A once every eight-week cabotegravir (CAB) long-acting parenteral is more effective than daily oral emtricitabine and tenofovir disoproxil fumarate in preventing human immunodeficiency virus type one (HIV-1) transmission. Extending CAB dosing to a yearly injectable advances efforts for the elimination of viral transmission. Here we report rigor, reproducibility and mechanistic insights for a year-long CAB injectable. Pharmacokinetic (PK) profiles of this nanoformulated CAB prodrug (NM2CAB) are affirmed at three independent research laboratories. PK profiles in mice and rats show plasma CAB levels at or above the protein-adjusted 90% inhibitory concentration for a year after a single dose. Sustained native and prodrug concentrations are at the muscle injection site and in lymphoid tissues. The results parallel NM2CAB uptake and retention in human macrophages. NM2CAB nanocrystals are stable in blood and tissue homogenates. The long apparent drug half-life follows pH-dependent prodrug hydrolysis upon slow prodrug nanocrystal dissolution and absorption. In contrast, solubilized prodrug is hydrolyzed in hours in plasma and tissues from multiple mammalian species. No toxicities are observed in animals. These results affirm the pharmacological properties and extended apparent half-life for a nanoformulated CAB prodrug. The report serves to support the mechanistic design for drug formulation safety, rigor and reproducibility.

A strategy for amide C–N bond activation with ruthenium catalyst is described for the first time. The in situ formed bis-cycloruthenated complexes were demonstrated to be the key active species with superior oxidative addition ability to an inert amide C–N bond. The direct C–H bond activation of 2-arylpyridines followed by the amide C–N bond activation took place in the presence of a ruthenium precatalyst to produce monoacylation products in moderate to good yields. Synthetically useful functional groups, such as halogen atoms (F and Cl), ester, acetyl, and vinyl, remained intact during tandem C–H/C–N bond activation reactions.

Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.

A single, once every eight-week cabotegravir (CAB) long-acting parenteral is more effective than daily oral emtricitabine and tenofovir disoproxil fumarate in preventing human immunodeficiency virus type one (HIV-1) transmission. Extending CAB dosing to a yearly injectable can advance efforts leading to the elimination of viral transmission. The current submission adds rigor, reproducibility and mechanistic insights for the extended apparent half-life of a yearlong antiretroviral injectable. Pharmacokinetic (PK) profiles of a nanoformulated fatty acid ester CAB prodrug (named NM2CAB) were affirmed at two academic and one contract research laboratory. PK profiles showed plasma CAB levels at or above the protein-adjusted 90% inhibitory concentration for up to one year after a single dose. Measures of drug biodistribution demonstrated sustained native drug at the muscle injection site and in lymphoid tissues (spleen and lymph nodes). The results paralleled NM2CAB uptake and retention in human macrophages. NM2CAB nanocrystals were stable in blood, tissue and liver homogenates. The long apparent drug half-life followed pH-dependent slow prodrug release in weeks from the nanocrystal. In contrast, solubilized prodrug was hydrolyzed in hours in plasma and tissues recorded from multiple mammalian species at basic pH. No measurable toxicities were recorded. These results, taken together, affirm the pharmacological mechanistic properties of a year-long nanoformulated CAB prodrug supporting the established protocol design for formulation safety, rigor and reproducibility.

The Ir-catalyzed asymmetric hydrogenation of cyclic pyridinium salts is presented as a new strategy for the convenient and efficient synthesis of chiral indolizidines. The asymmetric hydrogenation of cyclic pyridinium salts derived from 2-(2-acylphenyl)pyridines proceeded smoothly in the presence of [Ir(cod)Cl] 2 and (R)-DM-SegPhos to provide the desired chiral 7,8-benzoindolizidines 6 in high to excellent yields with moderate enantioselectivity (up to 86:14 er) and excellent diastereoselectivity (>20:1 dr). The enantiomeric purity of 6j was increased to 92:8 through recrystallization.

BACKGROUND Hepatobiliary diseases result in the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues which may contribute to an unfavorable prognosis. AIM To discover and validate diagnostic biomarkers of cholestatic liver diseases based on the urinary BA profile. METHODS We analyzed urine samples by liquid chromatography-tandem mass spectrometry and compared the urinary BA profile between 300 patients with hepatobiliary diseases vs 103 healthy controls by statistical analysis. The BA profile was characterized using BA indices, which quantifies the composition, metabolism, hydrophilicity, and toxicity of the BA profile. BA indices have much lower inter- and intra-individual variability compared to absolute concentrations of BA. In addition, BA indices demonstrate high area under the receiver operating characteristic curves, and changes of BA indices are associated with the risk of having a liver disease, which demonstrates their use as diagnostic biomarkers for cholestatic liver diseases. RESULTS Total and individual BA concentrations were higher in all patients. The percentage of secondary BA (lithocholic acid and deoxycholic acid) was significantly lower, while the percentage of primary BA (chenodeoxycholic acid, cholic acid, and hyocholic acid) was markedly higher in patients compared to controls. In addition, the percentage of taurine-amidation was higher in patients than controls. The increase in the non-12α-OH BA was more profound than 12α-OH BA (cholic acid and deoxycholic acid) causing a decrease in the 12α-OH/ non-12α-OH ratio in patients. This trend was stronger in patients with more advanced liver diseases as reflected by the model for end-stage liver disease score and the presence of hepatic decompensation. The percentage of sulfation was also higher in patients with more severe forms of liver diseases. CONCLUSION BA indices have much lower inter- and intra-individual variability compared to absolute BA concentrations and changes of BA indices are associated with the risk of developing liver diseases.

Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.

Background Hepatocyte nuclear factor 4α (HNF4α) and glucocorticoid receptor (GR), master regulators of liver metabolism, are down-regulated in fatty liver diseases. The present study aimed to elucidate the role of down-regulation of HNF4α and GR in fatty liver and hyperlipidemia. Methods Adult mice with liver-specific heterozygote (HET) and knockout (KO) of HNF4α or GR were fed a high-fat-high-sugar diet (HFHS) for 15 days. Alterations in hepatic and circulating lipids were determined with analytical kits, and changes in hepatic mRNA and protein expression in these mice were quantified by real-time PCR and Western blotting. Serum and hepatic levels of bile acids were quantified by LC-MS/MS. The roles of HNF4α and GR in regulating hepatic gene expression were determined using luciferase reporter assays. Results Compared to HFHS-fed wildtype mice, HNF4α HET mice had down-regulation of lipid catabolic genes, induction of lipogenic genes, and increased hepatic and blood levels of lipids, whereas HNF4α KO mice had fatty liver but mild hypolipidemia, down-regulation of lipid-efflux genes, and induction of genes for uptake, synthesis, and storage of lipids. Serum levels of chenodeoxycholic acid and deoxycholic acid tended to be decreased in the HNF4α HET mice but dramatically increased in the HNF4α KO mice, which was associated with marked down-regulation of cytochrome P450 7a1, the rate-limiting enzyme for bile acid synthesis. Hepatic mRNA and protein expression of sterol-regulatory-element-binding protein-1 (SREBP-1), a master lipogenic regulator, was induced in HFHS-fed HNF4α HET mice. In reporter assays, HNF4α cooperated with the corepressor small heterodimer partner to potently inhibit the transactivation of mouse and human SREBP-1C promoter by liver X receptor. Hepatic nuclear GR proteins tended to be decreased in the HNF4α KO mice. HFHS-fed mice with liver-specific KO of GR had increased hepatic lipids and induction of SREBP-1C and PPARγ, which was associated with a marked decrease in hepatic levels of HNF4α proteins in these mice. In reporter assays, GR and HNF4α synergistically/additively induced lipid catabolic genes. Conclusions induction of lipid catabolic genes and suppression of lipogenic genes by HNF4α and GR may mediate the early resistance to HFHS-induced fatty liver and hyperlipidemia. Graphical abstract