Noninvasive monitoring of evolving urinary metabolic patterns in neonatal encephalopathy.

BACKGROUND: Infants with moderate and severe neonatal encephalopathy (NE) frequently suffer from long-term adverse outcomes. We hypothesize that the urinary metabolome of newborns with NE reflects the evolution of injury patterns observed with magnetic resonance imaging (MRI). METHODS: Eligible patients were newborn infants with perinatal asphyxia evolving to NE and qualifying for therapeutic hypothermia (TH) included in the HYPOTOP trial. MRI was employed for characterizing brain injury. Urine samples of 55 infants were collected before, during, and after TH. Metabolic profiles of samples were recorded employing three complementary mass spectrometry-based assays, and the alteration of detected metabolic features between groups was assessed. RESULTS: The longitudinal assessment revealed significant perturbations of the urinary metabolome. After 24 h of TH, a stable disease pattern evolved characterized by the alterations of 4-8% of metabolic features related to lipid metabolism, metabolism of cofactors and vitamins, glycan biosynthesis and metabolism, amino acid metabolism, and nucleotide metabolism. Characteristic metabolomic fingerprints were observed for different MRI injury patterns. CONCLUSIONS: This study shows the potential of urinary metabolic profiles for the noninvasive monitoring of brain injury of infants with NE during TH. IMPACT: A comprehensive approach for the study of the urinary metabolome was employed involving a semi-targeted capillary electrophoresis-time-of-flight mass spectrometry (TOFMS) assay, an untargeted ultra-performance liquid chromatography (UPLC)-quadrupole TOFMS assay, and a targeted UPLC-tandem MS-based method for the quantification of amino acids. The longitudinal study of the urinary metabolome identified dynamic metabolic changes between birth and until 96 h after the initiation of TH. The identification of altered metabolic pathways in newborns with pathologic MRI outcomes might offer the possibility of developing noninvasive monitoring approaches for personalized adjustment of the treatment and for supporting early outcome prediction.

Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy.

Near-infrared (NIR) spectroscopy was evaluated as a rapid method for the determination of oleic, palmitic, linoleic and linolenic acids as well as omega-3, omega-6, and to predict polyunsaturated, monounsaturated and saturated fatty acids, together with triacylglycerides, diglycerides, free fatty acids and ergosterol in salmon oil. To do it, Partial Least Squares (PLS) regression models were applied to correlate NIR spectra with aforementioned fatty acids and lipid classes. Results obtained were validated in front of reference procedures based on high performance thin layer and gas chromatography. PLS-NIR has a good predictive capability with relative root mean square error of prediction (RRMSEP) values below or equal to 1.8% and provides rapid analysis without the use of any chemicals making it an environmentally friendly methodology.

A green analytical chemistry approach for lipid extraction: computation methods in the selection of green solvents as alternative to hexane.

There is a great interest in finding alternatives and green solvents in extraction processes to replace petroleum based solvents. In order to investigate these possibilities, computational methods, as Hansen solubility parameters (HSP) and conductor-like screening model for real solvent (COSMO-RS), were used in this work to predict the solvation power of a series of solvents in salmon fish lipids. Additionally, experimental studies were used to evaluate the performance in lipids extraction using 2-methyltetrahydrofurane, cyclopentyl methyl ether, dimethyl carbonate, isopropanol, ethanol, ethyl acetate, p-cymene and d-limonene compared with hexane. Lipid classes of extracts were obtained by using high performance thin-layer chromatography (HPTLC), whereas gas chromatography with a flame ionization detector (GC/FID) technique was employed to obtain fatty acid profiles. Some differences between theoretical and experimental results were observed, especially regarding the behavior of p-cymene and d-limonene, which separate from the predicted capability. Results obtained from HPTLC indicated that p-cymene and d-limonene extract triglycerides (TAGs) and diglycerides (DAGs) at levels of 73 and 19%, respectively, whereas the other studied extracts contain between 75 and 76% of TAGs and between 16 and 17% of DAGs. Fatty acid profiles, obtained by using GC-FID, indicated that saturated fatty acids (SFAs) between 19.5 and 19.9% of extracted oil, monounsaturated fatty acids (MUFAs) in the range between 43.5 and 44.9%, and PUFAs between 31.2 and 34.6% were extracted. p-Cymene and limonene extracts contained lower percentages than the other studied solvents of some PUFAs due probably to the fact that these unsaturated fatty acids are more susceptible to oxidative degradation than MUFAs. Ethyl acetate has been found to be the best alternative solvent to hexane for the extraction of salmon oil lipids. Graphical Abstract ᅟ.

Prediction of alkaline earth elements in bone remains by near infrared spectroscopy.

An innovative methodological approach has been developed for the prediction of the mineral element composition of bone remains. It is based on the use of Fourier Transform Near Infrared (FT-NIR) diffuse reflectance measurements. The method permits a fast, cheap and green analytical way, to understand post-mortem degradation of bones caused by the environment conditions on different skeletal parts and to select the best preserved bone samples. Samples, from the Late Roman Necropolis of Virgen de la Misericordia street and En Gil street located in Valencia (Spain), were employed to test the proposed approach being determined calcium, magnesium and strontium in bone remains and sediments. Coefficients of determination obtained between predicted values and reference ones for Ca, Mg and Sr were 90.4, 97.3 and 97.4, with residual predictive deviation of 3.2, 5.3 and 2.3, respectively, and relative root mean square error of prediction between 10% and 37%. Results obtained evidenced that NIR spectra combined with statistical analysis can help to predict bone mineral profiles suitable to evaluate bone diagenesis.

Direct determination of major components in human diets and baby foods.

A fast method has been developed for the determination of fat, proteins, carbohydrates, and energy value in baby food, infant fast food, and canteen menus, using near-infrared (NIR) and mid-infrared (MIR) spectroscopy measurements and multivariate calibration methods based on partial least square regression. Reference standard methods were employed to build and validate the infrared methods for direct determination of nutrients. Coefficients of determination obtained between predicted values and reference ones for total fat, proteins, carbohydrates, and energy value were 96.7, 98.1, 98.9, and 96.5 for NIR and 91.0, 93.0, 92.0, and 84.1 for MIR, respectively, with relative root mean square error of prediction (RRMSEP) below or equal to 9 % for NIR and 16 % for MIR. Results obtained indicate that both NIR and MIR techniques have good predictive capabilities, with the NIR method being the most accurate and simple.