Expected 8-Week Prenatal vs 12-Week Perinatal Tenofovir Alafenamide Prophylaxis to Prevent Mother-to-Child Transmission of Hepatitis B Virus: A Multicenter, Prospective, Open-Label, Randomized Controlled Trial.

Introduction: The course of maternal antiviral prophylaxis to prevent mother-to-child transmission of hepatitis B virus (HBV-MTCT) varies greatly, and it has not been demonstrated in a randomized controlled study.

Methods: In this multicenter, open-label, randomized controlled trial, eligible pregnant women with HBV DNA of 5.3-9.

Sensitive and effective method with 96-well plate for determination of levamlodipine in human plasma using LC-MS/MS.

we developed an effective protein precipitation method for determination of levamlodipine in human plasma using LC-MS/MS. Sample extraction was carried out by using liquid-liquid extraction in 96-well plate format. (S)-Amlodipine-d4 was used as internal standard (IS).

Safety, tolerability, pharmacokinetics and neutrophil elastase inhibitory effects of Sivelestat: A randomized, double-blind, placebo-controlled single- and multiple-dose escalation study in Chinese healthy subjects.

Background And Objective: Neutrophil elastase has been identified as a potential therapeutic target for acute lung injury or acute respiratory distress syndrome, and Sivelestat is a selective, reversible and competitive neutrophil elastase inhibitor. This study was designed to investigate the safety, tolerability, pharmacokinetics and neutrophil elastase inhibitory effects of Sivelestat in healthy Chinese subjects.

Methods: A randomized, double-blind, placebo-controlled single- and multiple-dose escalation clinical trial was carried out.

Tenofovir Alafenamide for Pregnant Chinese Women With Active Chronic Hepatitis B: A Multicenter Prospective Study.

Background & Aims: Data on long-term tenofovir alafenamide (TAF) therapy for pregnant women with active chronic hepatitis B (CHB) (immune clearance and reactivation phases, currently and previously diagnosed) and their infants are lacking.

Methods: Pregnant women with active CHB treated with TAF and tenofovir disoproxil fumarate (TDF) were enrolled in this multicenter prospective study, and infants received immunoprophylaxis. The primary outcomes were rates of adverse (safety) events in pregnant women and defects in infants and fetuses.

Detection of sivelestat and its metabolite in small volumes of plasma from Chinese ALI/ARDS patients with SIRS via high-throughput UPLC-MS/MS: A pharmacokinetic study.

In this study, we developed a sensitive and efficient analytical approach combining a 96-well plate-based protein precipitation strategy with ultra-performance liquid chromatography electrospray ionization tandem mass spectrometry (UPLC-MS/MS) in order to assess the pharmacokinetic (PK) properties of sivelestat and its metabolite XW-IMP-A in samples of plasma from ALI/ARDS patients with SIRS. The samples were separated via gradient elution with a C18 column (Phenomenex Kinetex, C18, 2.6 μm, 100 Å, 50 × 2.

Biosensing with microbial fuel cells and artificial neural networks: laboratory and field investigations.

In this study microbial fuel cell-based biosensing was integrated with artificial neural networks (ANNs) in laboratory and field testing of water samples. Inoculation revealed two types of anode-respiring bacteria (ARB) induction profiles, a relatively slow gradual profile and a faster profile that was preceded by a significant lag time. During laboratory testing, the MFCs generated well-organized normally distributed profiles but during field experiments the peaks had irregular shapes and were smaller in magnitude.

Neural network processing of microbial fuel cell signals for the identification of chemicals present in water.

Biosensing is emerging as an important element of water quality monitoring. This research demonstrated that microbial fuel cell (MFC)-based biosensing can be integrated with artificial neural networks (ANNs) to identify specific chemicals present in water samples. The non-fermentable substrates, acetate and butyrate, induced peak areas (PA) and peak heights (PH) that were generally larger than those caused by the injection of fermentable substrates, glucose and corn starch.

Using microbial fuel cell output metrics and nonlinear modeling techniques for smart biosensing.

Microbial fuel cells (MFCs) are promising tools for water quality monitoring but the response peaks have not been characterized and the data processing methods require improvement. In this study MFC-based biosensing was integrated with two nonlinear programming methods, artificial neural networks (ANN) and time series analysis (TSA). During laboratory testing, the MFCs generated well-organized normally-distributed peaks when the influent chemical oxygen demand (COD) was 150 mg/L or less, and multi-peak signals when the influent COD was 200 mg/L.