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Studies focusing on the perinatal period face unique challenges, yet research in this area is extremely important, as this period of life is highly delicate and adverse events might have a long-lasting impact. With the advent of powerful high-resolution and high-throughput analytical methods, researchers have started to successfully develop and implement novel approaches in this area. New insights have great potential to be translated into novel diagnostic tools, as well as alternative preventive and treatment approaches. This book collects a series of timely review and original research articles focusing on metabolomic, oxidative, and nitrosative stress in the perinatal period.We would like to thank all involved authors for their high-quality contributions and their commitment to the publication of this work and hope that this book will be a useful resource for students, scientists, and doctors working in this specific area of application.

Medicine --- glutathione --- glutamate --- oxidative stress --- hypoxia ischemia --- endotoxin --- magnetic resonance spectroscopy --- N-acetylcysteine --- vitamin D --- neonatal HIE --- MRS --- preterm birth --- sex differences --- male disadvantage --- female advantage --- bronchopulmonary dysplasia --- retinopathy of prematurity --- necrotizing enterocolitis --- intraventricular hemorrhage --- periventricular leukomalacia --- mortality --- transposition of the great arteries --- balloon atrial septostomy --- hypoxemia --- metabolomics --- newborn --- liquid chromatography-mass spectrometry (LC-MS) --- allopurinol --- hypothermia --- hypoxic-ischemic encephalopathy --- oxidative damage --- donor milk --- treatment --- Holder pasteurization --- breastmilk --- preterm --- antioxidant capacity --- asphyxia neonatorum --- non-invasive diagnostics --- saliva --- neonatal hypoxia–ischemia --- kynurenic acid (KYNA) --- neuroprotection --- hyperoxia --- prematurity --- n/a --- neonatal hypoxia-ischemia

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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

Research & information: general --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning --- metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning

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Metabolomics data analysis strategies are central to transforming raw metabolomics data files into meaningful biochemical interpretations that answer biological questions or generate novel hypotheses. This book contains a variety of papers from a Special Issue around the theme “Best Practices in Metabolomics Data Analysis”. Reviews and strategies for the whole metabolomics pipeline are included, whereas key areas such as metabolite annotation and identification, compound and spectral databases and repositories, and statistical analysis are highlighted in various papers. Altogether, this book contains valuable information for researchers just starting in their metabolomics career as well as those that are more experienced and look for additional knowledge and best practice to complement key parts of their metabolomics workflows.

metabolic networks --- mass spectral libraries --- metabolite annotation --- metabolomics data mapping --- nontarget analysis --- liquid chromatography mass spectrometry --- compound identification --- tandem mass spectral library --- forensics --- wastewater --- gut microbiome --- meta-omics --- metagenomics --- metabolomics --- metabolic reconstructions --- genome-scale metabolic modeling --- constraint-based modeling --- flux balance --- host–microbiome --- metabolism --- global metabolomics --- LC-MS --- spectra processing --- pathway analysis --- enrichment analysis --- mass spectrometry --- liquid chromatography --- MS spectral prediction --- metabolite identification --- structure-based chemical classification --- rule-based fragmentation --- combinatorial fragmentation --- time series --- PLS --- NPLS --- variable selection --- bootstrapped-VIP --- data repository --- computational metabolomics --- reanalysis --- lipidomics --- data processing --- triplot --- multivariate risk modeling --- environmental factors --- disease risk --- chemical classification --- in silico workflows --- metabolome mining --- molecular families --- networking --- substructures --- mass spectrometry imaging --- metabolomics imaging --- biostatistics --- ion selection algorithms --- liquid chromatography high-resolution mass spectrometry --- data-independent acquisition --- all ion fragmentation --- targeted analysis --- untargeted analysis --- R programming --- full-scan MS/MS processing --- R-MetaboList 2 --- liquid chromatography–mass spectrometry (LC/MS) --- fragmentation (MS/MS) --- data-dependent acquisition (DDA) --- simulator --- in silico --- untargeted metabolomics --- liquid chromatography–mass spectrometry (LC-MS) --- experimental design --- sample preparation --- univariate and multivariate statistics --- metabolic pathway and network analysis --- LC–MS --- metabolic profiling --- computational statistical --- unsupervised learning --- supervised learning

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Metabolomics is increasingly being used to explore the dynamic responses of living systems in biochemical research. The complexity of the metabolome is outstanding, requiring the use of complementary analytical platforms and methods for its quantitative or qualitative profiling. In alignment with the selected analytical approach and the study aim, sample collection and preparation are critical steps that must be carefully selected and optimized to generate high-quality metabolomic data. This book showcases some of the most recent developments in the field of sample preparation for metabolomics studies. Novel technologies presented include electromembrane extraction of polar metabolites from plasma samples and guidelines for the preparation of biospecimens for the analysis with high-resolution μ magic-angle spinning nuclear magnetic resonance (HR-μMAS NMR). In the following chapters, the spotlight is on sample preparation approaches that have been optimized for diverse bioanalytical applications, including the analysis of cell lines, bacteria, single spheroids, extracellular vesicles, human milk, plant natural products and forest trees.

metabolomics --- sample preparation --- hydrophilic interaction liquid chromatography --- ion mobility spectrometry --- high resolution mass spectrometry --- design of experiments --- AMOPLS --- metabonomics --- metabolic profiling --- NMR --- nuclear magnetic resonance spectroscopy --- cell line --- human cell line --- MiaPaCa-2 --- Panc-1 --- AsPC-1 --- extracellular vesicles --- exosomes --- microvesicles --- biomarkers --- diagnostics --- metabolic pathways --- plant metabolomics --- forestry --- trees --- mass spectrometry --- metabolite extraction --- GC-MS --- LC-MS --- metadata standardization --- databases --- multicellular tumor spheroids --- metallodrugs --- oxaliplatin --- KP1339 --- method development --- IT-139 --- 20% FCS --- harvesting --- extraction --- metabolites --- normalization --- electromembrane extraction --- cardiovascular disease --- multi-segment injection --- capillary electrophoresis–mass spectrometry --- liquid chromatography–mass spectrometry --- plant natural products --- drug discovery --- liquid chromatography --- gas chromatography --- human milk --- metabolome --- sampling --- liquid chromatography–mass spectrometry (LC-MS) --- nuclear magnetic resonance (NMR) --- gas chromatography–mass spectrometry (GC-MS) --- capillary electrophoresis—mass spectrometry (CE-MS) --- high-resolution magic angle spinning --- microscopic samples --- lipidomics --- LC-MS/MS --- human plasma