Vitamin D is a normal component of human milk. Daily maternal vitamin D supplementation in the 400 to 2,000 IU (10 to 50 mcg) range produces milk concentrations that are inadequate to deliver the daily requirement to an exclusively breastfed infant, and inadequate to correct pre-existing infant vitamin D deficiency through breastfeeding alone. Breastfeeding mothers who take vitamin D supplements in this range should give their infants a daily vitamin D supplement of at least 400 IU (10 mcg) to meet pediatric nutritional guidelines.[1-5] Daily maternal vitamin D dosages at or above 4,000 IU (100 mcg) achieve milk levels can potentially meet the daily infant goal intake of at least 400 IU (10 mcg), depending on the mother's underlying vitamin D status and daily infant milk intake. Obese mothers may have higher requirements. Holder pasteurization decreased median levels of the major forms of vitamin D in breastmilk by 20% in one study.[6]

Protein-ligand scoring functions are widely used in structure-based drug design for fast evaluation of protein-ligand interactions, and it is of strong interest to develop scoring functions with machine-learning approaches. In this work, by expanding the training set, developing physically meaningful features, employing our recently developed linear empirical scoring function Lin_F9 (Yang, C. J. Chem. Inf. Model. 2021, 61, 4630-4644) as the baseline, and applying extreme gradient boosting (XGBoost) with Δ-machine learning, we have further improved the robustness and applicability of machine-learning scoring functions. Besides the top performances for scoring-ranking-screening power tests of the CASF-2016 benchmark, the new scoring function ΔLin_F9XGB also achieves superior scoring and ranking performances in different structure types that mimic real docking applications. The scoring powers of ΔLin_F9XGB for locally optimized poses, flexible redocked poses, and ensemble docked poses of the CASF-2016 core set achieve Pearson's correlation coefficient (R) values of 0.853, 0.839, and 0.813, respectively. In addition, the large-scale docking-based virtual screening test on the LIT-PCBA data set demonstrates the reliability and robustness of ΔLin_F9XGB in virtual screening application. The ΔLin_F9XGB scoring function and its code are freely available on the web at (https://yzhang.hpc.nyu.edu/Delta_LinF9_XGB).

Gelsenicine is an indole alkaloid isolated from Gelsemium elegans Benth. In recent years, the role of G. elegans Benth preparations in anti-tumor, analgesic, dilatation and dermatological treatment has attracted attention, and it has been applied clinically, but it is easy to cause poisoning with its use. An UPLC-MS/MS method was established to determine the gelsenicine in mouse blood, and the pharmacokinetics of gelsenicine after intravenous (0.1 mg/kg) and intragastric (0.5 and 1 mg/kg) administration was studied. Deltalin was used as internal standard; a UPLC BEH C18 column was used for chromatographic separation. The mobile phase consisted of acetonitrile and 10 mmol/L ammonium acetate (0.1% formic acid) with a gradient elution flow rate of 0.4 mL/min. Multiple reaction monitoring mode was used for quantitative analysis of gelsenicine in electrospray ionization positive interface. Proteins from mouse blood were removed by acetonitrile precipitation. A validation of this method was performed in accordance with the US Food and Drug Administration guidelines. In the concentration range of 0.05-100 ng/mL, the gelsenicine in the mouse blood was linear (r > 0.995), and the lower limit of quantification was 0.05 ng/mL. In the mouse blood, the intra-day precision RSD was <12%, the inter-day precision RSD was <15%, the accuracy ranged from 89.8 to 112.3%, the average recovery was >76.8%, and the matrix effect was between 103.7 and 108.4%, which meet the pharmacokinetic research requirements of gelsenicine. The UPLC-MS/MS method is sensitive, rapid and selective, and has been successfully applied to the pharmacokinetic study of gelsenicine in mice. The absolute bioavailability of gelsenicine is 1.13%.