The purpose of this study was to investigate polymorphic variants of the genes FGB (rs1800790), NOS3 (rs2070744) and TMPRSS2 (rs12329760) in patients with SARS-CoV-2 and to determine their role in the COVID-19 severity course against the background of antiviral therapy. Real-time polymerase chain reaction (RT-PCR) was used to genotype the polymorphism of the selected genes. GS-5734 (remdesivir) was prescribed as the basic antiviral drug. Binary logistic regression confirmed a low probability of COVID-19 developing in carriers of the A-allele of the FGB gene. The highest probability of moderate and severe COVID-19 clinical forms developing was found in G-allele carriers (especially the GG genotype) of the FGB gene (rs1800790) and the T-allele of the TMPRSS2 gene (rs12329760). Antiviral drug GS-5734 (remdesivir) administration with anti-inflammatory therapy reduces the TMPRSS2 blood level in moderate COVID-19, IL-6 in severe COVID-19 course, and fibrinogen A- and D-dimers in both groups. The proposed treatment does not significantly affect the concentration of endothelin-1, but a decrease in procalcitonin associated with additional antibacterial use was observed, especially in severe COVID-19.

Objectives: The objective of the study was to investigate the mRNA expression of critical gene patterns, including IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1, and their associations with adverse postoperative outcomes in reconstructive and plastic surgery patients, depending on age. Methods: A total of 95 women participated in this prospective longitudinal cohort study and underwent reconstructive/plastic surgery. The mean age was 35.48 ± 6.61 years (range: 19–57). mRNA expression of IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1 genes was evaluated in peripheral blood leukocytes using a PCR-based method with reverse transcription of cDNA. Results: The risk of postoperative complications significantly increased with elevated expression levels of IL-6 and COL3A1 (7.5-fold, p = 0.007), CCL2 (6.2-fold, p = 0.012), and MAPK1 (25.5-fold, p < 0.001). Increased expression of MAPK8, IL-10, and MMP9 was associated with a 13.2-fold higher risk (p < 0.001). The strongest association was observed for COL1A1 overexpression, which increased complication risk by 58.33-fold (p < 0.001). Risk stratification using the Molecular Complication Risk Index (MCRI), incorporating weighted gene contributions, identified an unfavorable molecular profile predominantly among women aged ≥ 40 years. Receiver operating characteristic analysis confirmed the model’s discriminative ability (AUC = 0.78; 95% CI 0.68–0.87), with an optimal cut-off value of MCRI ≥ 8.5 (sensitivity 76%, specificity 71%, p < 0.05). Conclusions: The transcriptional activity of IL-6, CCL2, IL-10, MAPK1, MAPK8, MMP9, COL1A1, COL3A1, and TGFB1 is associated with postoperative wound healing risk. Women aged over 40 years are at the highest risk of complications. Implementation of the MCRI model may enable early identification of high-risk patients, support targeted preventive strategies, and improve personalized surgical planning.

Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.

Acute otitis media (AOM) is the 2^nd most frequent ENT diagnosis in early childhood. The aim of the study was to evaluate the clinical, anamnestic and micro-social predictors of AOM in children and its association with heat-shock protein gene (HSP70-2, rs1061581) polymorphism. A total of 95 children with AOM (7-18 years old) participated in the study. HSP70-2 (rs1061581) gene genotyping was performed by qPCR method. Risk factors were analyzed. The A-allele of the HSP70-2 gene is a predictor of a more severe AOM course in children and increases its risk and purulent ear discharge almost 2.5 times (p < 0.05), otorrhea - twofold. In preadolescence chronic hypertrophic or vasomotor rhinitis, adenoid vegetations of II-III degrees and non-obstructive diseases of the lower respiratory tract cause an AOM increase risk 3-6 times (p < 0.05). In adolescence, the probability of AOM increases with chronic persistence of hemolytic streptococcus in the oropharynx and broncho-obstructive diseases almost 10 times (p < 0.05). Early daycare attendance before the age of 3 elevates AOM likelihood more than 3 times (p = 0.001). Clinical and social predictors affect the severity of AOM in children depending on age. A-allele (rs1061581) associates with a more severe AOM course in children.

Postoperative wound healing complications present a major challenge in plastic and reconstructive surgery, prolonging recovery and impairing outcomes. Early risk identification is difficult due to complex interactions among clinical, laboratory, and molecular factors. This study developed and evaluated machine-learning (ML) models to predict wound healing outcomes and identify key complication predictors. Utilizing a dataset of 95 women and 76 variables (including hematological, biochemical, coagulation, and gene expression profiles), we evaluated several ML approaches, including Decision Tree, Extra Trees, Gaussian/Bernoulli Naive Bayes, Logistic Regression, and Support Vector Machine. Model performance was assessed via k-fold cross-validation, ROC analysis, and SHAP feature importance. Molecular markers (COL1A1, MMP9, MAPK1, MAPK8, IL10, and CCL2) emerged as the strongest predictors, whereas conventional clinical variables showed limited value. The models achieved high discriminative performance, with validation ROC–AUC values ranging from 0.903 to 0.913. Extra Trees and Gaussian Naive Bayes demonstrated the highest sensitivity for detecting complications (Recall = 0.820 ± 0.238 and 0.807 ± 0.246, respectively). These findings highlight the value of integrating molecular-genetic biomarkers with ML for personalized risk stratification and preventive care in reconstructive surgery.