Innovative Biosystems and Bioengineering

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

Daniil Liadov — 2025-11-24

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

Ievgen Nastenko — 2025-11-27

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.

Comparative Assessment of Cytotoxicity of Aliphatic Amino Carboxylic Compounds as Potential Anticoronavirus Agents

Mykhailo Smetiukh — 2025-12-23

Background. Despite the success in creating vaccines against SARS-CoV-2, the high mutagenicity of coronaviruses, interspecies transmission, and the emergence of new strains require further search for effective antiviral agents. A key step in this process is to evaluate the cytotoxicity of potential compounds to determine their safety and therapeutic potential. Modern IT solutions, such as automated image analysis and artificial intelligence, increase the accuracy and objectivity of assessments.

Objective. To determine the cytotoxicity of compounds with potential anticoronavirus activity and to analyze it using IТ tools.

Methods. The study used the grafting cell line BHK-21 of the gerbil hamster, which was incubated with seven aliphatic amino carbon compounds in six concentrations. Cell viability was determined using the MTT assay. Cell monolayer image processing and an exponential dose-response model were used for automated analysis.

Results. The study revealed a pronounced dose- and time-dependent cytotoxicity of most samples, with a maximum decrease in viability at concentrations above 10 mg/ml. The hormesis effect was recorded at low concentrations (up to 5-10 mg/ml), which may indicate the activation of cellular defense mechanisms. The high correlation between measurements at 492 nm and 550 nm (R² > 0.98) confirmed the reliability of the spectrophotometric data. The exponential model allowed us to approximate the toxicity curves, especially in the middle and high concentration ranges. The built neural network based on image data and MTT test showed the ability to predict cell viability even with a limited amount of training data.

Conclusions. The combination of the MTT assay with automated image analysis provides a comprehensive assessment of cytotoxicity. A dose-dependent decrease in cell viability and morphological changes under the influence of the studied compounds were found. Measurements at 550 nm proved to be more sensitive to early changes in cell metabolism. The use of ІТ algorithms has demonstrated the prospects of an automated approach to the screening of biologically active substances.

Parameter Study and Optimization of an Amperometric Biosensor for Pyruvate Determination Using Mathematical Modeling

Yuriy Karpenko — 2025-12-23

Background. Pyruvate serves as an important diagnostic marker of mitochondrial dysfunctions, lactic acidosis, and certain oncological diseases. Traditional methods for pyruvate analysis have a number of significant limitations: they require complex equipment, are labor-intensive, involve time-consuming sample preparation. Therefore, the development of new, sensitive, and selective methods for determining pyruvate concentration is a highly relevant task.

Objective. The aim of this work was to develop a procedure for the determination and optimization of the parameters of a mathematical model of an amperometric biosensor based on immobilized pyruvate oxidase, employing mathematical modeling of diffusion–reaction processes.

Methods. The biosensor was fabricated using a photopolymer matrix. The analytical characteristics of the biosensor were investigated experimentally. Reaction–diffusion mathematical model was developed to analyze the sensitivity of the biosensor to the substrate (pyruvate) with respect to system parameters. Optimization of these parameters was performed using the gradient descent method.

Results. The study demonstrated that the pyruvate oxidase-based biosensor exhibited a stable amperometric response to pyruvate. Model analysis revealed a significant influence of the substrate diffusion coefficient and the thickness of the bioselective membrane on the biosensor’s sensitivity to pyruvate. The responses of the biosensors showed high signal reproducibility. The theoretically calculated response curves of the biosensor were in good agreement with the experimental data.

Conclusions. The biosensor is characterized by high sensitivity and reproducibility in pyruvate determination. Mathematical modeling enabled rational optimization of the biosensor parameters. The influence of all parameters on the biosensor sensitivity decreased in the following order: from the most influential enzymatic reaction rate constant (k), to the substrate diffusion coefficient (D_S), to the membrane thickness (L), whereas the effect of the product diffusion coefficient (D_P) was found to be minimal.