Ultracentrifugation (UC) has long been considered the “gold standard” for extracellular vesicle (EV) isolation. However, due to its drawbacks such as high cost of an ultracentrifuge and rotors, time-consuming and labor-intensive protocol, low yield considering initial biofluid volume and low throughput, development of new EV isolation approaches is still ongoing. Here we compare three methods for isolating the most studied EV subtype, small extracellular vesicles (sEVs), from human plasma: ultracentrifugation (UC), express asymmetric depth filtration (ExADFi), and anti-CD9 immunoaffinity capture (AS-CD9) with focus on their Raman and proteomic profiles. For all three methods, purity and quality of the sEV isolation were assessed based on the level of contamination of the sEV fraction with major plasma proteins such as albumin and apolipoproteins (APOA1, APOH, APOA4, APOC2, APOC1, and APOC4). UC showed the highest ratio of protein to nanoparticle concentration. AS-CD9 and ExADFi provided comparable to UC purity and levels of non-vesicular contaminants with AS-CD9 requiring minimal time and labor. ExADFi showed characteristics including purity of the sEV samples, yield, and isolation time that is between the UC and AS-CD9 methods. Raman spectroscopy provided more details about characteristics of the isolated sEVs and confirmed differences observed in the proteomic profiles. The findings demonstrate that the AS-CD9 and ExADFi methods could be appropriate substitutes of the classical UC-based isolation method and be chosen depending on the final requirements and use of the purified sEVs such as further functional and biomarker studies.

Human papillomavirus (HPV)-associated cervical lesions remain a significant disease burden and minimally invasive blood-based biomarkers could complement cytology and HPV testing. This study aimed to characterize the proteomic composition of plasma-derived CD9+ small extracellular vesicles (sEVs) across the morphological spectrum of HPV-associated cervical disease, from histologically normal (NILM) through low-grade (LSIL) and high-grade (HSIL) lesions to invasive squamous cell carcinoma (SCC). Plasma samples from 34 women (NILM, LSIL, HSIL, SCC) were pooled per group, and CD9+ sEVs were isolated using an electrochemically controlled immunoaffinity capture method, followed by nanoparticle tracking analysis, transmission electron microscopy, Western blotting, and label-free LC-MS/MS proteomic profiling. The core sEV proteome comprised 258 shared proteins. LSIL showed the most pronounced changes with broad enrichment of complement and coagulation components and acute-phase reactants alongside depletion of immunoglobulin chains and complement C1r-like protein (C1RL). HSIL exhibited few differential proteins, dominated by neutrophil degranulation and retinoid metabolism pathways. SCC demonstrated extensive cargo depletion (22 downregulated proteins) and a nearly sevenfold upregulation of C1RL. Five proteins (including immunoglobulin chains and GPLD1) correlated positively with lesion severity. Pathway analysis consistently implicated platelet activation, lipoprotein remodeling, and insulin-like growth factor signaling. We conclude that plasma CD9+ sEVs carry stage-specific proteomic signatures distinguishing HPV-associated cervical lesions, with C1RL emerging as a candidate biphasic marker warranting further validation.

Ovarian cancer (OC), the third most common gynecologic malignancy, exhibits distinct metabolic alterations that could enable early detection via liquid biopsy. We developed an advanced machine learning pipeline integrating lipidomics (HPLC-MS, positive/negative ion modes) and NMR-based metabolomics to analyze plasma samples from 229 subjects, including 103 serous OC patients, 107 benign cases, and 19 healthy controls. By systematically evaluating feature selection methods and machine learning architectures, we identified optimal biomarker combinations for OC detection. Convolutional Neural Network (CNN) model based on Mann–Whitney-selected features demonstrated strong discriminatory power (81% accuracy) in distinguishing malignant from benign cases, while Extreme Gradient Boosting (XGBoost) combined with Support Vector Machine-Recursive Feature Elimination (SVM-RFE) achieved exceptional performance (96% accuracy) in differentiating benign from control samples. For multiclass classification, XGBoost with Kruskal–Wallis-selected features achieved 77% accuracy, while one-versus-one CNN models utilizing Mann–Whitney-selected features attained 78% accuracy, demonstrating optimal performance among tested approaches. The complementary strengths of deep learning and ensemble methods underscore their potential for tailored diagnostic applications. While clinical implementation requires further standardization, these findings provide both a methodological framework for metabolic biomarker discovery and biological insights into OC pathophysiology, paving the way for integrated multi-omics approaches in gynecologic oncology.

The maternal circulating proteome reflects critical physiological adaptations during pregnancy, yet standardized reference profiles for early gestation are lacking. In this prospective study, we employed targeted liquid chromatography-multiple reaction monitoring-mass spectrometry (LC-MRM-MS) with stable isotope-labeled (SIS) standards to characterize the serum proteome of 83 women with uncomplicated singleton pregnancies between 11+2 and 13+6 weeks' gestation. Robust analysis quantified 115 proteins (83% of targets), with 101 meeting ICH M10 standards. These included 38 FDA-approved, 19 CVD-related, and 25 CLIA-approved biomarkers. We identified 43 proteins significantly associated (p < 0.05) with gestational age, maternal factors (BMI, age, parity, and myomas), and fetal sex. Key findings included identification of 12 proteins significantly associated with trisomy risk (|R| = 0.21-0.45, p < 0.05) and extreme physiological variability in pregnancy zone protein (PZP, 123.9-fold), followed by apolipoprotein (a) (LPA; 9.9-fold) and pregnancy-associated plasma protein A (PAPP-A, 9.3-fold). In contrast, hemopexin (HPX) demonstrated remarkable stability (CV = 8.5%), suggesting its utility as a reference marker. The study successfully implemented multiples of the median (MoM) transformation for clinical standardization of protein profiles, with RobNorm proving particularly effective for batch-effect correction in our dataset. These methodological advances, combined with the establishment of comprehensive pregnancy-specific reference ranges, provide a valuable foundation for future research. The optimized analytical framework and protein signatures identified in this work not only enable the development of next-generation screening approaches but also offer new insights into the molecular adaptations occurring during early pregnancy.

Seminal plasma (SP) constitutes approximately 95% of the ejaculate volume and plays a crucial role in modulating sperm function. The analysis of SP through Raman spectroscopy provides valuable insights into the molecular composition of its constituents, offering the potential for enhanced diagnostic approaches in male fertility assessment. In this pilot study, we obtained in vitro Raman spectra from 70 dried SP samples in the range from 400 to 1900 cm-1 using a laser excitation wavelength of 532 nm. We employed Multivariate Curve Resolution (MCR) analysis and decomposed the Raman spectra into three key components: tyrosine, spermine phosphate hexahydrate, and a protein-rich component. We identified notable differences in the Raman spectra of normal and abnormal samples, particularly in peak intensities at 625, 1440, and 1008 cm-1 providing reliable criteria for differentiation with a specificity of 79% and sensitivity of 95%. The high classification rates achieved in this study not only confirm the efficacy of Raman spectroscopy as a diagnostic tool but also suggest that it could be pivotal in uncovering molecular-level abnormalities that standard semen analysis may overlook. Based on our literature review, this is the first study that highlights the biochemical distinctions between normal and abnormal SP samples in accordance with the criteria established by the World Health Organization (WHO), which includes factors such as sperm count, progressive motility, and normal morphology. Our findings contribute to the growing body of knowledge surrounding male infertility diagnostics and emphasize the necessity for innovative approaches in understanding and addressing reproductive health challenges.

The prevalence of fetal macrosomia is steadily increasing worldwide, reaching up to 20%. Fetal macrosomia complicates pregnancy and delivery. Current prediction strategies are inaccurate, and most patients with fetal macrosomia go into labor with an “unknown status.” The aim of this study was to develop a system for predicting fetal macrosomia based on the lipid profiles of pregnant women’s blood serum. In total, 110 patients were included in this study: 30 patients had gestational diabetes mellitus (GDM) and 80 did not. During the observation, blood samples were collected at three time points: in the first trimester (11–13 weeks of pregnancy), in the second trimester (24–26 weeks), and in the third trimester (30–32 weeks). Lipids were detected by flow injection analysis with mass spectrometry. Lipid profiles of pregnant women were discriminated by orthogonal projection on latent structure discriminant analysis (OPLS-DA) in all three trimesters. The developed OPLS-DA models allowed for the prediction of the occurrence of fetal macrosomia during pregnancy. Three sets of models were developed: models independent of GDM status with a sensitivity of 0.85 and specificity of 0.91, models for patients with positive GDM status with a sensitivity of 0.91 and specificity of 0.96, and models for patients with negative GDM status with a sensitivity of 0.93 and specificity of 0.92. Phosphatidylcholines and sphingomyelins were the most important discriminative features. These lipid groups probably play an important role in the pathogenesis of fetal macrosomia and may serve as laboratory markers of this pregnancy complication.

Hypoxic–ischemic encephalopathy (HIE) is a severe neurological disorder caused by perinatal asphyxia with significant consequences. Early recognition and intervention are crucial, with therapeutic hypothermia (TH) being the primary treatment, but its efficacy depends on early initiation of treatment. Accurately assessing the HIE severity in neonatal care poses challenges, but omics approaches have made significant contribution to understanding its complex pathophysiology. Our study further explores the impact of HIE on the blood metabolome over time and investigated changes associated with hypothermia’s therapeutic effects. Using a rat model of hypoxic–ischemic brain injury, we comprehensively analyzed dried blood spot samples for fat-soluble compounds using HPLC-MS. Our research shows significant changes in the blood metabolome after HIE, with a particularly rapid recovery of lipid metabolism observed. Significant changes in lipid metabolites were observed after 3 h of HIE, including increases in ceramides, carnitines, certain fatty acids, phosphocholines, and phosphoethanolamines, while sphingomyelins and N-acylethanolamines (NAEs) decreased (p < 0.05). Furthermore, NAEs were found to be significant features in the OPLS-DA model for HIE diagnosis, with an area under the curve of 0.812. TH showed a notable association with decreased concentrations of ceramides. Enrichment analysis further corroborated these observations, showing modulation in several key metabolic pathways, including arachidonic acid oxylipin metabolism, eicosanoid metabolism via lipooxygenases, and leukotriene C4 synthesis deficiency. Our study reveals dynamic changes in the blood metabolome after HIE and the therapeutic effects of hypothermia, which improves our understanding of the pathophysiology of HIE and could lead to the development of new rapid diagnostic approaches for neonatal HIE.

The primary therapeutic method for correcting hyperglycemia today is diet therapy. Lipids are not only a source of nutrients but can also act as initiators of adipocyte differentiation in the fetus, which explains the development of fetal macrosomia and future metabolic disorders in children born to mothers with gestational diabetes mellitus (GDM). Changes in the profile of circulating lipids in the maternal bloodstream, depending on adherence to a rational diet among mothers with GDM and the development of fetal macrosomia, is a complex and not fully understood process. The aim of the study was to examine the characteristics of the lipid profile in the blood plasma of pregnant women with GDM in all trimesters of pregnancy based on adherence to diet therapy. The design of the clinical part of the work was carried out within the framework of a 'case-control' study.The study included 110 women, 80 of whom were in the control group, 20 in the group with GDM adhering to the diet, and 10 in the group with GDM not adhering to the diet. The laboratory part was conducted as part of a longitudinal dynamic study with venous blood samples taken at three points: 11-13, 24-26, and 30-32 weeks of pregnancy.A significant impact of diet therapy on the blood lipid composition throughout pregnancy was demonstrated, starting as early as the first trimester. ROC analysis showed high effectiveness of the developed models with an AUC of 0.98 for the "30-32 weeks" model, with sensitivity and specificity of 1 and 0.9, respectively. An association was found between diet, maternal blood lipid composition at 32 weeks of pregnancy, and newborn weight. The direction of changes in the lipid spectrum during the development of macrosomia and diet therapy is diametrically opposed. This confirms at the molecular level that diet therapy can normalize not only carbohydrate but also lipid metabolism in the mother and fetus. Based on the obtained data, it can be suggested that after validation, the developed models could be used to refine the prognosis of macrosomia development based on the lipid profile of blood plasma at different stages of pregnancy.

Hypoxic–ischemic encephalopathy (HIE) is one of the most common causes of childhood disability. Hypothermic therapy is currently the only approved neuroprotective approach. However, early diagnosis of HIE can be challenging, especially in the first hours after birth when the decision to use hypothermic therapy is critical. Distinguishing HIE from other neonatal conditions, such as sepsis, becomes a significant problem in diagnosis. This study explored the utility of a metabolomic-based approach employing the NeoBase 2 MSMS kit to diagnose HIE using dry blood stains in a Rice–Vannucci model of HIE in rats. We evaluated the diagnostic fidelity of this approach in a range between 3 and 6 h after the onset of HIE, including in the context of systemic inflammation and concomitant hypothermic therapy. Discriminant analysis revealed several metabolite patterns associated with HIE. A logistic regression model using glycine levels achieved high diagnostic fidelity with areas under the receiver operating characteristic curve of 0.94 at 3 h and 0.96 at 6 h after the onset of HIE. In addition, orthogonal partial least squares discriminant analysis, which included five metabolites, achieved 100% sensitivity and 80% specificity within 3 h of HIE. These results highlight the significant potential of the NeoBase 2 MSMS kit for the early diagnosis of HIE and could improve patient management and outcomes in this serious illness.

IntroductionImmunometabolism is essential factor of tumor progression, and tumor-associated macrophages are characterized by substantial changes in their metabolic status. In this study for the first time, we applied targeted amino acid LC-MS/MS analysis to compare amino acid metabolism of circulating monocytes isolated from patients with breast, ovarian, lung, and colorectal cancer.MethodsMonocyte metabolomics was analyzed by liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/ MS) analysis of amino acid extracts. The targeted analysis of 26 amino acids was conducted by LCMS/MS on an Agilent 6460 triple quadrupole mass spectrometer equipped with an electrospray ionization source and an Agilent 1260 II liquid chromatograph.ResultsComparison of monocytes of cancer patients with monocytes of healthy control individuals demonstrated that in breast cancer most pronounced changes were identified for tryptophan (AUC = 0.76); for ovarian cancer, aminobutyric acid was significantly elevated (AUC= 1.00); for lung cancer significant changes we indented for citrulline (AUC = 0.70). In order to identify key amino acids that are characteristic for monocytes in specific cancer types, we compared each individual cancer with other 3 types of cancer. We found, that aspartic acid and citrulline are specific for monocytes of patients with colorectal cancer (p<0.001, FC = 1.40 and p=0.003, FC = 1.42 respectively). Citrulline, sarcosine and glutamic acid are ovarian cancer-specific amino acids (p = 0.003, FC = 0.78, p = 0.003, FC = 0.62, p = 0.02, FC = 0.78 respectively). Glutamine, methionine and phenylalanine (p = 0.048, FC = 1.39. p = 0.03, FC = 1.27 and p = 0.02, FC = 1.41) are lung cancer-specific amino acids. Ornithine in monocytes demonstrated strong positive correlation (r = 0.63) with lymph node metastasis incidence in breast cancer patients. Methyl histidine and cysteine in monocytes had strong negative correlation with lymph node metastasis in ovarian cancer patients (r = -0.95 and r = -0.95 respectively). Arginine, citrulline and ornithine have strong negative correlation with tumor size (r = -0.78, citrulline) and lymph node metastasis (r = -0.63 for arginine and r = -0.66 for ornithine).DiscussionThese alterations in monocyte amino acid metabolism can reflect the reaction of systemic innate immunity on the growing tumor. Our data indicate that this metabolic programming is cancer specific and can be inhibiting cancer progression. Cancer-specific differences in citrulline, as molecular link between metabolic pathways and epigenetic programing, provide new option for the development and validation of anti-cancer therapies using inhibitors of enzymes catalyzing citrullination.

Preeclampsia (PE) is a complex and multifaceted obstetric syndrome characterized by several distinct molecular subtypes. It complicates up to 5% of pregnancies and significantly contributes to maternal and newborn morbidity, thereby diminishing the long-term quality of life for affected women. Due to the widespread dissatisfaction with the effectiveness of existing approaches for assessing PE risk, there is a pressing need for ongoing research to identify newer, more accurate predictors. This study aimed to investigate early changes in the maternal serum proteome and associated signaling pathways. The levels of 125 maternal serum proteins at 11-13 weeks of gestation were quantified using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) with the BAK-125 kit. Ten serum proteins emerged as potential early markers for PE: Apolipoprotein M (APOM), Complement C1q subcomponent subunit B (C1QB), Lysozyme (LYZ), Prothrombin (F2), Albumin (ALB), Zinc-alpha-2-glycoprotein (AZGP1), Tenascin-X (TNXB), Alpha-1-antitrypsin (SERPINA1), Attractin (ATRN), and Apolipoprotein A-IV (APOA4). Notably, nine of these proteins have previously been associated with PE in prior research, underscoring the consistency and reliability of our findings. These proteins play key roles in critical molecular processes, including complement and coagulation cascades, platelet activation, and insulin-like growth factor pathways. To improve the early prediction of PE, a highly effective Support Vector Machine (SVM) model was developed, analyzing 19 maternal serum proteins from the first trimester. This model achieved an area under the curve (AUC) of 0.91, with 87% sensitivity and 95% specificity, and a hazard ratio (HR) of 13.5 (4.6-40.8) with p < 0.001. These findings demonstrate that serum protein-based SVM models possess significantly higher predictive power compared to the routine first-trimester screening test, highlighting their superior utility in the early detection and risk stratification of PE.

The primary therapeutic approach for managing hyperglycemia today is diet therapy. Lipids are not only a source of nutrients but also play a role in initiating adipocyte differentiation in the fetus, which may explain the development of fetal macrosomia and future metabolic disorders in children born to mothers with gestational diabetes mellitus (GDM). Alterations in the maternal blood lipid profile, influenced by adherence to a healthy diet in mothers with GDM and the occurrence of fetal macrosomia, represent a complex and not fully understood process. The aim of this study was to examine the characteristics of the blood plasma lipid profile in pregnant women with GDM across all trimesters based on adherence to diet therapy. The clinical part of the study followed a case-control design, including 110 women: 80 in the control group, 20 in a GDM group adhering to the diet, and 10 in a GDM group not adhering to the diet. The laboratory part was conducted as a longitudinal dynamic study, with venous blood samples collected at three time points: 11-13, 24-26, and 30-32 weeks of pregnancy. A significant impact of diet therapy on the composition of blood lipids throughout pregnancy was demonstrated, starting as early as the first trimester. ROC analysis indicated high effectiveness of the models developed, with an AUC of 0.98 for the 30- to 32-week model and sensitivity and specificity values of 1 and 0.9, respectively. An association was found between dietary habits, maternal blood lipid composition at 32 weeks, and newborn weight. The changes in lipid profiles during macrosomia development and under diet therapy were found to be diametrically opposed, confirming at the molecular level that diet therapy can normalize not only carbohydrate metabolism but also lipid metabolism in both the mother and fetus. Based on the data obtained, it is suggested that after further validation, the developed models could be used to improve the prognosis of macrosomia by analyzing blood plasma lipid profiles at various stages of pregnancy.

Endometriosis is a prevalent chronic inflammatory disease characterized by a considerable delay between initial symptoms and diagnosis through surgery. The pressing need for a timely, non-invasive diagnostic solution underscores the focus of current research efforts. This study examines the diagnostic potential of the menstrual blood lipidome. The lipid profile of 39 samples (23 women with endometriosis and 16 patients in a control group) was acquired using reverse-phase high-performance liquid chromatography-mass spectrometry with LipidMatch processing and identification. Profiles were normalized based on total ion counts. Significant differences in lipids were determined using the Mann-Whitney test. Lipids for the diagnostic model, based on logistic regression, were selected using a combination of variance importance projection filters and Akaike information criteria. Levels of ceramides, sphingomyelins, cardiolipins, triacylglycerols, acyl- and alkenyl-phosphatidylethanolamines, and alkenyl-phosphatidylcholines increased, while acyl- and alkyl-phosphatidylcholines decreased in cases of endometriosis. Plasmenylphosphatidylethanolamine PE P-16:0/18:1 and cardiolipin CL 16:0_18:0_22:5_22:6 serve as marker lipids in the diagnostic model, exhibiting a sensitivity of 81% and specificity of 85%. The diagnostic approach based on dried spots of menstrual blood holds promise as an alternative to traditional non-invasive methods for endometriosis screening.

Gipoksicheski-ishemicheskaya entsefalopatiya (GIE) — tyazhelaya neonatal'naya patologiya, vyzyvayushchaya razlichnyye nevrologicheskiye narusheniya, odna iz osnovnykh prichin smertnosti donoshennykh detey. Klyuchevym metodom lecheniya umerennoy i tyazheloy GIE yavlyayetsya terapevticheskaya gipotermiya (TG) — snizheniye temperatury tela novorozhdennykh, kotoraya sushchestvenno umen'shayet risk letal'nogo iskhoda i sposobstvuyet uluchsheniyu dolgosrochnykh iskhodov u detey s GIE. Odnako dlya primeneniya etogo metoda kriticheski vazhna svoyevremennaya diagnostika i opredeleniye tyazhesti zabolevaniya, krome togo on imeyet ryad ogranicheniy i trebovaniy. Aktual'ny izucheniye mekhanizma deystviya TG, poisk osnovnykh metabolicheskikh putey i potentsial'nykh misheney dlya terapii GIE. Tsel'yu raboty bylo s pomoshch'yu metodov VEZHKH-MS issledovat' metabolom sukhikh pyaten krovi kak meneye invazivnoy dlya patsiyenta proby dlya poiska markerov i metabolicheskikh putey, naiboleye aktivnykh pri TG i, veroyatno, oposreduyushchikh yeye polozhitel'nyye effekty. V rezul'tate byli obnaruzheny izmeneniya v klasse fosfoglitserolipidov, chto mozhet svidetel'stvovat' o vazhnoy roli metabolizma endokannabinoidov v zashchite organizma ot GIE. Krome togo, byli izmeneny puti metabolizma ubikhinona, ryada zhirnykh kislot, a takzhe zhelchnykh kislot. Targetnyye kolichestvennyye issledovaniya dannykh metabolitov pozvolyat optimizirovat' diagnostiku GIE i yeye terapiyu na osnove vyyavlennykh potentsial'nykh misheney.

Hypoxic ischemic encephalopathy (HIE) is a severe neonatal condition causing various neurological disorders and one of the main causes of mortality among full-term babies. Therapeutic hypothermia (TH), i.e. the newborn’s body temperature decrease that significantly reduces the risk of fatality and contributes to improvement of long-term outcomes in infants with HIE is the key treatment method for moderate-to-severe HIE. However, the timely diagnosis and disease severity determination are crucial for this method to be used, and the method has a number of limitations and requirements. Assessment of the mechanism underlying the effects of TH and the search for the major metabolic pathways and potential targets for HIE therapy are relevant. The study aimed to assess metabolome of dried blood spots by HPLC-MS, since it is the least invasive to patients test for the search for markers and metabolic pathways most active in TH that are likely to mediate its positive effects. As a result, alterations in the class of phosphoglycerolipids were found, which suggests an important role of endocannabinoid metabolism in protection of the body against HIE. Furthermore, metabolic pathways of ubiquinone, certain fatty acids, and bile acids were altered. The targeted quantitative studies of these metabolites will make it possible to optimize HIE diagnosis and treatment based on the potential targets identified.

Background/Objectives: The development of blood tests for the early detection of individual predisposition to socially significant diseases remains a pressing issue. Methods: In this pilot study, multiple reaction monitoring mass spectrometry (MRM-MS) with a BAK-270 assay was applied for protein concentrations analysis in blood plasma from 21 healthy volunteers of the European cohort. Results: The levels of 138 plasma proteins were reliably and precisely quantified in no less than 50% of samples. The quantified proteins included 66 FDA-approved markers of cardiovascular diseases (CVD), and other potential biomarkers of pathologies such as cancer, diabetes mellitus, and Alzheimer’s disease. The analysis of individual variations of the plasma proteins revealed significant differences between the male (11) and female (10) groups. In total, fifteen proteins had a significantly different concentration in plasma; this included four proteins that exhibited changes greater than ±1.5-fold, three proteins (RBP4, APCS, and TTR) with higher levels in males, and one (SHBG) elevated in females. The obtained results demonstrated considerable agreement with the data collected from 20 samples of a North American cohort, which were analyzed with the similar MRM assay. The most significant differences between the cohorts of the two continents were observed in the level of 42 plasma proteins (including 24 FDA markers), of which 17 proteins showed a ≥1.5-fold change, and included proteins increased in North Americans (APOB, CRTAC1, C1QB, C1QC, C9, CRP, HP, IGHG1, IGKV4-1, SERPING1, RBP4, and AZGP1), as well as those elevated in Europeans (APOF, CD5L, HBG2, SELPLG, and TNA). Conclusions: The results suggest a different contribution of specific (patho)physiological pathways (e.g., immune system and blood coagulation) to the development of socially significant diseases in Europeans and North Americans, and they should be taken into account when refining diagnostic panels.

Biological variance among samples across different cohorts can pose challenges for the long-term validation of developed models. Data-driven normalization methods offer promising tools for mitigating inter-sample biological variance. We applied seven data-driven normalization methods to quantitative metabolome data extracted from rat dried blood spots in the context of the Rice–Vannucci model of hypoxic–ischemic encephalopathy (HIE) in rats. The quality of normalization was assessed through the performance of Orthogonal Partial Least Squares (OPLS) models built on the training datasets; the sensitivity and specificity of these models were calculated by application to validation datasets. PQN, MRN, and VSN demonstrated a higher diagnostic quality of OPLS models than the other methods studied. The OPLS model based on VSN demonstrated superior performance (86% sensitivity and 77% specificity). After VSN, the VIP-identified potential biomarkers notably diverged from those identified using other normalization methods. Glycine consistently emerged as the top marker in six out of seven models, aligning perfectly with our prior research findings. Likewise, alanine exhibited a similar pattern. Notably, VSN uniquely highlighted pathways related to the oxidation of brain fatty acids and purine metabolism. Our findings underscore the widespread utility of VSN in metabolomics, suggesting its potential for use in large-scale and cross-study investigations.

Polycystic ovary syndrome (PCOS) is a complex disorder that impacts both the endocrine and metabolic systems, often resulting in infertility, obesity, insulin resistance, and cardiovascular complications. The aim of this study is to investigate the role of intestinal flora and its metabolites, particularly short-chain fatty acids (SCFAs), in the development of PCOS, and to assess the effects of metformin therapy on these components. SCFA levels in fecal and blood samples from women with PCOS (n=69) and healthy controls (n=18) were analyzed using Gas Chromatography–Mass Spectrometry (GC/MS) for precise measurement. Fecal microbiota were quantitatively detected by real-time polymerase chain reaction (PCR). To assess the efficacy of six months of metformin treatment, changes in the microbiota and SCFAs in the PCOS group (n=69) were also evaluated. The results revealed that women with PCOS exhibited a significant reduction in beneficial bacteria (namely, the C. leptum group and Prevotella spp.) alongside a notable overgrowth of opportunistic microorganisms (C. perfringens, C. difficile, Staphylococcus spp., and Streptococcus spp.). An overproduction of acetic acid (AA, FC=0.47, p<0.05) and valeric acid (VA, FC=0.54, p<0.05) suggests a link between elevated SCFAs and the development of obesity and PCOS. Interestingly, AA in the bloodstream might offer a protective effect against PCOS by ameliorating key symptoms such as high body mass index (r=−0.33, p=0.02), insulin resistance (r=−0.39, p=0.02), and chronic inflammation. Although serum SCFA levels showed non-significant changes following metformin treatment (p>0.05), the normalization of AA in the gut underscores that metformin exerts a more pronounced effect locally within the gastrointestinal tract. Furthermore, the study identified the most effective model for predicting the success of metformin therapy, based on serum concentrations of butyric acid (BA) and VA, achieving a 91% accuracy rate, 100% sensitivity, and 80% specificity. These promising findings highlight the potential for developing targeted interventions and personalized treatments, ultimately improving clinical outcomes for women with PCOS.

Background. The high mortality rate from ovarian cancer is largely due to the asymptomatic course of the disease. The signs of malignant and borderline ovarian tumors are similar to the manifestations of benign lesions, which determines the relevance of developing additional examination procedures and searching for new cancer markers that will distinguish benign and malignant processes. Aim. To build stable blood lipid panels for differentiation of healthy women, patients with benign (BOT) and malignant (MOT) ovarian tumors. Materials and methods. The search for markers for clustering of molecular profiles of blood samples of patients of the Kulakov National Medical Research Center for Obstetrics, Gynecology and Perinatology with BOT (cystadenoma – n=30, endometrioid cyst – n=56, teratoma – n=21), with MOT (borderline tumor – n=28, ovarian cancer of low malignancy – n=16, ovarian cancer of high malignancy – n=59) and volunteers of the group control (n=19) using discriminant analysis of orthogonal projections to hidden structures with an established threshold of importance of the variable VIP1 (OPLS) and the method of projections to hidden structures (PLS-PLS – it is a technology of multidimensional statistical analysis used to reduce the dimension of the feature space with minimal loss of useful information; VIP importance threshold 1) and other statistical tools. Samples’ molecular profile was complete by species, which were identificated by nuclear magnetic resonance and high-perfomance liquid chromatography-mass spectrometry. The analysis of the involvement of compounds that are potential markers of malignant processes in metabolic pathways was carried out. Results. Based on the OPLS and PLS methods, as a result of pairwise and multiclass comparisons, respectively, sets of lipids were identified that can be considered as markers of malignant and benign neoplasms. The overlap of the obtained panels with databases of metabolic pathways was studied, in particular, it was shown that all markers (except glucose) obtained by PLS for differentiation of healthy patients, patients with BOT or with MOT are involved in the transport of small molecules, glucose and lactate are involved in the “TCA Cycle” pathway “Nutrient Utilization and Invasiveness of Ovarian Cancer”. Triglycerides TG 16:0_16:0_18:1, TG 16:0_18:0_18:1, TG 16:0_18:1_18:1, TG 18:0_18:1_18:1, TG 18:0_18:1_18:2 and lactate are involved in the “HIF1A and PPARG regulation of glycolysis” pathway, and The HIF1A and PPARG genes are associated with the development of tumors. Metabolites CE 20:4, TG 16:0_16:0_18:1, TG 16:0_18:0_18:1, TG 16:0_18:1_18:1, TG 18:0_18:1_18:1, TG 18:0_18:1_18:2 are included in the pathways of energy metabolism, and LPC 16:0, PC 16:0_20:3, PC 16:0_20:4 is involved in the path of “Choline metabolism in cancer”. Graphs of the correlation interaction of markers that allow solving classification problems with an unambiguous interpretation of the results are constructed, which makes it possible to assert the prospects of using these panels for further creation of classification models. Conclusion. It is shown that lipids from the developed panels are involved in metabolic pathways associated with the development of tumor diseases and can be used for further validation of diagnostic models based on advanced machine learning methods. The introduction of the achievements of postgenomic research has the potential to increase the diagnostic value of the applied methods of differentiation of benign and malignant proliferative processes, as well as to supplement the available data on the processes of carcinogenesis in the ovaries. Thus, the analysis of the molecular profile of blood by mass spectrometry is a minimally invasive potentially effective diagnostic method.

Fetal arrhythmia develops in 0.1–5% of pregnancies and may cause fetal heart failure and fetal hydrops, thus increasing fetal, neonatal, and infant mortality. The timely initiation of transplacental antiarrhythmic therapy (ART) promotes the conversion of fetal tachycardia to sinus rhythm and the regression of the concomitant non-immune fetal hydrops. The optimal treatment regimen search for the fetus with tachyarrhythmia is still of high value. Polymorphisms of these genes determines the individual features of the drug pharmacokinetics. The aim of this study was to study the pharmacokinetics of transplacental anti-arrhythmic drugs in the fetal therapy of arrhythmias using HPLC-MS/MS, as well as to assess the effect of the multidrug-resistance gene ABCB1 3435C > T polymorphism on the efficacy and maternal/fetal complications of digoxin treatment. The predisposition to a decrease in the bioavailability of the digoxin in patients with a homozygous variant of the CC polymorphism showed a probable association with the development of ART side effects. A pronounced decrease in heart rate in women with the 3435TT allele of the ABCB1 gene was found. The homozygous TT variant in the fetus showed a probable association with an earlier response to ART and rhythm disruptions on the digoxin dosage reduction. high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS) methods for digoxin and sotalol therapeutic drug monitoring in blood plasma, amniotic fluid, and urine were developed. The digoxin and sotalol concentrations were determined in the plasma blood, urine, and amniotic fluid of 30 pregnant women at four time points (from the beginning of the transplacental antiarrhythmic therapy to delivery) and the plasma cord blood of 30 newborns. A high degree of correlation between the level of digoxin and sotalol in maternal and cord blood was found. The ratio of digoxin and sotalol in cord blood to maternal blood was 0.35 (0.27 and 0.46) and 1.0 (0.97 and 1.07), accordingly. The digoxin concentration in the blood of the fetus at the moment of the first rhythm recovery episode, 0.58 (0.46, 0.8) ng/mL, was below the therapeutic interval. This confirms the almost complete transplacental transfer of sotalol and the significant limitation in the case of digoxin. Previously, ABCB1/P-glycoprotein had been shown to limit fetal exposure to drugs. Further studies (including multicenter ones) to clarify the genetic features of the transplacental pharmacokinetics of antiarrhythmic drugs are needed.