Lipidomic-Based Algorithms Can Enhance Prediction of Obstructive Coronary Artery Disease.

Lipidomics emerges as a promising research field with the potential to help in personalized risk stratification and improve our understanding on the functional role of individual lipid species in the metabolic perturbations occurring in coronary artery disease (CAD). This study aimed to utilize a machine learning approach to provide a lipid panel able to identify patients with obstructive CAD. In this posthoc analysis of the prospective CorLipid trial, we investigated the lipid profiles of 146 patients with suspected CAD, divided into two categories based on the existence of obstructive CAD. In total, 517 lipid species were identified, from which 288 lipid species were finally quantified, including glycerophospholipids, glycerolipids, and sphingolipids. Univariate and multivariate statistical analyses have shown significant discrimination between the serum lipidomes of patients with obstructive CAD. Finally, the XGBoost algorithm identified a panel of 17 serum biomarkers (5 sphingolipids, 7 glycerophospholipids, a triacylglycerol, galectin-3, glucose, LDL, and LDH) as totally sensitive (100% sensitivity, 62.1% specificity, 100% negative predictive value) for the prediction of obstructive CAD. Our findings shed light on dysregulated lipid metabolism's role in CAD, validating existing evidence and suggesting promise for novel therapies and improved risk stratification.

Proteomics in Patients with Fibromyalgia Syndrome: A Systematic Review of Observational Studies.

PURPOSE OF REVIEW: Fibromyalgia syndrome (FMS) is a disease of unknown pathophysiology, with the diagnosis being based on a set of clinical criteria. Proteomic analysis can provide significant biological information for the pathophysiology of the disease but may also reveal biomarkers for diagnosis or therapeutic targets. The present systematic review aims to synthesize the evidence regarding the proteome of adult patients with FMS using data from observational studies. RECENT FINDINGS: An extensive literature search was conducted in MEDLINE/PubMed, CENTRAL, and clinicaltrials.gov from inception until November 2022. The study protocol was published in OSF. Two independent reviewers evaluated the studies and extracted data. The quality of studies was assessed using the modified Newcastle-Ottawa scale adjusted for proteomic research. Ten studies fulfilled the protocol criteria, identifying 3328 proteins, 145 of which were differentially expressed among patients with FMS against controls. The proteins were identified in plasma, serum, cerebrospinal fluid, and saliva samples. The control groups included healthy individuals and patients with pain (inflammatory and non-inflammatory). The most important proteins identified involved transferrin, α-, ß-, and γ-fibrinogen chains, profilin-1, transaldolase, PGAM1, apolipoprotein-C3, complement C4A and C1QC, immunoglobin parts, and acute phase reactants. Weak correlations were observed between proteins and pain sensation, or quality of life scales, apart from the association of transferrin and a2-macroglobulin with moderate-to-severe pain sensation. The quality of included studies was moderate-to-good. FMS appears to be related to protein dysregulation in the complement and coagulation cascades and the metabolism of iron. Several proteins may be dysregulated due to the excessive oxidative stress response.

Predicting Non-Alcoholic Steatohepatitis: A Lipidomics-Driven Machine Learning Approach.

Nonalcoholic fatty liver disease (NAFLD), nowadays the most prevalent chronic liver disease in Western countries, is characterized by a variable phenotype ranging from steatosis to nonalcoholic steatohepatitis (NASH). Intracellular lipid accumulation is considered the hallmark of NAFLD and is associated with lipotoxicity and inflammation, as well as increased oxidative stress levels. In this study, a lipidomic approach was used to investigate the plasma lipidome of 12 NASH patients, 10 Nonalcoholic Fatty Liver (NAFL) patients, and 15 healthy controls, revealing significant alterations in lipid classes, such as glycerolipids and glycerophospholipids, as well as fatty acid compositions in the context of steatosis and steatohepatitis. A machine learning XGBoost algorithm identified a panel of 15 plasma biomarkers, including HOMA-IR, BMI, platelets count, LDL-c, ferritin, AST, FA 12:0, FA 18:3 ω3, FA 20:4 ω6/FA 20:5 ω3, CAR 4:0, LPC 20:4, LPC O-16:1, LPE 18:0, DG 18:1_18:2, and CE 20:4 for predicting steatohepatitis. This research offers insights into the connection between imbalanced lipid metabolism and the formation and progression of NAFL D, while also supporting previous research findings. Future studies on lipid metabolism could lead to new therapeutic approaches and enhanced risk assessment methods, as the shift from isolated steatosis to NASH is currently poorly understood.

Detection of 26 Drugs of Abuse and Metabolites in Quantitative Dried Blood Spots by Liquid Chromatography-Mass Spectrometry.

A method was developed for the determination of 26 drugs of abuse from different classes, including illicit drugs in quantitative dried blood spots (qDBSs), with the aim to provide a convenient method for drug testing by using only 10 µL of capillary blood. A satisfactory limit of quantification (LOQ) of 2.5 ng/mL for 9 of the compounds and 5 ng/mL for 17 of the compounds and a limit of detection (LOD) of 0.75 ng/mL for 9 of the compounds and 1.5 ng/mL for 17 of the compounds were achieved for all analytes. Reversed-phase liquid chromatography was applied on a C18 column coupled to MS, providing selective detections with both +ESI and -ESI modes. Extraction from the qDBS was performed using AcN-MeOH, 1:1 (v/v), with recovery ranging from 84.6% to 106%, while no significant effect of the hematocrit was observed. The studied drugs of abuse were found to be stable over five days under three different storage conditions (at ambient temperature 21 °C, at -20 °C, and at 35 °C), thus offering a highly attractive approach for drug screening by minimally invasive sampling for individuals that could find application in forensic toxicology analysis.

Lipidomic Analysis of Liver and Adipose Tissue in a High-Fat Diet-Induced Non-Alcoholic Fatty Liver Disease Mice Model Reveals Alterations in Lipid Metabolism by Weight Loss and Aerobic Exercise.

Detailed investigation of the lipidome remodeling upon normal weight conditions, obesity, or weight loss, as well as the influence of physical activity, can help to understand the mechanisms underlying dyslipidemia in metabolic conditions correlated to the emergence and progression of non-alcoholic fatty liver disease (NAFLD). C57BL/6 male mice were fed a normal diet (ND) or a high-fat diet (HFD) for 20 weeks. Subgroups within the high-fat diet (HFD) group underwent different interventions: some engaged in exercise (HFDex), others were subjected to weight loss (WL) by changing from the HFD to ND, and some underwent a combination of weight loss and exercise (WLex) during the final 8 weeks of the 20-week feeding period. To support our understanding, not only tissue-specific lipid remodeling mechanisms but also the cross-talk between different tissues and their impact on the systemic regulation of lipid metabolism are essential. Exercise and weight loss-induced specific adaptations in the liver and visceral adipose tissue lipidomes of mice were explored by the UPLC-TOF-MS/MS untargeted lipidomics methodology. Lipidomic signatures of ND and HFD-fed mice undergoing weight loss were compared with animals with and without physical exercise. Several lipid classes were identified as contributing factors in the discrimination of the groups by multivariate analysis models, such as glycerolipids, glycerophospholipids, sphingolipids, and fatty acids, with respect to liver samples, whereas triglycerides were the only lipid class identified in visceral adipose tissue. Lipids found to be dysregulated in HFD animals are related to well-established pathways involved in the biosynthesis of PC, PE, and TG metabolism. These show a reversing trend back to basic levels of ND when animals change to a normal diet after 12 weeks, whereas the impact of exercise, though in some cases it slightly enhances the reversing trend, is not clear.

Ensuring Fact-Based Metabolite Identification in Liquid Chromatography-Mass Spectrometry-Based Metabolomics.

Metabolite identification represents a major bottleneck in contemporary metabolomics research and a step where critical errors may occur and pass unnoticed. This is especially the case for studies employing liquid chromatography-mass spectrometry technology, where there is increased concern on the validity of the proposed identities. In the present perspective article, we describe the issue and categorize the errors into two types: identities that show poor biological plausibility and identities that do not comply with chromatographic data and thus to physicochemical properties (usually hydrophobicity/hydrophilicity) of the proposed molecule. We discuss the problem, present characteristic examples, and propose measures to improve the situation.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review.

Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

Plasma Lipids Profile in the Prediction of Non-Alcoholic Steatohepatitis in Adults: A Case-Control Study.

Patients with non-alcoholic steatohepatitis (NASH) show significantly faster progress in the stages of fibrosis compared to those with non-alcoholic fatty liver (NAFL) disease. The non-invasive diagnosis of NASH remains an unmet clinical need. Preliminary data have shown that sphingolipids, especially ceramides, fatty acids, and other lipid classes may be related to the presence of NASH and the histological activity of the disease. The aim of our study was to assess the association of certain plasma lipid classes, such as fatty acids, acylcarnitines, and ceramides, with the histopathological findings in patients with NASH. The study included three groups: patients with NASH (N = 12), NAFL (N = 10), and healthy [non non-alcoholic fatty liver disease (NAFLD)] controls (N = 15). Plasma samples were collected after 12 h of fasting, and targeted analyses for fatty acids, acylcarnitines, and ceramides were performed. Baseline clinical and demographic characteristics were collected. There was no significant difference in baseline characteristics across the three groups or between NAFL and NASH patients. Patients with NASH had increased levels of several fatty acids, including, among others, fatty acid (FA) 14:0, FA 15:0, FA 18:0, FA 18:3n3, as well as Cer(d18:1/16:0), compared to NAFL patients and healthy controls. No significant difference was found between NAFL patients and healthy controls. In conclusion, patients with NASH exhibited a distinctive plasma lipid profile that can differentiate them from NAFL patients and non-NAFLD populations. More data from larger cohorts are needed to validate these findings and examine possible implications for diagnostic and management strategies of the disease.

Optimizing the Distillation of Greek Oregano-Do Process Parameters Affect Bioactive Aroma Constituents and In Vitro Antioxidant Activity?

The aim of the present work was to optimize the conditions of the distillation process at a pilot scale to maximize the yield of specific bioactive compounds of the essential oil of oregano cultivated in Greece, and subsequently to study the in vitro antioxidant activity of these oils. Steam distillation was conducted at an industrial distillery and a Face-Centered Composite (FCC) experimental design was applied by utilizing three distillation factors: time, steam pressure and temperature. Essential oil composition was determined by static headspace gas chromatography-mass spectrometry (HS-GC/MS). To obtain a comprehensive profile of the essential oils, instrumental parameters were optimized, including sample preparation, incubation conditions, sampling process, injection parameters, column thermal gradient and MS conditions. With the applied GC-MS method, more than 20 volatile compounds were identified in the headspace of the oregano essential oils and their relative percentages were recorded. Carvacrol was the most prominent constituent under all distillation conditions applied. Data processing revealed time as the main factor which most affected the yield. The Desired Space (DSc) was determined by conducting a three-dimensional response surface analysis of the independent and dependent variables, choosing yields of thymol and carvacrol as optimization criteria. The in vitro antioxidant activity of the essential oils of all samples was measured in terms of the interaction with the stable free radical 2,2-diphenyl-1-picrylhydrazyl (DPPH) after 20 and 60 min. The most prominent essential oils at different distillation conditions were also tested as inhibitors of lipid peroxidation. Higher % values of carvacrol and thymol were correlated to higher antioxidant activity. Evaluating the impact of the distillation conditions on the in vitro results, it seems that lower pressure, less time and higher temperature are crucial for enhanced antioxidant activities.

Hepatic Metabolic Profiling of Lifelong Exercise Training Rats.

Regular physical exercise has been investigated as a primary preventive measure of several chronic diseases and premature death. Moreover, it has been shown to synchronize responses across multiple organs. In particular, hepatic tissue has proven to be a descriptive matrix to monitor the effect of physical activity. In this study, we performed an untargeted metabolomics-based analysis of hepatic tissue extracts from rats that have undergone either lifelong or chronic exercise training. For this purpose, 56 hepatic samples were collected and were analyzed by UHPLC-TOF-MS in negative ionization mode. This approach involved untargeted metabolite detection on hepatic tissue extracts accompanied by an in-house retention time/accurate mass library enabling confident metabolite identification. Unsupervised (PCA) and supervised (OPLS-DA) multivariate analysis showed significant metabolic perturbation on a panel of 28 metabolites, including amino acids, vitamins, nucleotides, and sugars. The training regime employed in this study resulted in a probable acceleration of the bioenergetic processes (glycolysis, glycogen metabolism), promoted catabolism of purines, and supplied biosynthetic precursors via the pentose phosphate pathway and pentose and glucuronate interconversions. Overall, the applied methodology was able to discriminate the different training schedules based on the rat liver metabolome.

Is Current Practice Adhering to Guidelines Proposed for Metabolite Identification in LC-MS Untargeted Metabolomics? A Meta-Analysis of the Literature.

Metabolite identification remains a bottleneck and a still unregulated area in untargeted LC-MS metabolomics. The metabolomics research community and, in particular, the metabolomics standards initiative (MSI) proposed minimum reporting standards for metabolomics including those for reporting metabolite identification as long ago as 2007. Initially, four levels were proposed ranging from level 1 (unambiguously identified analyte) to level 4 (unidentified analyte). This scheme was expanded in 2014, by independent research groups, to give five levels of confidence. Both schemes provided guidance to the researcher and described the logical steps that had to be made to reach a confident reporting level. These guidelines have been presented and discussed extensively, becoming well-known to authors, editors, and reviewers for academic publications. Despite continuous promotion within the metabolomics community, the application of such guidelines is questionable. The scope of this meta-analysis was to systematically review the current LC-MS-based literature and effectively determine the proportion of papers following the proposed guidelines. Also, within the scope of this meta-analysis was the measurement of the actual identification levels reported in the literature, that is to find how many of the published papers really reached full metabolite identification (level 1) and how many papers did not reach this level.

Prognostic significance of metabolomic biomarkers in patients with diabetes mellitus and coronary artery disease.

BACKGROUND: Diabetes mellitus (DM) and coronary artery disease (CAD) constitute inter-related clinical entities. Biomarker profiling emerges as a promising tool for the early diagnosis and risk stratification of either DM or CAD. However, studies assessing the predictive capacity of novel metabolomics biomarkers in coexistent CAD and DM are scarce. METHODS: This post-hoc analysis of the CorLipid trial (NCT04580173) included 316 patients with CAD and comorbid DM who underwent emergency or elective coronary angiography due to acute or chronic coronary syndrome. Cox regression analyses were performed to identify metabolomic predictors of the primary outcome, which was defined as the composite of major adverse cardiovascular or cerebrovascular events (MACCE: cardiovascular death, myocardial infarction, stroke, major bleeding), repeat unplanned revascularizations and cardiovascular hospitalizations. Linear regression analyses were also performed to detect significant predictors of CAD complexity, as assessed by the SYNTAX score. RESULTS: After a median 2-year follow up period (IQR = 0.7 years), the primary outcome occurred in 69 (21.8%) of patients. Acylcarnitine ratio C4/C18:2, apolipoprotein (apo) B, history of heart failure (HF), age > 65 years and presence of acute coronary syndrome were independent predictors of the primary outcome in diabetic patients with CAD (aHR = 1.89 [1.09, 3.29]; 1.02 [1.01, 1.04]; 1.28 [1.01, 1.41]; 1.04 [1.01, 1.05]; and 1.12 [1.05-1.21], respectively). Higher levels of ceramide ratio C24:1/C24:0, acylcarnitine ratio C4/C18:2, age > 65 and peripheral artery disease were independent predictors of higher CAD complexity (adjusted ß = 7.36 [5.74, 20.47]; 3.02 [0.09 to 6.06]; 3.02 [0.09, 6.06], respectively), while higher levels of apoA1 were independent predictors of lower complexity (adjusted ß= - 0.65 [- 1.31, - 0.02]). CONCLUSIONS: In patients with comorbid DM and CAD, novel metabolomic biomarkers and metabolomics-based prediction models could be recruited to predict clinical outcomes and assess the complexity of CAD, thereby enabling the integration of personalized medicine into routine clinical practice. These associations should be interpreted taking into account the observational nature of this study, and thus, larger trials are needed to confirm its results and validate them in different and larger diabetic populations.

Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management.

BACKGROUND: Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. OBJECTIVE: The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. KEY REPORTING PRACTICES: Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: "minimal" and "best reporting practice" levels.

Detection and determination of C12-, C14-, C16-alkyldimethylamines in human blood using gas chromatography-mass spectrometry.

RATIONALE: N,N-Dimethyldodecylamine is produced from lauryl alcohol and dimethylamine. C12-C16 alkyldimethylamines are used as intermediates for the manufacture of amineoxides and quaternary amino compounds. In the present study a gas chromatography-mass spectrometry (GC/MS) method for the determination of C12-C16 alkyldimethylamines in blood was developed and validated. The reason for this study was the detection of the above compounds in the postmortem blood sample of a fatal suicide case. METHODS: Analysis of amines was performed using a gas chromatograph (Agilent Technologies 7890A) with an MS 5975C inrXL, EI/CI MSD with triple-axis detector in selected ion monitoring mode, after liquid-liquid extraction. Four different organic solvents (butyl acetate, ethyl acetate, n-hexane and n-heptane) were used for the optimization of the extraction procedure, resulting in ethyl acetate being the solvent of choice for the extraction procedure. A QuEChERS step was applied (20 mg of MgSO4 , 5 mg of NaCl) to 1 mL of blood and pH was adjusted at 12 (K2 CO3 buffer solution). After the addition of the extraction solvent, samples were vortexed, centrifuged and directly injected into the GC/MS system. RESULTS: In validation, the method was found to be selective and sensitive (limit of detection from 0.3 to 0.5 ng/mL, limit of quantitation from 10.0 to 20.0 ng/mL), whilst validation included recovery, stability, accuracy and precision (relative standard deviation). Validation results were found satisfactory: intra- and interday precision ranged from 0.4% to 2% and from 0.6% to 1.9% respectively, while intra- and interday accuracy ranged from 87% to 109% and from 86% to112.8%. C12-C16 alkyldimethylamines were detected in blood samples at a concentration of 8.39 µg/mL (C12), 3.01 µg/mL (C14) and 0.42 µg/mL (C16). CONCLUSIONS: A rapid, sensitive and reliable method was developed for the determination of C12-C16 alkyldimethylamines in postmortem blood, after optimization of the sample preparation procedure, and finally successfully applied to a real postmortem blood sample from a fatal case involving these compounds.

A HILIC-MS/MS method development and validation for the quantitation of 13 acylcarnitines in human serum.

Acylcarnitines are essential diagnostic markers for complex diseases and fatty acid metabolism disorders, and play an important role in cardiovascular diseases. Herein, a HILIC-MS/MS method was developed and validated for the rapid quantitation of the acylcarnitines C2, C3, C4, C5, C6, C8, C10, C12, C14, C16, C18, C18:1 and C18:2 in human serum. RPLC and HILIC modes were tested, and HILIC was selected since it provided optimum analyte separation. Intra- and interday accuracy ranged from 90.4% to 114% and from 96% to 112%, respectively, while intra- and interday precision ranged from 0.37% to 13.7% and from 1.3% to 9.5%, respectively. A limit of quantitation (LOQ) of 78.1 ng/mL was found for C2, 2.4 ng/mL for C3, C18:1 and C18:2, and 1.2 ng/mL for C4, C5, C6, C8, C10, C12, C14, C16, and C18. Method validation was performed in accordance with bioanalytical method guidelines. Subsequently the method was applied in the analysis of approximately 1040 samples from patients with coronary artery disease.

Liquid chromatography-mass spectrometry method for the determination of polyethylene terephthalate and polybutylene terephthalate cyclic oligomers in blood samples.

Food contact materials (FCM) polyethylene terephthalate (PET) and polybutylene terephthalate (PBT) used extensively in food packaging may contain cyclic oligomers which may migrate into food and thus cause toxic effects on human health. A simple, fast, and sensitive ultra-high-performance liquid chromatography method quadrupole time-of-flight mass spectrometer was developed for the analysis of 7 cyclic oligomers in post-mortem blood samples. The targeted analytes were separated on a Waters BEH C18 (150 × 2.1 mm, 1.7 µm) analytical column by gradient elution. Calibration curves were constructed based on standard solutions and blood samples and Student's t-test was applied to evaluate the matrix effect. The LODs ranged from 1.7 to 16.7 µg mL-1, while the method accuracy was assessed by recovery experiments and resulting within the range 84.2-114.6%. Such an analytical method for the determination of PET and PBT cyclic oligomers in biological samples is reported for the first time. The developed methodology allows the determination of these oligomers in blood providing a useful analytical tool to assess the exposure and thus the potential hazard and health risks associated with these non-intentionally added substances (NIAS) from PET and PBT FCM through food consumption. The method was validated and successfully applied to the analysis of 34 post-mortem whole blood samples. Polyethylene terephthalate trimer was detected in four of them, for the first time in literature.

UHPLC-MS/MS method for the simultaneous determination of nicotine and tobacco-specific nitrosamines NNN and NNK for use in preclinical studies.

A new method was developed and validated for the simultaneous determination of nicotine and tobacco-specific nitrosamines (TSNAs) 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and N-nitrosonornicotine (NNN) in two different tests matrices: porcine buccal epithelium tissue and phosphate buffered saline (PBS) extracts of smokeless tobacco products. The novelty of this work is in the development of a liquid chromatography tandem mass spectrometry method that can provide simultaneous quantification of trace levels of TSNAs and high concentrations of nicotine in biological media. Precision, accuracy, and stability were evaluated during method validation to ensure the method was fit for purpose. Several sample preparation and extraction methods were evaluated to minimize matrix effects and maximize analyte recoveries. The method was accurate in the range of 81.1% - 117%; repeatability was estimated in the range of 1.5% - 13.6% across multiple concentrations. The linear regression correlation coefficient (R2) was greater than 0.9959 for all analytes, and the limit of detection (LOD) was determined for nicotine, NNK, and NNN at 1 ng/mL 0.005 ng/mL, and 0.006 ng/ mL, respectively. Our method was found to be appropriate for the analysis of nicotine, NNN, and NNK in the porcine buccal epithelium and PBS extracts of smokeless tobacco products.

Investigation of salivary biomarkers as indicators of skeletal and dental maturity in children.

OBJECTIVE: Estimation of patient's skeletal maturity in orthodontics is essential for the diagnosis and treatment planning. The aim of the study was to investigate the potential use of metabolic fingerprint of saliva for bone growth and tooth development estimation. MATERIALS AND METHODS: Saliva samples from 54 young patients were analysed by an untargeted gas chromatography-mass spectrometry metabolomics-based method. The skeletal maturity was calculated with the cervical vertebrae maturation method, and the dental age was estimated with the Demirjian method. Multivariate analysis and univariate analysis were performed to investigate differences within skeletal, dental and chronological age groups. RESULTS: Metabolomic analysis identified 61 endogenous compounds. Mannose, glucose, glycerol, glyceric acid and pyroglutamic acid levels differentiated significantly with skeletal age (P = .02 to .043), while mannose, lactic acid, glycolic acid, proline, norleucine, 3-aminoisobutyric acid, threonine, cadaverine and hydrocinnamic acid levels differed within the dental age groups (P = .018 to .04); according to the chronological age, only the levels of mannose and 3-hydroxyphenylacetic acid showed variation (P = .029 and .048). The principal component analysis did not manage to highlight differences between the groups of the studied parameters. CONCLUSION: Differentiated levels of mannose, glucose, glycerol, glyceric acid and pyroglutamic acid related to skeletal maturation were identified. According to dental development, the levels of mannose, lactic acid, glycolic acid, proline, norleucine, 3-aminoisobutyric acid, threonine, cadaverine and hydrocinnamic acid differed within the groups, while regarding chronological age, only the levels of mannose and 3-hydroxyphenylacetic acid showed variations. Further studies are required to prove their relation to skeletal and dental development pathway by applying complementary analytical techniques to wider cover the metabolome.

Acylcarnitines in Ophthalmology: Promising Emerging Biomarkers.

Several common ocular diseases are leading causes of irreversible visual impairment. Over the last decade, various mainly untargeted metabolic studies have been performed to show that metabolic dysfunction plays an important role in the pathogenesis of ocular diseases. A number of metabolites in plasma/serum, aqueous or vitreous humor, or in tears have been found to differ between patients and controls; among them are L-carnitine and acylcarnitines, which are essential for mitochondrial fatty acid oxidation. The metabolic profile of carnitines regarding a variety of diseases has attracted researchers' interest. In this review, we present and discuss recent advances that have been made in the identification of carnitines as potential metabolic biomarkers in common ocular diseases, such as age-related macular degeneration, diabetic retinopathy, retinopathy of prematurity, central retinal vein occlusion, primary open-angle glaucoma, rhegmatogenous retinal detachment, and dry eye syndrome.