Portable point-of-care surface enhanced Raman scattering spectroscopy for the quantification of glutathione in whole blood microsamples.

Glutathione (GSH) is a non-protein tripeptide thiol that plays a prominent role in oxidative stress defense. GSH concentration is particularly critical in the neonatal period, especially for premature newborns that face increased susceptibility to oxidative stress. Monitoring GSH levels provides valuable insights into newborn health, helping to tailor care to their specific needs. The aim of this study was the development of a sensor specifically targeted for its use in neonatology, enabling GSH determination in only 2 µL of whole blood. The newly developed sensing system simplifies sample processing, addressing a critical need in clinical applications. Unlike current methods that demand fast pre-processing of relatively large sample volumes, expensive equipment, and skilled personnel, the developed approach streamlines the analytical process. By using 2 µL of whole blood, a single syringe filter for sample treatment, a deuterated internal standard (IS) for signal normalization, and Surface Enhanced Raman Scattering (SERS) spectroscopy with a silver colloid substrate for GSH detection, the set-up's characteristics are compatible with point-of-care applications. The analytical procedure was validated and applied to diverse populations including healthy adults (N = 63) and newborns (N = 35), yielding GSH concentration values ranging from 0.6 to 1.8 and 0.8-2.1 mM, respectively. This new optical sensor offers a quick and cost-effective solution to support the assessment of GSH levels in newborns that can greatly benefit not only neonatal care, but also the study of adult populations for health monitoring.

Searching molecular biomarkers correlating with BSID-III at 24 months in infants with neonatal hypoxic-ischemic encephalopathy.

An early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. The objectives were (i) to analyze the characteristics of miRNA expression and metabolic patterns of neonates with NE and (ii) to assess their predictive performance for neurodevelopmental outcomes. Plasma samples from moderate/severe NE patients (N = 92) of the HYPOTOP study were collected before, during, and after therapeutic hypothermia (TH) and compared to a control group (healthy term infants). The expression of miRNAs and concentrations of metabolites (hypoxia-related and energy, steroid, and tryptophan metabolisms) were analyzed. Neurodevelopmental outcomes were evaluated at 24 months postnatal age using Bayley Scales of Infant Development, ed. III, BSID-III. Differences in miRNA and metabolic profiles were found between NE vs. control infants, abnormal (i.e., mildly and moderately abnormal and severe) vs. normal, and severe vs. non-severe (i.e., normal and mildly and moderately abnormal) BSID-III. 4-Androstene-3,17-dione, testosterone, betaine, xanthine, and lactate were suitable for BSID-III outcome prediction (receiver operating characteristic areas under the curve (AUCs) ≥ 0.6), as well as 68 miRNAs (AUCs of 0.5-0.9). Significant partial correlations of xanthine and betaine levels and the expression of several miRNAs with BSID-III sub-scales were found. Conclusion: We have identified metabolites/miRNAs that might be useful to support the prediction of middle-term neurodevelopmental outcomes of NE. What is known and what is new: • The early prediction of outcomes of neonatal hypoxic-ischemic encephalopathy (NE) is of key importance in reducing neonatal mortality and morbidity. • Alterations of the metabolome and miRNAs had been observed in NE. • We performed miRNA sequencing and quantified selected metabolites (i.e., lactate, pyruvate, ketone bodies, Krebs cycle intermediates, tryptophan pathway, hypoxia-related metabolites, and steroids) by GC- and LC-MS. • Specific miRNAs and metabolites that allow prediction of middle-term neurodevelopmental outcomes of newborns with NE undergoing hypothermia treatment were identified.

Oxylipin profile of human milk and human milk-derived extracellular vesicles.

BACKGROUND: Small Extracellular Vesicles (sEVs) are nano-sized vesicles that are present in all biofluids including human milk (HM) playing a crucial role in cell-to-cell communication and the stimulation of the neonatal immune system. Oxylipins, which are bioactive lipids formed from polyunsaturated fatty acids, have gained considerable attention due to their potential role in mitigating disease progression and modulating the inflammatory status of breastfed infants. This study aims at an in-depth characterization of the oxylipin profiles of HM and, for the first time, of HM-derived sEVs (HMEVs) employing an ad-hoc developed and validated ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) method. RESULTS: The UPLC-MS/MS method covered a panel of 13 oxylipins for quantitation and 93 oxylipins for semi-quantitation. In 200 µL of HM and HMEV isolates of 15 individuals, 42 out of 106 oxylipins were detected in either HM or HMEVs, with 38 oxylipins being detected in both matrices. Oxylipins presented distinct profiles in HM and HMEVs, suggesting specific mechanisms responsible for the encapsulation of target molecules in HMEVs. Ten and eight oxylipins were quantified with ranges between 0.03 - 73 nM and 0.30 pM-0.07 nM in HM and HMEVs, respectively. The most abundant oxylipins found in HMEVs were docosahexaenoic acid derivatives (17-HDHA and 14-HDHA) with known anti-inflammatory properties, and linoleic acid derivatives (9-10-DiHOME and 12,13-DiHOME) in HM samples. SIGNIFICANCE AND NOVELTY: This is the first time a selective, relative enrichment of anti-inflammatory oxylipins in HMEVs has been described. Future studies will focus on the anti-inflammatory and pro-healing capacity of oxylipins encapsulated in HMEVs, with potential clinical applications in the field of preterm infant care, specifically the prevention of severe intestinal complications including necrotizing enterocolitis.

Predicting Injuries in Elite Female Football Players With Global-Positioning-System and Multiomics Data.

PURPOSE: Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players. METHODS: We designed a prospective cohort study over 2 seasons (2019-20 and 2021-22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload. RESULTS: Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P < .05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10-4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index: .74). CONCLUSIONS: Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model.

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support.

BACKGROUND: Serum transaminases, alkaline phosphatase and bilirubin are common parameters used for DILI diagnosis, classification, and prognosis. However, the relevance of clinical examination, histopathology and drug chemical properties have not been fully investigated. As cholestasis is a frequent and complex DILI manifestation, our goal was to investigate the relevance of clinical features and drug properties to stratify drug-induced cholestasis (DIC) patients, and to develop a prognosis model to identify patients at risk and high-concern drugs. METHODS: DIC-related articles were searched by keywords and Boolean operators in seven databases. Relevant articles were uploaded onto Sysrev, a machine-learning based platform for article review and data extraction. Demographic, clinical, biochemical, and liver histopathological data were collected. Drug properties were obtained from databases or QSAR modelling. Statistical analyses and logistic regressions were performed. RESULTS: Data from 432 DIC patients associated with 52 drugs were collected. Fibrosis strongly associated with fatality, whereas canalicular paucity and ALP associated with chronicity. Drugs causing cholestasis clustered in three major groups. The pure cholestatic pattern divided into two subphenotypes with differences in prognosis, canalicular paucity, fibrosis, ALP and bilirubin. A predictive model of DIC outcome based on non-invasive parameters and drug properties was developed. Results demonstrate that physicochemical (pKa-a) and pharmacokinetic (bioavailability, CYP2C9) attributes impinged on the DIC phenotype and allowed the identification of high-concern drugs. CONCLUSIONS: We identified novel associations among DIC manifestations and disclosed novel DIC subphenotypes with specific clinical and chemical traits. The developed predictive DIC outcome model could facilitate DIC prognosis in clinical practice and drug categorization.

Exploring Individual Variability in Drug-Induced Liver Injury (DILI) Responses through Metabolomic Analysis.

Drug-induced liver injury (DILI) is a serious adverse hepatic event presenting diagnostic and prognostic challenges. The clinical categorization of DILI into hepatocellular, cholestatic, or mixed phenotype is based on serum alanine aminotransferase (ALT) and alkaline phosphatase (ALP) values; however, this classification may not capture the full spectrum of DILI subtypes. With this aim, we explored the utility of assessing changes in the plasma metabolomic profiles of 79 DILI patients assessed by the RUCAM (Roussel Uclaf Causality Assessment Method) score to better characterize this condition and compare results obtained with the standard clinical characterization. Through the identification of various metabolites in the plasma (including free and conjugated bile acids and glycerophospholipids), and the integration of this information into predictive models, we were able to evaluate the extent of the hepatocellular or cholestatic phenotype and to assign a numeric value with the contribution of each specific DILI sub-phenotype into the patient's general condition. Additionally, our results showed that metabolomic analysis enabled the monitoring of DILI variability responses to the same drug, the transitions between sub-phenotypes during disease progression, and identified a spectrum of residual DILI metabolic features, which can be overlooked using standard clinical diagnosis during patient follow-up.

Comparing the direct assessment of steatosis in liver explants with mid- and near-infrared vibrational spectroscopy, prior to organ transplantation.

A fast and accurate assessment of liver steatosis is crucial during liver transplantation surgery as it can negatively impact its success. Recent research has shown that near-infrared (NIR) and attenuated total reflectance-Fourier transform mid-infrared (ATR-FTIR) spectroscopy could be used as real-time quantitative tools to assess steatosis during abdominal surgery. Here, in the frame of a clinical study, we explore the performance of NIR and ATR-FTIR spectroscopy for the direct assessment of steatosis in liver tissues. Results show that both NIR and ATR-FTIR spectroscopy are able to quantify the % of steatosis with cross-validation errors of 1.4 and 1.6%, respectively. Furthermore, the two portable instruments used both provided results within seconds and can be placed inside an operating room evidencing the potential of IR spectroscopy for initial characterization of grafts in liver transplantation surgery. We also evaluated the complementarity of the spectral ranges through correlation spectroscopy.

Gut Microbiota and Plasma Bile Acids Associated with Non-Alcoholic Fatty Liver Disease Resolution in Bariatric Surgery Patients.

Bariatric surgery (BS) has several benefits, including resolution of non-alcoholic fatty liver disease (NAFLD) in many patients. However, a significant percentage of patients do not experience improvement in fatty liver after BS, and more than 10% develop new or worsening NAFLD features. Therefore, a question that remains unanswered is why some patients experience resolved NAFLD after BS and others do not. In this study, we investigated the fecal microbiota and plasma bile acids associated with NAFLD resolution in twelve morbidly obese patients undergoing BS, of whom six resolved their steatosis one year after surgery and another six did not. Results indicate that the hallmark of the gut microbiota in responder patients is a greater abundance of Bacteroides, Akkermansia, and several species of the Clostridia class (genera: Blautia, Faecalibacterium, Roseburia, Butyricicoccusa, and Clostridium), along with a decreased abundance of Actinomycetes/Bifidobacterium and Faecalicatena. NAFLD resolution was also associated with a sustained increase in primary bile acids (particularly non-conjugated), which likely results from a reduction in bacterial gut species capable of generating secondary bile acids. We conclude that there are specific changes in gut microbiota and plasma bile acids that could contribute to resolving NAFLD in BS patients. The knowledge acquired can help to design interventions with prebiotics and/or probiotics to promote a gut microbiome that favors NAFLD resolution.

Fast profiling of primary, secondary, conjugated, and sulfated bile acids in human urine and murine feces samples.

Bile acids (BAs) are a complex class of metabolites that have been described as specific biomarkers of gut microbiota activity. The development of analytical methods allowing the quantification of an ample spectrum of BAs in different biological matrices is needed to enable a wider implementation of BAs as complementary measures in studies investigating the functional role of the gut microbiota. This work presents results from the validation of a targeted ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method for the determination of 28 BAs and six sulfated BAs, covering primary, secondary, and conjugated BAs. The analysis of 73 urine and 20 feces samples was used to test the applicability of the method. Concentrations of BAs in human urine and murine feces were reported, ranging from 0.5 to 50 nmol/g creatinine and from 0.012 to 332 nmol/g, respectively. Seventy-nine percent of BAs present in human urine samples corresponded to secondary conjugated BAs, while 69% of BAs present in murine feces corresponded to primary conjugated BAs. Glycocholic acid sulfate (GCA-S) was the most abundant BA in human urine samples, while taurolithocholic acid was the lowest concentrated compound detected. In murine feces, the most abundant BAs were α-murocholic, deoxycholic, dehydrocholic, and ß-murocholic acids, while GCA-S was the lowest concentrated BA. The presented method is a non-invasive approach for the simultaneous assessment of BAs and sulfated BAs in urine and feces samples, and the results will serve as a knowledge base for future translational studies focusing on the role of the microbiota in health.

Enhancing the accuracy of mid-infrared spectroscopy-based liver steatosis quantification using digital image analysis as a reference.

The assessment of liver steatosis is crucial in both hepatology and liver transplantation (LT) surgery. Steatosis can negatively impact the success of LT. Steatosis is a factor for excluding donated organs for LT, but the increasing demand for transplantable organs has led to the use of organs from marginal donors. The current standard for evaluating steatosis is a semi-quantitative grading based on the visual examination of a hematoxylin and eosin (H&E)-stained liver biopsy, but this method is time-consuming, subjective, and lacks reproducibility. Recent research has shown that infrared (IR) spectroscopy could be used as a real-time quantitative tool to assess steatosis during abdominal surgery. However, the development of IR-based methods has been hindered by the lack of appropriate quantitative reference values. In this study, we developed and validated digital image analysis methods for the quantitation of steatosis in H&E-stained liver sections using univariate and multivariate strategies including linear discriminant analysis (LDA), quadratic DA, logistic regression, partial least squares-DA (PLS-DA), and support vector machines. The analysis of 37 tissue samples with varying grades of steatosis demonstrates that digital image analysis provides accurate and reproducible reference values that improve the performance of IR spectroscopic models for steatosis quantification. A PLS model in the 1810-1052 cm-1 region using first derivative ATR-FTIR spectra provided RMSECV = 0.99%. The gained improvement in accuracy critically enhances the applicability of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to support an objective graft evaluation at the operation room, which might be especially relevant in cases of marginal liver donors to avoid unnecessary graft explantation.

Plasma serotonergic biomarkers are associated with hypoxemia events in preterm neonates.

BACKGROUND: Hypoxemia is a physiological manifestation of immature respiratory control in preterm neonates, which is likely impacted by neurotransmitter imbalances. We investigated relationships between plasma levels of the neurotransmitter serotonin (5-HT), metabolites of tryptophan (TRP), and parameters of hypoxemia in preterm neonates. METHODS: TRP, 5-HT, 5-hydroxyindoleacetic acid (5-HIAA), and kynurenic acid (KA) were analyzed in platelet-poor plasma at ~1 week and ~1 month of life from a prospective cohort of 168 preterm neonates <31 weeks gestational age (GA). Frequency of intermittent hypoxemia (IH) events and percent time hypoxemic (<80%) were analyzed in a 6 h window after the blood draw. RESULTS: At 1 week, infants with detectable plasma 5-HT had fewer IH events (OR (95% CI) = 0.52 (0.29, 0.31)) and less percent time <80% (OR (95% CI) = 0.54 (0.31, 0.95)) compared to infants with undetectable 5-HT. A similar relationship occurred at 1 month. At 1 week, infants with higher KA showed greater percent time <80% (OR (95% CI) = 1.90 (1.03, 3.50)). TRP, 5-HIAA or KA were not associated with IH frequency at either postnatal age. IH frequency and percent time <80% were positively associated with GA < 29 weeks. CONCLUSIONS: Circulating neuromodulators 5-HT and KA might represent biomarkers of immature respiratory control contributing to hypoxemia in preterm neonates. IMPACT: Hypoxemia events are frequent in preterm infants and are associated with poor outcomes. Mechanisms driving hypoxemia such as immature respiratory control may include central and peripheral imbalances in modulatory neurotransmitters. This study found associations between the plasma neuromodulators serotonin and kynurenic acid and parameters of hypoxemia in preterm neonates. Imbalances in plasma biomarkers affecting respiratory control may help identify neonates at risk of short- and long-term adverse outcomes.

The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics.

Drug hepatotoxicity assessment is a relevant issue both in the course of drug development as well as in the post marketing phase. The use of human relevant in vitro models in combination with powerful analytical methods (metabolomic analysis) is a promising approach to anticipate, as well as to understand and investigate the effects and mechanisms of drug hepatotoxicity in man. The metabolic profile analysis of biological liver models treated with hepatotoxins, as compared to that of those treated with non-hepatotoxic compounds, provides useful information for identifying disturbed cellular metabolic reactions, pathways, and networks. This can later be used to anticipate, as well to assess, the potential hepatotoxicity of new compounds. However, the applicability of the metabolomic analysis to assess the hepatotoxicity of drugs is complex and requires careful and systematic work, precise controls, wise data preprocessing and appropriate biological interpretation to make meaningful interpretations and/or predictions of drug hepatotoxicity. This review provides an updated look at recent in vitro studies which used principally mass spectrometry-based metabolomics to evaluate the hepatotoxicity of drugs. It also analyzes the principal drawbacks that still limit its general applicability in safety assessment screenings. We discuss the analytical workflow, essential factors that need to be considered and suggestions to overcome these drawbacks, as well as recent advancements made in this rapidly growing field of research.

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved.

Toxicity studies, among them hepatotoxicity, are key throughout preclinical stages of drug development to minimise undesired toxic effects that might eventually appear in the course of the clinical use of the new drug. Understanding the mechanism of injury of hepatotoxins is essential to efficiently anticipate their potential risk of toxicity in humans. The use of in vitro models and particularly cultured hepatocytes represents an easy and robust alternative to animal drug hepatotoxicity testing for predicting human risk. Here, we envisage an innovative strategy to identify potential hepatotoxic drugs, quantify the magnitude of the alterations caused, and uncover the mechanisms of toxicity. This strategy is based on the comparative analysis of metabolome changes induced by hepatotoxic and non-hepatotoxic compounds on HepG2 cells, assessed by untargeted mass spectrometry. As a training set, we used 25 hepatotoxic and 4 non-hepatotoxic compounds and incubated HepG2 cells for 24 h at a low and a high concentration (IC10 and IC50) to identify mechanism-related and cytotoxicity related metabolomic biomarkers and to elaborate prediction models accounting for global hepatotoxicity and mechanisms-related toxicity. Thereafter, a second set of 69 chemicals with known predominant mechanisms of toxicity and 18 non-hepatotoxic compounds were analysed at 1, 10, 100 and 1000 µM concentrations from which and based on the magnitude of the alterations caused as compared with non-toxic compounds, we defined a "toxicity index" for each compound. In addition, we extracted from the metabolome data the characteristic signatures for each mechanism of hepatotoxicity. The integration of all this information allowed us to identify specific metabolic patterns and, based on the occurrence of that specific metabolome changes, the models predicted the likeliness of a compound to behave as hepatotoxic and to act through a given toxicity mechanism (i.e., oxidative stress, mitochondrial disruption, apoptosis and steatosis) for each compound and concentration.

Metabolomic Diversity of Human Milk Cells over the Course of Lactation-A Preliminary Study.

Human milk (HM) is a complex biofluid containing a wide cell variety including epithelial cells and leukocytes. However, the cellular compositions and their phenotypic properties over the course of lactation are poorly understood. The aim of this preliminary study was to characterize the cellular metabolome of HM over the course of lactation. Cells were isolated via centrifugation and the cellular fraction was characterized via cytomorphology and immunocytochemical staining. Cell metabolites were extracted and analyzed using ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UPLC-QqTOF-MS) in the positive and negative electrospray ionization modes. Immunocytochemical analysis revealed a high variability of the number of detected cells with relative median abundances of 98% of glandular epithelial cells, 1% of leukocytes, and 1% of keratinocytes. Significant correlations between the milk postnatal age with percentage of epithelial cells and leukocytes, and with total cell count were observed. Results from the Hierarchical Cluster Analysis of immunocytochemical profiles were very similar to those observed in the analysis of the metabolomic profiles. In addition, metabolic pathway analysis showed alterations in seven metabolic pathways correlating with postnatal age. This work paves the way for future investigations on changes in the metabolomic fraction of the cellular compartment of HM.

Intestinal group 1 innate lymphoid cells drive macrophage-induced inflammation and endocrine defects in obesity and promote insulinemia.

Hypercaloric diets overactivate the intestinal immune system and disrupt the microbiome and epithelial cell functions, impairing glucose metabolism. The origins of this inflammatory cascade are poorly characterized. We investigated the involvement of intestinal proinflammatory group 1 innate lymphoid cells (ILC1s) in obesity progression and metabolic disruption. In obese mice, we studied longitudinally the ILC1s response to the diet and ILC1s depletion to address its role in obesity. ILC1s are required for the expansion of pro-inflammatory macrophages and ILC2s. ILC1s depletion induced the ILC3-IL-22 pathway, increasing mucin production, antimicrobial peptides, and neuroendocrine cells. These changes were translated into higher gut hormones and reduced insulinemia and adiposity. ILC1s depletion was also associated with a bloom in Akkermansia muciniphila and decreases in Bilophila spp. Intestinal-ILC1s are upstream activators of inflammatory signals, connecting immunity with the microbiome, the enteroendocrine system, and the intestinal barrier in the control of glucose metabolism and adiposity.

Longitudinal perturbations of plasma nuclear magnetic resonance profiles in neonatal encephalopathy.

BACKGROUND: Neonatal encephalopathy (NE) is a major cause of mortality and severe neurological disability in the neonatal period and beyond. We hypothesized that the degree of brain injury is reflected in the molecular composition of peripheral blood samples. METHODS: A sub-cohort of 28 newborns included in the HYPOTOP trial was studied. Brain injury was assessed by magnetic resonance imaging (MRI) once per patient and neurodevelopment at 24 months of age was evaluated using the Bayley III Scales of Infant and Toddler Development. The nuclear magnetic resonance (NMR) profile of 60 plasma samples collected before, during, and after cooling was recorded. RESULTS: In total, 249 molecular features were quantitated in plasma samples from newborns and postnatal age showed to affect detected NMR profiles. Lactate, beta-hydroxybutyrate, pyruvate, and three triglyceride biomarkers showed the ability to discern between different degrees of brain injury according to MRI scores. The prediction performance of lactate was superior as compared to other clinical and biochemical parameters. CONCLUSIONS: This is the first longitudinal study of an ample compound panel recorded by NMR spectroscopy in plasma from NE infants. The serial determination of lactate confirms its solid position as reliable candidate biomarker for predicting the severity of brain injury. IMPACT: The use of nuclear magnetic resonance (NMR) spectroscopy enables the simultaneous quantitation of 249 compounds in a small volume (i.e., 100 µL) of plasma. Longitudinal perturbations of plasma NMR profiles were linked to magnetic resonance imaging (MRI) outcomes of infants with neonatal encephalopathy (NE). Lactate, beta-hydroxybutyrate, pyruvate, and three triglyceride biomarkers showed the ability to discern between different degrees of brain injury according to MRI scores. Lactate is a minimally invasive candidate biomarker for early staging of MRI brain injury in NE infants that might be readily implemented in clinical guidelines for NE outcome prediction.

Isolation and Lipidomic Screening of Human Milk Extracellular Vesicles.

Extracellular vesicles (EVs) are secreted by cells and can be found in biological fluids (e.g., blood, saliva, urine, cerebrospinal fluid, and milk). EV isolation needs to be optimized carefully depending on the type of biofluid and tissue. Human milk (HM) is known to be a rich source of EVs, and they are thought to be partially responsible for the benefits associated with breastfeeding. Here, a workflow for the isolation and lipidomic analysis of HM-EVs is described. The procedure encompasses initial steps such as sample collection and storage, a detailed description for HM-EV isolation by multistage ultracentrifugation, metabolite extraction, and analysis by liquid chromatography coupled to mass spectrometry, as well as data analysis and curation.

A targeted metabolic analysis of football players and its association to player load: Comparison between women and men profiles.

Professional athletes undertake a variety of training programs to enhance their physical performance, technical-tactical skills, while protecting their health and well-being. Regular exercise induces widespread changes in the whole body in an extremely complex network of signaling, and evidence indicates that phenotypical sex differences influence the physiological adaptations to player load of professional athletes. Despite that there remains an underrepresentation of women in clinical studies in sports, including football. The objectives of this study were twofold: to study the association between the external load (EPTS) and urinary metabolites as a surrogate of the adaptation to training, and to assess the effect of sex on the physiological adaptations to player load in professional football players. Targeted metabolic analysis of aminoacids, and tryptophan and phenylalanine metabolites detected progressive changes in the urinary metabolome associated with the external training load in men and women's football teams. Overrepresentation analysis and multivariate analysis of metabolic data showed significant differences of the effect of training on the metabolic profiles in the men and women teams analyzed. Collectively, our results demonstrate that the development of metabolic models of adaptation in professional football players can benefit from the separate analysis of women and men teams, providing more accurate insights into how adaptation to the external load is related to changes in the metabolic phenotypes. Furthermore, results support the use of metabolomics to understand changes in specific metabolic pathways provoked by the training process.