TB is also a major cause of morbidity and mortality in children, with an estimated one million dying from TB each year 1. One-quarter of the world’s population is infected with Mycobacterium tuberculosis, and 10 million people fell ill with tuberculosis (TB) in 2019 1. The use of metabolomics could be useful to improve the prediction of TB progression and diagnosis in children. Differences in the metabolic fingerprint in children with different diagnostic certainty of TB could contribute to a more accurate characterisation of TB in the paediatric population. Moreover, children with unconfirmed TB with X-rays compatible with TB showed differences in the metabolic fingerprint compared to children with non-pathological X-rays (p = 0.009). We observed differences in the metabolic fingerprint of children with bacteriologically confirmed and unconfirmed TB compared to children with unlikely TB (p = 0.041 and p = 0.013, respectively). Urine metabolic fingerprints were identified using high- and low-field proton NMR platforms and assessed with pattern recognition techniques such as principal components analysis and partial least squares discriminant analysis. Six of the children with presumptive TB had bacteriologically confirmed TB, 52 children with unconfirmed TB, and 4 children with unlikely TB. We included 62 children with signs and symptoms of TB and 55 apparently healthy children. In this study, we explore whether urine nuclear magnetic resonance (NMR)-based metabolomics could be used to identify differences in the metabolic response of children with different diagnostic certainty of TB. Finally, an average predicted spectrum is calculated (employing a Boltzmann weighted average of the shifts calculated for all low-energy conformers).ġH NMR ‘ Best‘ prediction analyses the individual chemical shifts from the two complementary methods to give a single, unified predicted chemical shift.Tuberculosis (TB) is a major cause of morbidity and mortality in children, and early diagnosis and treatment are crucial to reduce long-term morbidity and mortality. This method requires first the generation of 3D conformers from a 2D structure so the individual spectra of all conformers are predicted. This algorithm, named CHARGE, is a composite program made up of a neural network based approach for the one-, two- and three-bond substituent effects plus a theoretical calculation of the long range effects of substituents. Furthermore, a complementary prediction approach based upon partial atomic charges and steric interactions is also performed. These substructures provide the base value of a final predicted chemical shift. First, a prediction algorithm that is based on tabulated chemical shifts for classes of structures, corrected with additive contributions from neighboring functional groups or substructures, is carried out. This prediction follows a similar approach to the case of 13C spectra.
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