Vyacheslav Valerievich Ryabov

Fiber‐based bioelectronic devices have emerged as a compelling platform for wearable and implantable health technologies, owing to their one‐dimensional topology, mechanical compliance, textile integrability, and compatibility with minimally invasive deployment. This review systematically examines advances in fiber bioelectronics across four domains: physical sensing, chemical sensing, therapeutic applications, and functional upgrading of established clinical devices including sutures and catheters. For each domain, the governing material architectures, transduction mechanisms, and fabrication strategies are analyzed from conductive network design and encapsulation to coaxial spinning and in‐textile photolithography. And system‐level integration is further discussed, encompassing multimodal sensing arrays, theranostic closed‐loop platforms, and fiber‐scale sensing–computing convergence. Key performance considerations—signal stability under mechanical deformation, long‐term in vivo biocompatibility, multi‐parameter selectivity, and scalable manufacturing—are critically assessed across application contexts ranging from epidermal sweat monitoring to implantable hemodynamic and neurochemical sensing. The field is converging on system‐level reliability in complex physiological environments rather than isolated metric optimization, with functional modification of mature clinical devices offering a near‐term pathway to translation. Challenges and future directions are outlined, with emphasis on closing the gap between laboratory demonstration and clinical deployment.

Until recently, the only transcatheter synthetic aortic prostheses available on the Russian market were those with transapical or transaortic delivery systems. A synthetic prosthesis with a novel transfemoral delivery system has been developed in our country; however, the literature contains no data on the results of its use. Aim: To assess the incidence of in-hospital complications in the early postoperative period following transcatheter aortic valve replacement (TAVR) with the MedInzh prosthesis in patients with degenerative aortic valve stenosis, based on multislice computed tomography (panaortography) data (MSCT), and to identify their potential anatomical predictors. Material and methods. This clinical trial is a prospective, single-center pilot study included patients with confirmed severe aortic stenosis: effective aortic valve orifice area of ≤ 1.0 cm²; mean transvalvular gradient ≥ 40 mmHg. All patients underwent transfemoral valve replacement for correction of severe degenerative aortic stenosis using the synthetic MedInzh prosthesis. All procedures were performed between 2023 and 2025. Results. A total of 30 patients at moderate and high surgical risk were enrolled; median age was 75 (68.5; 77.5) years; 27% were male. The incidence of in-hospital complications was 27% (acute cerebrovascular accident 7%; ventricular arrhythmias 7%; acute common iliac artery thrombosis 7%; acute myocardial ischemia 3%; lymphocele 3%). The odds of complications after TAVR increased when the minimal diameter of the common iliac artery on the access side was ≤ 10.5 mm (OR = 19.2; 95% CI: 1.844–199.9); with the longitudinal diameter of the external iliac artery was ≤ 7.75 mm (OR = 25.5; 95% CI: 2.5–276); and with a transverse diameter was ≤ 8.2 mm (OR = 8; 95% CI: 1.2–55). Conclusions. Among intermediate- and high-risk patients with severe symptomatic aortic stenosis undergoing transfemoral aortic valve replacement with the MedInzh prosthesis, the rate of in-hospital complications was 27%, with vascular complications accounting for 7%. MSCT panaortography results suggest that the diameter of the common iliac arteries, the minimal diameter of the common iliac artery, and the longitudinal and transverse diameters of the external iliac arteries on the access side are potential anatomical predictors associated with the development of vascular complications after TAVR.

Aim . To evaluate the changes in the importance of lean body mass (LBM) in predicting all-cause mortality in primary myocardial infarction (MI) survivors over an 8-year follow-up. Material and methods . This retrospective single-center study included 1917 patients discharged from hospital in 2016-2018,. We assessed mortality after 8,2±0,9 years. LBM was calculated using the James formula. The significance of LBM was assessed in multivariable models that included age, left ventricular ejection fraction, MI type, hospitalization delay >24 hours, and the presence of percutaneous coronary intervention during hospitalization for men and women separately. Results . Men and women differed in age (59 (52; 66) vs 68 (61; 77) years) (pConclusion . LBM is an independent predictor of all-cause mortality in both men and women. The time frame for determining LBM significance differs by sex. The main determinants of all-cause mortality (age and left ventricular ejection fraction) do not have sex differences.

Background: Despite advances in understanding and treating non–ST-elevation acute coronary syndrome (NSTE-ACS), patients continue to experience high rates of adverse outcomes, particularly those with non–ST-segment elevation myocardial infarction, which remains a leading cause of cardiovascular mortality. Existing risk models may not fully reflect contemporary patient populations due to substantial changes in clinical profiles. Developing new machine learning (ML)–based risk calculators may improve the prediction of in-hospital mortality (IHM) at different stages of the diagnostic process, and ultimately improve patient outcomes. Objective: This study aimed to develop predictive models for IHM in patients with NSTE-ACS using ML methods and predictor sets obtained during the diagnostic process. Methods: This retrospective observational study included 1144 patients with NSTE-ACS admitted between 2019 and 2021. IHM occurred in 94 (8.1%) of 1144 patients. Predictive models were developed using multivariable logistic regression, Random Forest, XGBoost (Extreme Gradient Boosting), and CatBoost algorithms. Model performance was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve, calibration metrics, and decision curve analysis. Results: A key feature of the developed models was their applicability at different stages of the diagnostic process, using predictors available at each specific stage. At admission, ML models achieved ROC-AUC values up to 0.93. After incorporating laboratory and echocardiographic data, predictive performance increased to ROC-AUC values of 0.94 to 0.95. The best-performing model demonstrated an ROC-AUC of 0.951 (95% CI 0.946‐0.956), a sensitivity of 0.902 (95% CI 0.889‐0.916), and a specificity of 0.891 (95% CI 0.886‐0.896). The area under the precision-recall curve reached 0.685, and the Brier score was 0.0331, indicating good calibration. Random Forest models demonstrated greater clinical utility than the Global Registry of Acute Coronary Events score ( P <.001). Shapley Additive Explanations analysis identified the most significant predictors of mortality risk, including Killip class of acute heart failure, age, Charlson comorbidity index, creatinine level, and hematocrit level. Conclusions: ML-based models enabled accurate prediction of IHM in patients with NSTE-ACS at different stages of the diagnostic process and may improve risk stratification and clinical decision-making in real-world practice.

Background . Acute decompensated heart failure (ADHF) in patients with post-infarction cardiosclerosis, even with previously performed optimal coronary revascularization, is typically considered a manifestation of ischemic heart disease progression. However, in clinical practice acute decompensation can also be influenced by non-ischemic mechanisms, including myocarditis. Such a combination of conditions appears particularly significant in patients with reduced left ventricular ejection fraction (LVEF) and a history of coronary revascularization in the absence of signs of ongoing myocardial ischemia. Aim : To assess the prevalence of myocarditis and myocardial viral antigen expression in patients hospitalized with ADHF, reduced LVEF, post-infarction myocardial fibrosis, and a history of coronary revascularization. Material and Methods . The study included 26 patients admitted with ADHF. Acute coronary syndrome and other conditions, such as anemia, pneumonia, sepsis, influenza, decompensation of diabetes mellitus, tumors, cachexia, or severe valvular heart stenosis, capable of independently causing HF decompensation were excluded. Median LVEF was 29.5% (IQR 22.0–32.0). All patients underwent transthoracic echocardiography, invasive coronary angiography, and right ventricular endomyocardial biopsy. Histological and immunohistochemical (IHC) analyses were performed to identify myocardial inflammation and viral antigen expression. Endomyocardial biopsy of the right ventricle was performed, with three myocardial samples obtained from each patient, followed by pathohistological and immunohistochemical examination. Results . Myocarditis was diagnosed in 18 patients (69%), including viral myocarditis in 13 (50%), viral-autoimmune myocarditis in 3 (12%), and autoimmune myocarditis in 1 patient (4%). Viral antigen expression in the myocardium was detected in 89% of cases. The frequency of viral antigen detection was as follows: enterovirus – 77%, human herpesvirus type 1 – 8%, type 2 – 8%, type 6 – 50%, Epstein-Barr virus – 23%, cytomegalovirus – 8%; parvovirus B19 and adenovirus were not detected. Viral antigen expression was observed in 94% of patients with myocarditis (except for one case of autoimmune myocarditis) and in 75% of patients without histological signs of myocardial inflammation. Conclusion . In patients with ADHF, reduced LVEF, post-infarction cardiosclerosis, and a history of coronary revascularization, myocarditis was detected in 69% of cases and represented a frequent comorbid condition. In the absence of myocardial ischemia, inflammation of the myocardium may contribute to the development of ADHF, which should be considered during diagnostic evaluation.

Purpose . To evaluate the prognostic value of contrast-enhanced magnetic resonance imaging (MRI) performed early after acute myocardial infarction (AMI) in the development of adverse outcomes during the follow-up period of 12 months. Materials and methods. This observational study consecutively enrolled 88 patients admitted to the cardiology emergency department with a primary diagnosis of AMI, with or without ST-segment elevation. A combined endpoint was formed at the end of the 12-month follow-up period, including 'hard' clinical outcomes. All patients underwent cardiac magnetic resonance imaging (CMR) with contrast on days 4–7 after admission. CMR was used to measure the main volumetric indices of the heart chambers and their function, as well as the tissue characteristics of the myocardium, such as edema percentage, infarct size, and the grey zone of myocardial infarction, expressed as a percentage and in grams of left ventricular myocardial mass (LVMM). Results . The follow-up period was 357 ± 23 days. Endpoint (EP) data were obtained from all 88 patients in the sample (100%). A total of 16 patients (18%) were found to have achieved the combined EP. Myocardial infarct size, as expressed by LV myocardial mass and LV end-diastolic index, is an independent predictor of the development of adverse cardiovascular events in the mid-term follow-up period in patients undergoing acute myocardial infarction. The prognostic model based on clinical data and MRI scans had a higher prognostic value, but this did not reach statistical significance in the sample of patients considered. Meanwhile, the prognostic models based on clinical risk factors for adverse cardiovascular events (ACE) (sex, age and GRACE risk) and those based on CMR data did not differ significantly in terms of informativeness. Conclusion . Cardiac structural and functional indices obtained by CMR are associated with the severity of the disease at the hospital stage in patients after an acute myocardial infarction and can be used as prognostic predictors of adverse cardiovascular events in the medium term. These indices can also be used as prognostic predictors of adverse cardiovascular events in the medium term.

Objective. To develop a multivariate predictive model for the early detection of acute coronary occlusion (ACO) in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) using machine learning methods and a set of predictors available within the first hours of hospitalization. Material and methods. This retrospective observational study included 1,144 patients diagnosed with non-ST-elevation myocardial infarction or unstable angina, hospitalized at a regional vascular center between 2019 and 2021. Invasive coronary angiography was performed in 900 patients, with ACO diagnosed in 64 (7.1%). To identify predictors of ACO, we analyzed 149 clinical, anamnestic, laboratory, and instrumental parameters, available during the initial hours of hospitalization. After selecting predictors, predictive models were developed using machine learning techniques: multivariate logistic regression (MLR), random forest, stochastic gradient boosting (XGBoost), and categorical boosting (CatBoost). Model performance was evaluated based on ROC-AUC, sensitivity (Sens), specificity (Spec), Precision-Recall AUC (PR-AUC), Brier-score, positive predictive value (PPV), negative predictive value (NPV), F1-score, and Accuracy. Results. Independent predictors of ACO included newly diagnosed or new left ventricular regional wall motion abnormalities, erythrocyte sedimentation rate >15.5 mm/h, high-density lipoprotein cholesterol 82 U/L, and signs of ongoing myocardial ischaemia at admission: refractory or recurrent chest pain and/or dyspnea, combined with at least one other established criterion of very high-risk NSTE-ACS according to current clinical guidelines. The CatBoost-based model demonstrated the best prognostic performance (ROC-AUC = 0.841). A highly interpretable prognostic model based on multivariable logistic regression, using the identified risk factors, had performance metrics comparable to the CatBoost model. Conclusion.  The combination of clinical, laboratory, and echocardiographic parameters enables effective prediction of ACO in the NSTE-ACS cohort. The developed machine learning models demonstrate diagnostic accuracy and can form the basis for modifying early risk stratification algorithms and optimizing invasive treatment strategies.

Introduction . Patients with multivessel coronary artery disease who develop acute non-­ST-segment elevation myocardial infarction (NSTEMI) complicated by cardioge­nic shock (CS) are recommended to employ a strategy for urgent blood flow restoration in the infarct-­related coronary artery. To reduce perioperative risk in such ca­ses, percutaneous coronary intervention (PCI) under mechanical circulatory support (MCS) is possible, of which intra-­aortic balloon counterpulsation (IABC) is the most common and accessible. However, given the ambiguity of the existing evidence base, the place and significance of using IABP in the early CS stages remain controversial. Brief description . This case report describes a 74-year-old patient with stab­le multivessel coronary artery disease and a diagnosed abdominal aortic aneurysm without dissection. He was admitted electively for coronary artery bypass grafting. In the preoperative period, despite clinical well-being, he developed NSTEMI, complicated by stage B CS with a significant decrease in left ventricular ejection fraction to 20%, necessitating emergency myocardial revascularization. A multidisciplinary team decided to perform PCI of the infarct-­related coronary artery using IABC (due to the absence of available alternative methods) despite a contraindication — abdominal aortic aneurysm. As a result of PCI, an optimal angiographic outcome (TIMI 3) was achieved. Timely placement of the IABC prior to PCI stabilized systemic hemodynamics and prevented the progression of shock. There were no complications with IABC placement or operation. Discussion. The use of the IABC in this case proved justified, despite conflicting data from modern studies. This case highlights the importance of a personalized approach in the management of patients with severe coronary stenosis in an urgent situation.

Introduction . Cardiac magnetic resonance (CMR) is the gold standard for assessing myocardial remodeling after myocardial infarction. Particular attention is paid to myocardial tissue characteristics assessed using late gadolinium enhancement (LGE). Textural heterogeneity parameters of LGE are a novel quantitative metric that reflects the structural heterogeneity of left ventricular (LV) myocardial tissue changes. Aim: To investigate the association between textural parameters, assessed by quantitative analysis of signal intensity heterogeneity on late gadolinium enhancement CMR, and the development of major adverse cardiovascular events (MACE) in patients with acute myocardial injury. Material and methods . This retrospective study included 108 patients admitted to the emergency cardiology department with a diagnosis of primary ST-elevation or non-ST-elevation myocardial infarction (STEMI or NSTEMI). A composite primary endpoint was established, which included the following clinical outcomes: cardiovascular death, all-cause death, non-fatal myocardial infarction, and non-fatal acute stroke. Inclusion criteria were: 1) performance of contrast-enhanced CMR within 4–7 days of hospitalization; 2) CMR findings consistent with acute ischemic injury of the LV; and 3) satisfactory image quality. CMR criteria for acute ischemic injury included: a high-intensity signal on T2-weighted images (T2WI) with co-localized LGE in a segment(s) demonstrating an ischemic pattern of contrast distribution. Quantitative CMR analysis was performed using the dedicated post-processing software CVI42 (Circle Cardiovascular Imaging, Canada). Myocardial texture analysis was conducted using the 3D Slicer application, version 5.2.2 (The Slicer Community, USA). For the analysis, LGE images were used. From each slice, textural features of signal intensity (SI) heterogeneity were extracted separately for the following regions of interest (ROIs): the LV myocardial injury zone, intact myocardium, and the entire LV (comprising both injured and intact myocardium). Results . The mean age of the patients was 59.56 ± 10.7 years, with 75% (n = 81) being male. STEMI was present in 89.3% of the entire cohort. The follow-up period was 1095 ± 23 days. Follow-up data were obtained for all 108 patients (100% of the sample). Based on the occurrence of the primary endpoint, two groups were formed: the group without cardiovascular events (“–MACE”) and the group that reached the endpoint (“+MACE”). Analysis of LV myocardial tissue characteristics assessed in the LGE phase revealed no significant differences between the study groups for almost all parameters, with the exception of the global LV SI elevation on T2-WI, which was significantly lower in the “+MACE” group. Quantitative analysis of SI heterogeneity across the entire LV using textural features revealed differences in first-order statistics, with higher values of these indices in the “+MACE” group. Patients who experienced a MACE during the follow-up period were characterized by a more asymmetric and complex signal texture, featuring abrupt variations in gray-level intensity, higher gray-level irregularity, shorter lengths of homogeneous areas and run lengths, and a predominance of small heterogeneous areas. Analysis of the intact myocardium in the LV also demonstrated higher heterogeneity and gray-level irregularity, with a high number of small heterogeneous regions. Conclusion . Heterogeneity parameters assessed by CMR reflect the changes occurring in the LV myocardium after MI, are associated with cardiac functional indices, and may be considered prognostic factors for an adverse clinical course. Given the limitations of this study, further research is needed to investigate the relationship between LV tissue characteristics on CMR, entropy, and adverse outcomes after acute myocardial injury.

Aim. To evaluate the influence of epicardial adipose tissue (EAT) volume and density, based on computed tomography (CT) data, on the severity of myocardial injury assessed using cardiac biomarkers. Material and methods. The study included patients with newly diagnosed acute myocardial infarction (MI). All patients underwent invasive coronary angiography. Two following groups were formed: 1) MI with obstructive coronary artery disease (MICAD) (stenosis ≥50%); 2) MI with non-obstructive coronary arteries (MINOCA) (stenosis Results . The study included 31 patients as follows: 21 with MICAD (age 62 (56; 68) years) and 10 with MINOCA (age 68 (57; 79) years). MICAD patients had higher levels of cardiac biomarkers. Significant differences in EAT volume were found between MICAD and MINOCA patients (pConclusion. In the acute period after acute MI, in patients with MINOCA, the EAT phenotype, characterized by a smaller volume and higher density, is associated with greater myocardial damage according to biomarker assessment, while in MICAD, the relationship between EAT properties and cardiac biomarkers is predominantly absent.