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Research Article
Tissue Regeneration After Thermal Burns Using Chitosan Derivatives: An Experimental Study
Baykulov Azim Kenjayevich*
Issue:
Volume 14, Issue 5, October 2026
Pages:
107-111
Received:
9 February 2026
Accepted:
24 February 2026
Published:
2 September 2026
Abstract: Thermal burns represent a serious clinical challenge due to extensive tissue damage, high susceptibility to microbial contamination, prolonged inflammatory response, and delayed regenerative processes. The development of multifunctional wound-healing agents combining antimicrobial, anti-inflammatory, and regenerative properties remains a priority in experimental and clinical medicine. Chitosan and its derivatives are natural polysaccharide-based biopolymers characterized by biocompatibility, biodegradability, low toxicity, and pronounced biological activity, including antimicrobial, hemostatic, and tissue-regenerative effects. The present experimental study aimed to investigate the regenerative potential and prolonged antimicrobial, osmotic, and adsorptive properties of chitosan derivatives in the treatment of third-degree thermal burns. The experiment was performed on 40 white outbred male rats with standardized full-thickness thermal burns. The animals were randomly divided into four groups: (1) chitosan-furacilin composition, (2) chitosan derivative alone, (3) Levomekol ointment (reference treatment), and (4) physiological saline (control). Treatment was administered topically under standardized conditions. Wound healing dynamics were evaluated by planimetric measurement of wound area and assessment of epithelialization rates on days 3, 7, and 10 post-injury. Quantitative analysis demonstrated significantly accelerated wound contraction and epithelialization in the chitosan-treated groups compared to both control and reference therapy groups (p < 0.05). The chitosan-furacilin composition showed the most pronounced regenerative and antimicrobial effect, indicating a synergistic action. The results confirm that chitosan derivatives enhance reparative processes, reduce inflammatory manifestations, and improve overall wound healing dynamics. These findings suggest that chitosan-based formulations represent promising therapeutic agents for the management of thermal burn injuries and warrant further experimental and clinical investigation.
Abstract: Thermal burns represent a serious clinical challenge due to extensive tissue damage, high susceptibility to microbial contamination, prolonged inflammatory response, and delayed regenerative processes. The development of multifunctional wound-healing agents combining antimicrobial, anti-inflammatory, and regenerative properties remains a priority i...
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Research Article
Laboratory Resistance to Antiplatelet Therapy as Part of Secondary Prevention of Ischemic Stroke in a Young Patient (Clinical Case)
Batenkova Tatiana Yurevna,
Volkova Larisa Ivanovna*
Issue:
Volume 14, Issue 5, October 2026
Pages:
112-118
Received:
8 July 2026
Accepted:
25 August 2026
Published:
4 September 2026
DOI:
10.11648/j.ajcem.20261405.12
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Views:
Abstract: In recent years, the incidence of ischemic strokes in young people worldwide has been increasing, leading to early disability, loss of work capacity, and reduced quality of life. Platelets play an active role in the pathogenesis of ischemic stroke. The main drugs for secondary prevention of ischemic stroke are acetylsalicylic acid (ASA) and clopidogrel (CL). ASA inhibits cyclooxygenase-1 (COX-1), which prevents the production of thromboxane A2 (TXA2), thereby inhibiting platelets, while CL acts by inhibiting ADP, which binds to two protein receptors on platelets (P2Y1 and P2Y12) and leads to platelet aggregation. Effective antiplatelet therapy can significantly reduce the risk of recurrent ischemic stroke. Aim. To identify clinical and genetic factors contributing to the development of laboratory resistance to antiplatelet agents in a patient with a previous ischemic stroke. Materials and methods. The medical history of a patient who had a previous ischemic stroke of unknown origin was studied In Sverdlovsk Regional Clinical Hospital No 1 (SOKB 1). To identify laboratory resistance, we used the optical aggregometry method and a set of genes (ABCB1, CYP2C19*2, CYP2C19*3, CYP2C19*17, ITGA2, ITGB3, PAI-1) that affect the development of high residual platelet reactivity. Results. For the first time, the patient was examined 3 months after the development of an ischemic stroke, against the background of regular ASA intake. Laboratory resistance to this antiplatelet agent was detected using optical aggregometry. Subsequently, against the background of ASA correction, the introduction of clopidogrel (CL) was repeatedly revealed ineffective disaggregation. When analyzing anamnestic data, it was revealed that the development of high residual platelet reactivity could be influenced by the presence of obesity, hypertension, which are present in this patient. Genetic studies have identified mutations in two genes (ABCB1, CYP2C19*2) that may also contribute to the development of laboratory resistance. Conclusions. Conclusions. Effective disaggregation against the background of ASA and CL intake is an important factor in the secondary prevention of ischemic stroke. The study and identification of clinical and genetic risk factors that may affect the development of high residual platelet reactivity remains an important clinical task that requires further study.
Abstract: In recent years, the incidence of ischemic strokes in young people worldwide has been increasing, leading to early disability, loss of work capacity, and reduced quality of life. Platelets play an active role in the pathogenesis of ischemic stroke. The main drugs for secondary prevention of ischemic stroke are acetylsalicylic acid (ASA) and clopido...
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Research Article
Interpretable Machine Learning for the Identification of Key Metabolic Biomarkers Associated with Newly Diagnosed Malignancies
Kermen Ivanovna Bairova*,
Ashot MusaelovichMkrtumyan
Issue:
Volume 14, Issue 5, October 2026
Pages:
119-128
Received:
16 July 2026
Accepted:
24 August 2026
Published:
4 September 2026
DOI:
10.11648/j.ajcem.20261405.13
Downloads:
Views:
Abstract: Objective: To identify the most informative metabolic biomarkers associated with newly diagnosed cancer and to evaluate the potential of interpretable machine learning methods for their identification and patient classification. Materials and methods: This single-center retrospective study included 210 patients: 110 subjects without cancer and 100 patients with newly diagnosed malignancies. Clinical, anthropometric, laboratory, and metabolic variables were analyzed, including body mass index, waist circumference, visceral adiposity index, fasting glucose, immunoreactive insulin, insulin resistance indices, lipid profile, adipokines, and inflammatory markers. After preprocessing and stratified splitting into training, validation, and test sets, a family of Logistic Regression models, Elastic Net, Decision Tree, and CatBoost were used for binary classification. Model interpretation was performed using SHAP analysis, CatBoost feature importance, decision tree structure, SHAP Waterfall plots, and standardized Elastic Net coefficients. Results: CatBoost demonstrated the best classification performance on the test set (AUROC=0.9805; AUPRC=0.9711; Recall=0.9091; Precision=0.9524; F1-score=0.9302). Independent interpretation methods consistently identified the hyperglycemia criterion, fasting glucose, HOMA-IR, oral glucose tolerance test parameters, and the number of metabolic syndrome components as the leading predictors. Distribution analysis confirmed a shift of these biomarkers toward more pronounced carbohydrate metabolism disorders in the oncology group. Conclusion: Interpretable machine learning can support the identification of metabolic biomarkers associated with newly diagnosed cancer. Carbohydrate metabolism and insulin resistance markers were the most informative predictors and may be useful for early oncometabolic risk stratification and future clinical decision-support models.
Abstract: Objective: To identify the most informative metabolic biomarkers associated with newly diagnosed cancer and to evaluate the potential of interpretable machine learning methods for their identification and patient classification. Materials and methods: This single-center retrospective study included 210 patients: 110 subjects without cancer and 100 ...
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