Lyudmila Kovalenko

Machine learning (ML) models integrating genetic and clinical data show promise for personalizing antiplatelet therapy after myocardial infarction (MI). This study aimed to develop an ML model using clinical features and CYP2C19 genotype to predict long-term major adverse cardiac events (MACEs) and guide P2Y12 inhibitor selection. In a prospective observational study of 218 patients with MI undergoing percutaneous coronary intervention, with up to 9-year follow-up for MACEs, we trained and evaluated multiple models incorporating clinical, genetic, and angiographic variables. Uplift modeling principles were applied to assess treatment heterogeneity, and feature importance was analyzed using Shapley Additive Explanations (SHAP). The optimal model (CatBoost with SHAP-based feature selection) achieved an area under the receiver operating characteristic curve of 0.721. Discrimination remained stable under bootstrap resampling and across both infarction presentations. Key prognostic predictors included age, comorbidity index, number of significant coronary lesions, P2Y12 inhibitor type, stent type, and CYP2C19 loss-of-function/gain-of-function variants. Notably, CYP2C19 variants were significant MACE predictors, while drug-eluting stents and ticagrelor were associated with lower predicted long-term risk. These findings suggest that an ML framework incorporating CYP2C19 genotype can stratify long-term MACE risk. This data-driven approach points to the prognostic utility of genetic testing and supports the potential role of ticagrelor, particularly in genetically defined high-risk patients with MI, for optimizing secondary prevention.

Background: Pulmonary fibrosis is a chronic and progressive disease characterized by excessive deposition of extracellular matrix and scarring of lung tissue, resulting in a gradual decline in respiratory function. Despite advances in understanding its pathogenesis, effective therapeutic options remain limited. This study aimed to evaluate the antifibrotic effects of concentrated polyphenolic extracts obtained from bilberry (Vaccinium uliginosum), cranberry (Vaccinium oxycoccos), and lingonberry (Vaccinium vitis-idaea) in a bleomycin-induced rat model of pulmonary fibrosis. Methods: Pulmonary fibrosis was induced in rats through the intratracheal administration of bleomycin. Beginning seven days after fibrosis induction, concentrated polyphenolic extracts from the three Vaccinium species were administered orally for 14 consecutive days. Lung histomorphology, inflammatory cell infiltration, collagen composition, and hemorheological parameters were subsequently evaluated. Results: Treatment with the berry polyphenolic extracts reduced the severity of bleomycin-induced pulmonary fibrosis and decreased inflammatory cell infiltration in lung tissue. The extracts also influenced collagen remodeling, promoting a relative predominance of immature type III collagen over mature type I collagen. Furthermore, bleomycin-induced pulmonary fibrosis was accompanied by increased blood viscosity, whereas treatment with the polyphenolic extracts partially normalized the altered hemorheological parameters. Conclusions: Concentrated polyphenolic extracts derived from Vaccinium species demonstrated antifibrotic, anti-inflammatory, and hemorheological effects in experimental pulmonary fibrosis. These findings support further investigation of their underlying molecular mechanisms and potential translational applications studies.

Metabolomic aging clocks—quantitative models that estimate biological age based on small-molecule profiles in biological fluids—have become dynamic tools for assessing individual aging trajectories and predicting the risk of age-related diseases. Although NMR-based approaches have been previously reviewed, the analytical landscape of mass spectrometry (MS)-based metabolomic clocks has not been systematically assessed. This review examines the key components of the MS-based analytical workflow underlying the development of metabolomic aging clocks, including biological matrix selection, chromatographic separation strategies, MS instrumentation, data preprocessing, metabolite annotation, and machine learning-based modeling approaches. Twelve published studies on MS-based metabolomic clocks were identified and systematically compared. These clocks provide predictions of chronological age with mean absolute errors of 3.5–10 years and demonstrate robust associations between metabolomic age acceleration and cardiometabolic risk, frailty, and mortality. Recurrent age-related metabolite classes include tryptophan–kynurenine pathway metabolites, acylcarnitines, sphingolipids, modified nucleosides, and tricarboxylic acid (TCA) cycle intermediates. Currently, liquid chromatography with electrospray ionization (LC-ESI) coupled to high-resolution QTOF or Orbitrap instruments dominates current workflows, with elastic net regression being the most commonly applied modeling strategy. Significant heterogeneity in analytical conditions, incomplete methodological descriptions, and limited cross-study validation remain key obstacles to clinical application. Standardization of pre-analytical protocols, the use of certified reference materials, and harmonized validation frameworks are identified as critical priorities for advancing mass spectrometry-based metabolomic clocks toward clinical translation.

Background/Objectives: Chronic dust-induced lung diseases, including pneumoconiosis and asbestosis, require long-term monitoring and individualized management. Conventional rehabilitation and follow-up often depend on in-person visits and may not provide continuous assessment outside clinical settings. This scoping review mapped evidence on digital technologies used to monitor patients, assess respiratory symptoms and functional status, and support rehabilitation follow-up in chronic dust-induced lung diseases. Methods: The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) and Joanna Briggs Institute methodology. PubMed, Scopus, and Web of Science were searched from inception to 29 June 2026 without language restrictions. Five eligible publications were included. Results: The evidence was grouped into three categories: wearable monitoring of activity and respiratory symptoms, analysis of data from a digital rehabilitation platform, and electronic medical record (EMR) analysis. Wearable sensors showed high performance in recognizing basic activity states and cough events under controlled conditions. Platform- and EMR-based approaches showed potential for using clinical and functional data to support patient stratification and monitoring. However, most studies were limited to early technical or algorithmic validation and did not assess long-term home use, patient adherence, integration into clinical workflows, or clinical and rehabilitation outcomes. Conclusions: Digital technologies may support objective monitoring and rehabilitation follow-up in chronic dust-induced lung diseases, but the evidence base remains small and clinically immature. Prospective studies in real-world settings should evaluate usability, external validity, workflow integration, and clinically meaningful outcomes.

This study investigated the IgA antibodies targeting bovine serum albumin (BSA) in 27 adult celiac disease (CD) patients adhering to a gluten-free diet (GFD), compared to 123 controls (including individuals with autoimmune disorders, those with gastrointestinal cancers, and healthy donors). Serum samples were evaluated using a multiplex assay based on a microarray comprising 66 immobilized antigens, including autoantigens associated with autoimmune diseases, different albumins, cytokines, and inflammatory markers. Elevated IgA-BSA levels were detected in 22% of CD patients versus 3.25% of controls. IgA-BSA did not cross-react with milk proteins like casein, β-lactoglobulin, and γ-globulin, nor with autoantigens and human albumin, ruling out autoimmunity against self-proteins. The observed cross-reactivity with porcine albumin suggests that antibodies target epitopes shared by bovine and porcine albumin. Increased IgA-BSA levels may interfere with immunoassays performed using BSA as a stabilizer, necessitating protein-free buffers to avoid false results when testing CD patients. Elevated IgA-BSA levels may reflect ongoing gut barrier dysfunction in CD patients on a GFD, allowing dietary proteins like BSA to trigger immune responses. This study identifies a novel immune response in CD patients on a GFD, emphasizing the need for tailored diagnostic approaches (BSA-free assays) and further research into the clinical and dietary implications of IgA-BSA elevation.

Background: Tauopathy has been identified as a prevalent causative agent of neurodegenerative diseases, including frontotemporal dementia with parkinsonism-17 (FTDP-17). This rare hereditary neurodegenerative condition is characterised by the manifestation of parkinsonism and behavioural changes. The majority of cases of FTDP-17 are associated with mutations in the MAPT gene, which encodes the tau protein. MAPT mutations lead to disruption of the balance between 3R and 4R tau forms, which causes destabilisation of microtubules and impairment of cellular organelle functions, particularly mitochondrial dysfunction. The development of model systems and tools for studying the molecular, genetic, and biochemical mechanisms underlying FTDP-17 and testing therapies at the cellular level is an urgent necessity. Methods: In this study, we generated transgenic lines of induced pluripotent stem cells (iPSCs) from a patient carrying the pathogenic mutation c.2013T > G (rs63750756, p.N279K) of MAPT and a healthy donor. A doxycycline-controlled transgene of the genetically encoded biosensor MitoTimer was integrated into the AAVS1 locus of these cells. The MitoTimer biosensor allows for lifetime monitoring of the turnover of mitochondria in neuronal cells derived from directed iPSC differentiation. The fact that transcription of the transgene can be induced by doxycycline provides additional possibilities for pulse labelling of newly formed mitochondria. Results: Transgenic iPSC lines provide a unique tool to study the molecular and genetic mechanisms of FTDP-17 caused by the presence of the c.2013T > G (p.N279K) mutation, as well as to test potential drugs in vitro.

Background/Objectives: The aim of this study was to evaluate the avidity of thyroid autoantibodies (Abs) in sera of patients with autoimmune thyroid disease (AITD) and thyroid autoantibody carriers without diagnosed AITD. Methods: A hydrogel microarray-based multiplex assay with the chaotrope destruction stage was developed to measure the avidity of thyroid disease-associated autoantibodies, including those targeting thyroperoxidase (TPO), thyroglobulin (Tg), and other minor antigens. Results: Evaluation of the assay in three independent cohorts of patients, totaling 266 individuals with and without AITD, demonstrated the heterogeneous avidity of autoantibodies to thyroid proteins. For the confirmation study, the median avidity index (AI) for AbTg was 29.9% in healthy autoantibody carriers, 52.6% for AITD patients, and 92.7% for type 1 diabetes (T1D) thyroid autoantibody carriers. The median AI for AbTPO was 39.9% in healthy carriers, 73.4% in AITD patients, 83.2% in T1D thyroid autoantibody carriers, and 98.5% in AITD patients with thyroid neoplasm. In patients with Hashimoto’s thyroiditis and known disease duration, changes in the avidity maturation of AbTPO over time were demonstrated. Conclusions: Longitudinal studies of TPO- and/or Tg-positive healthy individuals (with an interval of 1–2 years between visits) are needed to evaluate the maturation of autoantibody avidity during the asymptomatic phase and to assess the potential of autoantibody avidity as a prognostic marker for disease development.

Osteogenesis imperfecta (OI) is a group of inherited disorders of connective tissue that cause significant deformities and fragility in bones. Most cases of OI are associated with pathogenic variants in collagen type I genes and are characterized by pronounced polymorphisms in clinical manifestations and the absence of clear phenotype–genotype correlation. The objective of this study was to conduct a comprehensive molecular–genetic and clinical analysis to verify the diagnosis of OI in six Russian patients with genetic variants in the COL1A1 and COL1A2 genes. Clinical and laboratory data were obtained from six OI patients who were observed at the Medical Genetics Center in Saint Petersburg from 2016 to 2023. Next-generation sequencing on MGISEQ G400 (MGI, China) was used for DNA analysis. The GATK bioinformatic software (version 4.5.0.0) was used for variant calling and hard filtering. Genetic variants were verified by the direct automatic sequencing of PCR products using the ABI 3500X sequencer. We identified six genetic variants, as follows pathogenic c.3505G>A (p. Gly1169Ser), c.769G>A (p.Gly257Arg), VUS c.4123G>A (p.Ala1375Thr), and c.4114A>T (p.Asn1372Tyr) in COL1A1; and likely pathogenic c.2035G>A (p.Gly679Ser) and c.739-2A>T in COL1A2. In addition, clinical cases are presented due to the presence of the c.4114A>T variant in the COL1A2 gene. Molecular genetics is essential for determining different OI types due to the high similarity across various types of the disease and the failure of unambiguous diagnosis based on clinical manifestations alone. Considering the variable approaches to OI classification, an integrated strategy is required for optimal patient management.