-transfusion behavior, and optimizing storage and biobanking strategies. However, the practical application of zeta potential analysis in clinical transfusion medicine remains insufficiently explored.

To summarize current knowledge on erythrocyte heterogeneity by surface charge, their biophysical properties, diagnostic value, and application prospects in transfusion medicine and biobanking.

A systematic literature search was conducted using PubMed, Scopus, Web of Science, Cochrane Library, Google Scholar, and Clinical Key, focusing on studies addressing erythrocyte biophysics, surface charge parameters, population heterogeneity, zeta potential measurement methods, and clinical applications.

Erythrocyte heterogeneity by surface charge is a fundamental population property shaped by physiological aging, oxidative stress, and membrane–cytoskeleton modifications. Reduced zeta potential is associated with enhanced aggregation, impaired deformability, and decreased microvascular passage. Analysis of zeta potential enables identification of subpopulations with varying levels of damage and prediction of their functional performance after transfusion. In transfusion medicine, this approach may improve transfusion efficiency and safety, while in biobanking it offers opportunities for better selection of cells for long-term storage. Surface charge differentiation also holds promise for predicting erythrocyte shelf life and advancing personalized transfusion strategies.

Integration of zeta potential analysis into laboratory practice could enhance the quality of blood components and support the development of personalized transfusion medicine.

Results of Machine Learning Applications in the Study of COVID-19 Associated Cardiopulmonary Pathology Using Computed Tomography Data

2025-03-24T00:21:06+02:00

Background. The COVID-19 pandemic, caused by SARS-CoV-2, has significantly impacted global health, emphasizing the importance of efficient diagnostic methods. Computed tomography (CT) imaging plays a crucial role in identifying COVID-19-associated lung pathologies, yet manual analysis of extensive imaging data remains burdensome. Machine learning (ML) methods offer promising automated solutions to expedite diagnostics and reduce workload on radiologists.

Objective. To evaluate the effectiveness of machine learning algorithms, specifically convolutional neural networks (CNN) and texture analysis methods, in automated detection and classification of COVID-19-related cardiopulmonary pathology using chest CT imaging.

Methods. This study analyzed chest CT datasets obtained from clinical resources, which included images from patients with COVID-19 exhibiting ground-glass opacities, crazy-paving patterns, and consolidations. Regions of interest (ROIs) were segmented and classified using various machine learning approaches: CNN combined with gray-level co-occurrence matrix (GLCM) texture analysis, logistic self-organizing forest (LSOF), group method of data handling (GMDH), and ensemble methods including random forest, XGBoost, LightGBM, and random forest of optimal complexity trees (RFOCT).

Results. The CNN and texture-based hybrid classifiers achieved high accuracy, with overall classification accuracies ranging from 83% to 99%. Specifically, ground-glass opacity identification reached up to 100% accuracy, while crazy-paving patterns and consolidations showed slightly lower accuracies (71–95%). The ensemble method RFOCT achieved the highest accuracy (89%) in differentiating acute COVID-19 from Long COVID. Additionally, methods incorporating texture analysis significantly enhanced the accuracy and informativeness of CT-based diagnostics.

Conclusions. Machine learning algorithms, particularly CNNs and advanced texture analysis, demonstrate significant potential in automating the diagnosis of COVID-19-associated lung pathology. These approaches not only increase diagnostic efficiency but also facilitate the detection of subtle pathological changes, crucial for clinical decision-making and patient management during epidemiological crises. Future research should address current limitations related to dataset size, computational complexity, and generalizability to further enhance clinical applicability.

Keywords: COVID-19, Computed Tomography, Medical Image Analysis, Machine Learning, Deep Learning, Convolutional Neural Networks, Texture Analysis, Ensemble Methods.

Innovative Biosystems and Bioengineering

Erythrocyte Distribution by Surface Charge: Biophysical Characteristics of Heterogeneity, Diagnostic Significance and Application in Transfusionology and Biobanking

Results of Machine Learning Applications in the Study of COVID-19 Associated Cardiopulmonary Pathology Using Computed Tomography Data