AIM:To establish an LC-MS method for determining the concentrations of loratadine (LOR) in human plasma and to evaluate its pharmacokinetic characteristics. METHODS: A ZORBAX Eclipse XDB-C8 (5 μm, 150 mm×4.6 mm) column was used, atmospheric pressure electronic spray ionization (AP-ESI) and ion mass spectrum (m/z) of 388.2 (M+H)+ were selected to quantify LOR, and 275.1 (M+H)+ for ropivacaine (internal standard, IS). RESULTS: The linear range of LOR standard curve was 0.5-50 ng·ml-1, and the determination limit was 0.5 ng·ml-1. The pharmacokinetic parameters of LOR after a single dose of 20 mg tablet (T1), capsule (T2) and reference (R) were as follows, the half life (t1/2) 13.52±1.35, 13.14±0.98 and 14.00±1.25 h, the time to peak concentration (Tmax) 1.24±0.06, 1.18±0.12 and 1.17±0.12 h, the peak concentration (Cmax) 21.72±7.70, 21.49±8.34 and 20.50±8.65 ng·ml-1, the area under time-concentration curve (AUC0-48 and AUC0-∞) 137.24±47.84 and 146.61±51.03 ng·ml-1·h, 139.65±45.69 and 148.04±48.10 ng·ml-1·h, 134.19±49.03 and 143.70±52.08 ng·ml-1·h, the relative bioavailability of LOR tablet and capsule were (105.49±8.08)% and (102.90±10.02)%, respectively. CONCLUSION: The LC-MS method for determining the concentration of LOR in human plasma is sensitive and accurate and can be used for LOR bioavailability and pharmacokinetic studies. LOR tests and reference are bioequivalent.

A sensitive and selective liquid chromatography-tandem mass spectrometric (LC-MS/MS) method was developed and validated for the determination of betamethasone in human plasma. The analyte was isocratically eluted on a Venusil XBP C8 column (200 mm ×3.9 mm ID, 5 μm) with methanol-water with a triple quad LC-MS/MS using ESI with positive ionization. Ions monitored in the multiple reaction monitoring (MRM) mode were m/z 393.3→355.2 for betamethasone and m/z 361.3→343.2 for prednisolone (IS). Betamethasone was extracted from 0.5 mL human plasma with ethyl acetate. The validation study demonstrated excellent precision and accuracy across the calibration range of 0.5 - 80.0 injection in healthy Chinese volunteers.

The aim of this study is to establish an HPLC method for simultaneous determinations of mifepristone and its metabolites, mono-demethylated mifepristone, di-demethylated mifepristone and C-hydroxylated mifepristone in plasma and to evaluate the pharmacokinetic characteristics of mifepristone tablet. Twenty healthy female Chinese subjects were recruited and a series of blood samples were collected before and after 0.25, 0.5, 1.0, 1.5, 2.0, 4.0, 8.0, 12.0, 24.0, 48.0, 72.0 and 96.0 hours administration by a single oral dose of 75 mg mifepristone tablet. Mifepristone and its three metabolites were extracted from plasma using ethyl acetate and determined by high performance liquid chromatography. The main pharmacokinetic parameters of mifepristone and its metabolites, including Cmax, tmax, MRT, t(1/2), V, CL, AUC(0-96 h) and AUC(0-infinity), were calculated by Drug and Statistical Software Version 2.0. The simple, accurate and stable method allows the sensitive determinations ofmifepristone and its metabolites in human plasma up to 4 days after oral administration of 75 mg mifepristone tablet and the clinical applications of their pharmacokinetic studies.

Objective:To develop an artificial intelligence (AI) quality control model of fetal heart in the first trimester and verify its effectiveness.Methods:A total of 18 694 images of the four-chamber view(4CV) and three-vessel and tracheal view(3VT) of fetal heart in the first trimester were selected from Shenzhen Maternal and Child Health Hospital Affiliated to Southern Medical University since January 2022 to December 2022. A total of 14 432 images were manually annotated. The one-stage target detection algorithm YOLO V5 was used to train the AI quality control model in the first trimester of fetal heart, and 4 262 images (golden standard set by expert group) were used to evaluate the application effectiveness of AI quality control model. Kappa consistency test was used to compare the results of section classification and standard degree judgment from AI quality control model, Doctor 1(D1) and Doctor 2(D2).Results:①Precision of the AI quality control model was 0.895, recall was 0.852, mean average precision (mAP 50) was 0.873.The average precision(AP) of the AI quality control model for section classification was 0.907 (4CV) and 0.989 (3VT), respectively. ②Compared with the gold standard, the overall coincidence rate and consistency of section classification of AI quality control model, D1 and D2 were 99.91% (Kappa=0.998), 100% (Kappa=1.000), 100% (Kappa=1.000), respectively. The coincidence rate and consistency of the plane standard degree evaluation from the AI quality control model, D1 and D2 were 97.46% (Weighted Kappa=0.932), 93.73% (Weighted Kappa=0.847), and 93.12% (Weighted Kappa=0.832), respectively. Strong consistency was displayed. Moreover, AI quality control model showed the highest coincidence rate and the strongest consistency in judging section standard degree, which was superior to manual quality control. The time-consuming of AI quality control (0.012 s/sheet) was significantly less than the way of manual quality control (4.76-6.11 s/sheet)( Z=-8.079, P<0.001). Conclusions:The use of artificial intelligent fetal heart quality control model in the first trimester can effectively and accurately control the image quality.