L-histidine plays an important role in the growth of infants and young children as well as positively improves insulin resistance through suppressed inflammation. The study aimed to investigate the relationship between L-histidine levels and obesity and evaluate the feasibility of L-histidine as an important indicator of auxiliary diagnosis for pediatric obesity. Thus, the urinary content of L-histidine from 74 children with obesity and 84 normal weight were determined and analyzed in the study. Results found that the L-histidine concentration in urine (median [interquartile range] mg/L) was 8.29 (4.42-20.70) mg/L in the children with obesity and 5.60 (2.16-14.06) mg/L in the normal-weight children (P<0.01), 5.71 (2.57-17.15) mg/L in girls and 9.92 (5.54-23.33) mg/L in boys (P<0.05). L-histidine concentration in severe obesity children were significantly higher than that of mild obesity (P<0.05). The urinary L-histidine levels were positively correlate with BMI and the mean and median of L-histidine concentration in urine increase with children's obese levels.
<p>The origin of cold materials identified by different criteria is unclear. They are highly suspected to be the erupted prominence. However, some cold materials defined by charge depletion exist in both solar wind and ICMEs. Recently, solar observations show failed prominence eruption in CMEs that it did not propagate into the interplanetary space. Besides, the related prominence eruptions of the earth-directed ICMEs at 1 au are difficult to identify before the launch of STEREO mission. This work uses Random Forest (RF) that is an interpretable classifier of supervised machine learning to study the distinct signatures of prominence cold materials (PCs) compared to quiet solar wind (SW) and ICMEs. 12 parameters measured by ACE at 1 au are used in this study, which are proton moments, magnetic field component Bz, He/H, He/O, Fe/O, mean charge of oxygen and carbon, C<sup>6+</sup>/C<sup>5</sup>, C<sup>6+</sup>/C<sup>4+</sup>, and O<sup>7+</sup>/O<sup>6+</sup>. According to the returned weights from RF classifier and the training accuracy from one black box classifier, the most important in situ signatures of PCs are obtained. Next, the trained RF classifier is used to check the category of the origin-unknown cold materials in ICMEs. The results show that most of the cold materials are from prominence, but 2 of them are possibly from quiet solar wind. The most distinct signatures of PCs are lower charges of C and O, proton temperature, and He/O. This work provides quantitative evidence for the charges of C and O being most effective solid criteria. Considering the obvious overlaps on key parameters between SW, ICMEs, and PCs, multi-parameter classifier of machine learning show an advantage in separating them than solid criteria.</p>
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