2020
DOI: 10.1007/s11306-020-01680-4
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Metabolic changes in early neonatal life: NMR analysis of the neonatal metabolic profile to monitor postnatal metabolic adaptations

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Cited by 11 publications
(20 citation statements)
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References 53 publications
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“…37 Other work has used nuclear magnetic resonance (NMR)-based metabolomics of 253 healthy newborns to explore changes in the urinary metabolite profiles from the first and third days of life. 38 This work showed clear changes in the metabolite profiles with the first 72 h after birth and distinctions between late preterm and term newborn infants. 38 In a different NMR spectroscopy study of urinary metabolites, Diaz et al (2016) looked at prematurity in newborns.…”
Section: Ta B L E 1 (Continued)mentioning
confidence: 65%
See 2 more Smart Citations
“…37 Other work has used nuclear magnetic resonance (NMR)-based metabolomics of 253 healthy newborns to explore changes in the urinary metabolite profiles from the first and third days of life. 38 This work showed clear changes in the metabolite profiles with the first 72 h after birth and distinctions between late preterm and term newborn infants. 38 In a different NMR spectroscopy study of urinary metabolites, Diaz et al (2016) looked at prematurity in newborns.…”
Section: Ta B L E 1 (Continued)mentioning
confidence: 65%
“…38 This work showed clear changes in the metabolite profiles with the first 72 h after birth and distinctions between late preterm and term newborn infants. 38 In a different NMR spectroscopy study of urinary metabolites, Diaz et al (2016) looked at prematurity in newborns. 39 This study included 46 control infants with an average gestational age of 39 weeks and investigated the urine metabolomic profiles in comparison with 17 premature infants with an average gestational age of 35 weeks.…”
Section: Ta B L E 1 (Continued)mentioning
confidence: 65%
See 1 more Smart Citation
“…Data processing and statistical analysis: For both LC-MS-based [29] and NMRbased [30,31] approaches, an untargeted analytical approach was employed, and, hence, univariate as well as multivariate statistical methods were applied coupled to both unsupervised and supervised methods. Multiple data comparisons were performed as follows to shed light on amifostine's 3D cell anti-angiogenesis metabolome: (a) antiangiogenesis profiling upon deferoxamine stimulation (amifostine_deferoxamine, n = 14 vs. sunitinib_deferoxamine, n = 14), (b) anti-angiogenesis profiling upon VEGF-A stimulation (amifostine_VEGF-A, n = 14 vs. sunitinib_VEGF-A, n = 14), (c) drug-specific anti-angiogenesis profiling (amifostine_deferoxamine_VEGF-A, n = 28 vs. sunitinib_ deferoxamine_VEGF-A, n = 28), and (d) pathway-specific anti-angiogenesis profiling (amifostine_sunitinib_deferoxamine, n = 28 vs. amifostine_sunitinib_ VEGF-A, n = 28).…”
Section: Methodsmentioning
confidence: 99%
“…With the rapid development and gradual maturation of metabolomics, its application has been implemented in many fields, for example, the study of postnatal metabolic adaptations [103] and neonatal diseases [104,105]. Currently, though, only a few metabolomicsbased studies of ROP have been reported.…”
Section: Retinopathy Of Prematurity (Rop)mentioning
confidence: 99%