Neomangiferin, a natural C-glucosyl xanthone, has recently received a great deal of attention due to its multiple biological activities. In this study, a rapid and sensitive ultra-high performance liquid chroma-tography tandem mass spectrometry (UHPLC–MS/MS) method for the quantification of neomangiferin in rat plasma was developed. Using chloramphenicol as an internal standard (IS), plasma samples were subjected to a direct protein precipitation process using methanol (containing 0.05% formic acid). Quan-tification was performed by multiple reactions monitoring (MRM) method, with the transitions of the parent ions to the product ions of m/z 583.1-330.9 for NG and m/z 321.1-151.9 for IS. The assay was shown to be linear over the range of 0.2–400 ng/mL, with a lower limit of quantification of 0.2 ng/mL. Mean recovery of neomangiferin in plasma was in the range of 97.76%–101.94%. Relative standard deviations (RSDs) of intra-day and inter-day precision were both o 10%. The accuracy of the method ranged from 94.20%to 108.72%. This method was successfully applied to pharmacokinetic study of neomangiferin after intravenous (2 mg/kg) and intragastric (10 mg/kg) administration for the first time. The oral absolute bioavailability of neomangiferin was estimated to be 0.53%7 0.08%with an elimination half-life (t1/2) value of 2.74 7 0.92 h, indicating its poor absorption and/or strong metabolism in vivo.

Tacrolimus is a commonly used medication for the treatment of nephrotic syndrome. Due to its narrow therapeutic window and significant pharmacokinetic differences among individuals, therapeutic drug monitoring is required during its clinical use. In the process of therapeutic drug monitoring, machine learning-based personalized dosing prediction models for tacrolimus can excavate medication patterns from a large amount of clinical data, assist in clinical decision-making, and achieve individualized precise medication. Machine learning models, the application progress of machine learning in personalized administration of tacrolimus for patients with nephrotic syndrome, modeling points of machine learning prediction models, and the limitations of current prediction models were reviewed in this paper, which could provide references for future research in this field.

ObjectiveTo investigate effect of conducting training of autism spectrum disorder （ASD） early screening skill on improving the ability to early identify ASD of medical staffs in primary care hospitals. MethodsIn September 2021， the training of ASD early screening skills was carried out for medical staffs from 20 primary care hospitals in Chengdu. After training， the training effect was evaluated. The numbers of referrals from primary care hospitals to superior hospitals， confirmed ASD as well as their average diagnostic age of children with ASD before and after training were used as evaluation indicators. ResultsAfter training， the number of children with suspected ASD referred by primary care hospitals was more than that before training ［（16.65±11.60） vs. （3.40±2.23）， t=5.431， P<0.01］， the number of children diagnosed with ASD was more than that before training［（6.85±4.93） vs. （2.45±1.67）， t=4.171， P<0.01］， and the differences were statistically significant. As for the diagnosed age of ASD children， after training， the average age was lower than that before training ［（34.95±11.67） vs. （42.2±14.64）， t=-2.553， P=0.019］. ConclusionTraining of ASD early screening skills for medical staffs in primary care hospitals may help to improve their ability to early screening ASD children.