Ekaterina Chernevskaya

Introduction . Patients with severe brain injury (stroke and traumatic brain injury) exhibit pronounced hypercatabolism and rapid development of protein–energy malnutrition, highlighting the need for optimized nutritional support. However, comparative data on the effectiveness of metabolically targeted enteral formulas versus standard formulas in this population remain scarce, underscoring the relevance of this study. Objective . To evaluate the impact of a metabolically targeted enteral formula on clinical outcomes and nutritional status dynamics compared with a standard enteral formula in critically ill patients with severe brain injury. Materials and methods. This singlecenter prospective interventional study compared a metabolically targeted enteral formula with a retrospective control group drawn from the RICD v. 3.0 database. The intervention group received the metabolically targeted formula for 20 days, while the control group received standard enteral formulas. Clinical characteristics, laboratory parameters, the Prognostic Nutritional Index (PNI), and hospitalization outcomes were assessed. Results . After selection, the retrospective cohort included 225 patients receiving standard formulas (210 received milk proteinbased formulas, 15 received milk and meat proteinbased formulas). The prospective group comprised 16 patients. Changes in PNI and major laboratory parameters did not differ significantly between groups. However, the metabolically targeted group had a significantly shorter ICU stay (23 [22–28] vs. 35 [26–49] days, p=0.005) and a lower requirement for mechanical ventilation (62.5 % vs. 88.9 %, p=0.009). Conclusions . Use of the metabolically targeted enteral formula in patients with severe cerebral pathology was associated with reduced ICU length of stay and decreased need for mechanical ventilation. These findings warrant further evaluation in other patient populations and age groups.

The aim of this study is to determine the prevalence of antibiotic resistance genetic markers in pathogens causing lower respiratory tract infections in patients with chronic critical illness. Materials and Methods . We analyzed genetic markers of resistance to antimicrobial agents in pathogens causing lower respiratory tract infections in 251 patients in a chronic critical condition who were admitted to the intensive care unit (ICU) in 2025. Pathogen identification and gene detection were performed using an automated analyzer and the real-time polymerase chain reaction (qPCR) method. Results . The predominance of three pathogens — Klebsiella pneumoniae , Acinetobacter baumannii , and Pseudomonas aeruginosa — was confirmed; these accounted for more than 40% of the identified infections. It was found that among the identified genetic determinants of resistance, the shv gene (58.35%) was predominant; this gene is responsible for the intrinsic resistance of K. pneumoniae and the acquired resistance of E. coli . The ctx-m-1 gene ranked second in prevalence at 55.80 %, and the tem gene ranked third at 50.29 %. Carbapenemase genes accounted for a significant proportion: ndm genes were detected in 41.45 % of samples, and genes of the oxa family were widely distributed — oxa-48-like was detected in 40.47 % of cases, oxa-40-like in 38.51 %, oxa-51-like in 34.38 %, and oxa-23-like in 18.86 %. Conclusion. Among patients with chronic critical illness admitted to the ICU, Gram-negative bacteria — including Klebsiella pneumoniae , Acinetobacter baumannii , and Pseudomonas aeruginosa — were the predominant pathogens causing lower respiratory tract infections in more than 40 % of cases. Two or more genetic markers of resistance were detected in 80.55% of the samples. The oxa- 51-like gene was detected in all A. baumannii strains. Carbapenemase genes ( ndm , oxa , kpc ) and beta-lactamase genes ( shv , ctx-m-1 , tem ) were prevalent.

This article presents a clinical observation of the successful treatment of a 3-year-old child with severe combined fall-related trauma (fall from the 11th floor), complicated by an extensive (30 × 20 cm) contaminated trunk wound defect with damage to internal organs. The key factor that determined the success in treating such a large wound, with documented microbial contamination but without secondary infectious complications, was the combined use of low-temperature argon plasma, vacuum therapy. The use of these methods at all stages of treatment, from initial surgical debridement to preparation for plastic reconstruction, provided a pronounced bactericidal effect, including control of nosocomial bacteria, destruction of bacterial biofilms, and rapid wound cleansing, which created optimal conditions for plastic surgery within a short time frame. For objective infection control, microbiological and molecular-genetic monitoring (using polymerase chain reaction) of the wound surface was actively employed. Serum was also examined for microbial metabolites and sepsis biomarkers, which made it possible to dynamically assess the response to therapy and to perform plastic procedures at an early stage, without purulent-inflammatory complications and without excessive escalation of antibiotic therapy. Such a rapid transition of an extensive wound process to the regeneration phase demonstrates the high effectiveness of the combined use of modern physical methods of wound treatment and laboratory monitoring techniques. This approach made it possible to achieve a favorable outcome in a child in a life-threatening condition, to avoid septic complications, and to discharge the patient from the hospital in a short period with a good result.

Pancreatic cancer is among the most aggressive malignancies, and late diagnosis remains a key challenge. For a systematic review of pancreatic cancer diagnosis and prognosis, Scopus and Web of Science databases were used for the period from 2016 to 2026. The search query included the following keywords and their combinations: pancreatic cancer, diagnosis, early detection, prognosis, biomarkers, metabolomic profiling, CA19-9, microbiome, metagenomic changes, circulating tumor DNA, genomic analysis. Inclusion criteria included only articles published in English. Exclusion criteria included case reports and studies that did not examine pancreatic cancer. Our analysis demonstrates that integrating multi-omics data, particularly combining traditional CA19-9 with circulating tumor DNA (ctDNA) and metabolomic profiles (lipids, amino acids, carbohydrates), significantly improves diagnostic accuracy. Microbiome composition and genomic alterations further refine risk stratification and prognostic assessment. The synergistic use of these biomarkers may facilitate the development of screening, early diagnosis, risk stratification, and treatment optimization. However, the introduction of new diagnostic approaches into clinical practice requires additional verification, standardization and prospective clinical studies.

Protein-energy malnutrition (PEM) remains one of the most pressing issues in patients with severe traumatic brain injury in intensive care units (ICUs), as it is highly prevalent, difficult to manage, and its causes are not fully understood. The aim of the study was to assess the influence of gut microbial imbalance and gastrointestinal motility on the development of malnutrition in patients in a chronic critical state and severe brain damage. Materials and methods . A single-center prospective observational study included 31 patients (median age 52 years; 68% males) aged 18–74 years with traumatic brain injury or stroke requiring ICU stay for more than 5 days and enteral tube feeding. Patients with diabetes mellitus, acute multiple organ failure (MOF), shock, implanted devices, or tracheoesophageal fistula were excluded. Nutritional status was assessed at baseline and on Day 20 using the Russian malnutrition scale and the Global Leadership Initiative on Malnutrition (GLIM) criteria. Additionally, clinical outcomes, anthropometric data, gastrointestinal biomarkers, gut microbiota composition, electrogastroenterography (EGEG) and functional scales parameters were recorded. Results . Moderate and severe malnutrition according to the GLIM criteria was found at baseline in 29.1% of patients, and in 27.7% of patients on Day 20 (p = 0.9), while according to the Russian scale these numbers were 61.3% and 78.6%, respectively (p = 0.8). Dynamics of clinical scales, functional indicators, and gastrointestinal biomarkers during the follow-up revealed no clinically significant changes. Significant and persistent deviations 2 from reference values in gut microbiota composition (decrease in the content of E. coli , p = 0.026; increase in Enterobacter spp., p = 0.020) and EGEG parameters were recorded at both evaluation time-points. Identified PEM was also associated with impaired gastrointestinal motility. Conclusion . The data indicate a statistically significant relationship between PEM, changes in the gut microbiota and gastrointestinal motility, which confirms the important role of these factors in PEM pathogenesis in patients with severe brain damage in a chronic critical state.

Klebsiella pneumoniae (K. pneumoniae) is a major nosocomial pathogen with increasing antibiotic resistance. Treatment failures and high mortality rates in sepsis caused by K. pneumoniae are associated with difficulties in choosing an adequate antibacterial therapy in the presence of resistance to all available antibiotics, based on the results of susceptibility tests. This study aimed to identify “weak points” in the metabolism of K. pneumoniae, to be able to use these features in the future. Ten nosocomial K. pneumoniae strains were incubated with fourteen broad-spectrum antibiotics representing major drug classes. Aromatic metabolites were analyzed using gas chromatography–mass spectrometry after 24 h exposure. Phenyllactic acid (PhLA), comprising 86% of detected phenylcarboxylic acids, served as the metabolic activity marker. Antibiotics demonstrated multidirectional effects on aromatic compound metabolism. Doxycycline, nitrofurantoin, rifampicin, and tigecycline significantly suppressed metabolic activity, confirmed by decreased PhLA levels. Conversely, meropenem, cephalosporins (ceftriaxone, cefepime, cefotaxime, and ceftazidime), ciprofloxacin, and amikacin stimulated PhLA production, suggesting that bacterial metabolic activity was maintained despite the presence of antibiotics. PhLA is a promising biomarker for quantifying K. pneumoniae’s metabolic response to antibiotics. This potentially introduces a novel approach for future investigations into resistance mechanisms and has the potential to increase the effectiveness of therapies for multidrug-resistant K. pneumoniae infections by providing an additional analytical tool to traditional susceptibility testing methodologies.

Assessing gut microbiota disturbances for subsequent modulation remains a challenge. This study aims to evaluate the safety and efficacy of a microbiota-oriented strategy in treating patients with chronic critical illness (CCI). This single-center prospective study included chronically critically ill patients, stratified into three groups by severity of microbiota dysfunction. Three different microbiota modulation regimens including metabiotics, enteral, and anaerobic-safe systemic antibiotics were applied subsequently. Forty-three patients with chronic critical illness were included. Mild microbiota dysfunction was present in 49% patients, moderate in 19% and severe in 32%. Monitoring of biomarkers for 14 days confirmed the safety of reducing the pharmacological load in mild to moderate microbiota dysfunction. The microbiota-oriented strategy demonstrated improvements in neurological condition, a decrease in inflammation, and normalization of several hematological and biochemical parameters, without contributing to the activation of opportunistic microorganisms in the intestinal microbiota. The incidence of pneumonia in patients with CCI was reduced significantly during the 28-day observation period. The results of the pilot study suggest the potential benefits of a microbiota-oriented strategy in preventing nosocomial pneumonia in CCI patients.

Background: In the treatment of oncological diseases in children, the search for opportunities for the earlier detection of complications to improve treatment results is very important. Metabolomic studies are actively conducted to stratify different groups of patients in order to identify the most promising markers. Methods: Three groups of patients participated in this study: healthy children as a control group (n = 18), children with various malignant oncological diseases (leukemia, lymphoma, nephroblastoma, ependymoma, etc.) as patients (n = 40) without complications, and patients (n = 31) with complications (inflammatory and infectious). The mitochondrial metabolites (succinic and fumaric acids); biomarkers related to inflammation such as C-reactive protein (CRP), procalcitonin (PCT), and presepsin (PSP); and sepsis-associated aromatic metabolites, such as phenyllactic (PhLA), hydroxyphenyllactic (p-HPhLA), and hydroxyphenylacetic acids (p-HPhAA), were identified. Results: It was found that children with malignant oncological diseases had profound metabolic dysfunction compared to healthy children, regardless of the presence of systemic inflammatory response syndrome (SIRS) or sepsis. The prognostic ability of procalcitonin and presepsin for detecting sepsis was high: AUROC = 0.875, cut-off value (Youden index) = 0.913 ng/mL, and AUROC = 0.774, with cut-off value (Youden index) of 526 pg/mL, respectively. Conclusions: A significant increase in aromatic microbial metabolites and biomarkers in non-survivor patients that is registered already in the first days of the development of complications indicates the appropriateness of assessing metabolic dysfunction for its timely targeted correction.

Background: Early diagnosis of post-operative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. The purpose of this study was to determine whether an integrated approach based on clinical data, along with metabolites and biomarkers, had greater predictive value than the models built on fewer data in the early diagnosis of post-operative complications after cardiac surgery. Methods: The study included patients (n = 62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass) with (n = 26) or without (n = 36) post-operative complications. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients’ blood samples were collected before and on the first day after surgery to determine biomarkers and metabolites. Results: Multivariate PLS-DA models, predicting the presence or absence of post-operative complications, were built using clinical data, concentrations of metabolites and biomarkers, and the entire data set (ROC-AUC = 0.80, 0.71, and 0.85, respectively). For comparison, we built univariate models using the EuroScore2 and SOFA scales, concentrations of lactate, the dynamic changes of 4-hydroxyphenyllactic acid, and the sum of three sepsis-associated metabolites (ROC-AUC = 0.54, 0.79, 0.62, 0.58, and 0.70, respectively). Conclusions: The proposed complex model using the entire dataset had the best characteristics, which confirms the expediency of searching for new predictive models based on a variety of factors.

Background: Early diagnosis of postoperative complications is an urgent task, allowing timely prescribing of appropriate therapy and reducing the cost of patient treatment. Prognostic models based on various data could help in the early diagnosis of postoperative complications after cardiac surgery. Objectives: The purpose of the study was to determine whether an integrated approach based on clinical data along with metabolites and biomarkers had greater predictive value than the models built on less data. Methods: The study included patients (n=62) admitted for planned cardiac surgery (coronary artery bypass grafting with cardiopulmonary bypass), who were retrospectively divided into two groups depending on the presence (n=26) or absence (n=36) of postoperative complications of all types. Clinical and laboratory data on the first day after surgery were analyzed. Additionally, patients' blood samples were collected before and on the first day after surgery to determine biomarkers (interleukin-6, procalcitonin, NT-proBNP, high-sensitivity troponin T) and metabolites (8 aromatic and 2 dicarboxylic acids were detected using gas chromatography-mass spectrometry). Results: Patients before surgery, compared with healthy donors, differed in the concentration of metabolites (p < 0.05), and had concentrations of a number of biomarkers exceeding the corresponding reference values. Multivariate PLS-DA models, predicting the presence or absence of postoperative complications, were built using clinical data (ROC-AUC=0.71), concentrations of metabolites and biomarkers (ROC-AUC=0.60), and the entire data set (ROC-AUC=0.75). For comparison we built univariate models using the EuroScore2 (ROC-AUC=0.59) and SOFA (ROC-AUC=0.76) scales; concentrations of lactate (ROC-AUC=0.71), the dynamic changes of 4-hydroxyphenyllactic acid (ROC-AUC=0.69), and the sum of three sepsis-associated metabolites (ROC-AUC=0.70). The proposed complex model using entire data set was comparable to the SOFA scale, which confirms the expediency of searching for new predictive models based on a variety of factors.