Rapid determination of hippuric acid as an exposure biomarker of toluene using a colorimetric assay and comparison with high-performance liquid chromatography.

Occupational exposure to toluene is associated with health risks that require reliable monitoring methods. Hippuric acid (HA), a urinary metabolite of toluene, serves as a valuable biomarker for such exposure. Colorimetric methods for the quantitative determination of HA have gained prominence due to their simplicity, cost-effectiveness, and suitability for field application. In the present study, a simple colorimetric technique was optimized for the determination of HA in the urine sample, and compared with a usual HPLC technique. The central composite design (CCD) was applied to examine the effective parameters on the colorimetric determination of HA. The calibration curve for HA was established within the concentration range of 6 to 100 mg L-1 with R2 = 0.97. The detection limit (LOD) and quantification limit (LOQ) were determined to be 1.8 mg L-1 and 6 mg L-1 respectively. The relative standard deviation (RSD%) was less than 5%, and the recovery% (R%) was 90.5-100.1. The overall results showed good agreement between the colorimetric and HPLC results. There was a significant relationship between the results obtained from HPLC and colorimetric methods especially for higher concentration levels of HA (≥ 500 mg/g creatinine). In conclusion, our optimized colorimetric method is a simple, cost-effective, and rapid method for determination of HA in occupational exposure, which is comparable with the HPLC technique.

Efficient Quantification of Milk Metabolites from 1H NMR Spectra Using the Signature Mapping (SigMa) Approach: Chemical Shift Library Development for Cows' Milk and Colostrum.

Cow milk contains essential nutrients for humans, and its bulk composition is usually analyzed using Fourier transform infrared spectroscopy. The higher sensitivity of nuclear magnetic resonance (NMR) spectroscopy can augment the extractible qualitative and quantitative information from milk to nearly 60 compounds, enabling us to monitor the health of cows and milk quality. Proton (1H) NMR spectroscopy produces complex spectra that require expert knowledge for identifying and quantifying metabolites. Therefore, an efficient and reproducible methodology is required to transform complex milk 1H NMR spectra into annotated and quantified milk metabolome data. In this study, standard operating procedures for screening the milk metabolome using 1H NMR spectra are developed. A chemical shift library of 63 milk metabolites was established and implemented in the open-access Signature Mapping (SigMa) software. SigMa is a spectral analysis tool that transforms 1H NMR spectra into a quantitative metabolite table. The applicability of the proposed methodology to whole milk, skim milk, and ultrafiltered milk is demonstrated, and the method is tested on ultrafiltered colostrum samples from dairy cows (n = 88) to evaluate whether metabolic changes in colostrum may reflect the metabolic status of cows.

Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance.

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using 1H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the 1H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q2 of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized 1H NMR spectral recordings.

Signature Mapping (SigMa): An efficient approach for processing complex human urine 1H NMR metabolomics data.

Proton Nuclear Magnetic Resonance (NMR) spectroscopic analysis of urine generates rich but complex spectra. Retrieving metabolite information from such spectra is challenging due to signal overlapping, chemical shift changes, and large concentration variations of complex urine metabolome. This study demonstrates a new method, Signature Mapping (SigMa), for the rapid and efficient conversion of raw urine NMR spectra into an informative metabolite table. The principle behind SigMa relies on a division of the urine NMR spectra into Signature Signals (SS), Signals of Unknown spin Systems (SUS) and bins of complex unresolved regions (BINS). The method allows simultaneous detection of urinary metabolites in large NMR metabolomics studies using a SigMa chemical shift library and a new automatic peak picking algorithm. For quantification of SS and SUS SigMa uses multivariate curve resolution, while the unresolved inter-SS spectral regions are binned (BINS). SigMa is tested on three human urine 1H-NMR datasets including spiking experiments, and has proven to be extraordinarily efficient, quantitatively reliable and robust.

On the relationship between docking scores and protein conformational changes in HIV-1 protease.

We present a detailed investigation of the effect of the crystallographic structure of the HIV-1 protease (PR) on the binding energy of different classes of inhibitors obtained from docking simulations. The crystal structures of 222 HIV-1 proteases (in wild-type and mutant forms) and 202 inhibitors were downloaded from appropriate databases. A cross-docking approach (docking of all 202 inhibitors to all 222 PR structures) using Autodock Vina was implemented. The protease structures were clustered using a Kohonen self-organization map analysis of the data matrix of docking energies. The obtained clusters of PRs were correlated with the x-y-z coordinates of the PR structures to identify structural segments underlying this clustering. The PR structures were clustered into 4 classes. One of these classes exhibits rather strong binding with almost all inhibitors, while another class exhibits rather weak binding. The remaining two classes are intermediate in binding strength. The selectivity ratio indices for the carbon-alpha atoms of the PR structures indicate that conformational motion of residues outside the binding pocket contributes significantly to the discrimination of the 4 classes.

Multivariate image analysis-thin layer chromatography (MIA-TLC) for simultaneous determination of co-eluting components.

This paper addresses the solution of peak overlapping, as a fundamental problem in TLC, by multivariate analysis of the images recorded by a digital camera. We report the results of our study on the application of multivariate image analysis (MIA) for simultaneous determination of several species on thin layer chromatography (TLC) sheet for the first time. An imaging system, composed of a dark cabinet, a digital camera and a multivariate image analysis program, was prepared for recording the images of TLC plates after development of a multi-component solution. The written program was able to produce 2- and 3-dimensional chromatograms of the solutions, which were subsequently used as inputs of partial least squares, as an efficient multivariate calibration method. The ability of the proposed MIA-TLC method for simultaneous determination of the co-eluting components was validated by analysis of ternary synthetic mixtures of indicators of highly overlapped chromatograms (i.e., methyl yellow, bromocresol green and creseol red) and a real mixture of nifedipine and its photo-degradation product. By application of different strategies like principal component analysis and variable selection, models were obtained that could estimate the concentration of indicators in the external prediction set with relative errors of lower than 10% and in most cases lower than 5%.