6-Benzyl-2-[(triphenyl-λ5-phosphanyl­idene)amino]-4,5,6,7-tetra­hydro­thieno[2,3-c]pyridine-3-carbonitrile

In the title compound, C33H28N3PS, the P atom has a distorted tetra­hedral PNC3 environment, formed by the N atom and three aryl rings. No inter­molecular hydrogen-bonding inter­actions or π–π stacking inter­actions are present in the crystal structure

Bis{N,N-bis­[(diphenyl­phosphan­yl)meth­yl]aniline-κ2 P,P′}copper(I) tetra­fluoridoborate

In the cation of the title compound, [Cu(C32H29NP2)2]BF4, the CuI atom is four-coordinated in a distorted tetra­hedral geometry by four P atoms from two N,N-bis­[(diphenyl­phosphan­yl)meth­yl]aniline ligands. In the crystal, the cations are linked by C—H⋯π inter­actions, forming chains along the a axis. Intra­molecular C—H⋯N and inter­molecular C—H⋯F hydrogen bonds are also observed

Online Preconcentration and Determination of Trace Amounts of Zinc in Nature Waters

A simple, sensitive, reliable and flexible flow injection spectrophotometric method is proposed for on-line preconcentration and determination of trace amounts of zinc in water. At the presence of Tween-80 in pH 9.3 buffer solutions, the shade of color of Zn (II)-PAN complex is in a linear relation to the zinc amount at the point of the maximum absorption peak of 560 nm. The optimal experimental conditions, including reaction conditions and preconcentration conditions, had been obtained. The linear range of the proposed method was between 2.0 and 360 μg L−1 and the detection limit was 0.42 μg L−1. The relative standard deviation was 3.55% and 2.14% for 5.0 μg L−1 and 50 μg L−1 of zinc standard solution (n = 8). The method had been successfully applied to zinc determination in water samples and the analytical results were satisfactory

Empowering AI drug discovery with explicit and implicit knowledge

Motivation: Recently, research on independently utilizing either explicit knowledge from knowledge graphs or implicit knowledge from biomedical literature for AI drug discovery has been growing rapidly. These approaches have greatly improved the prediction accuracy of AI models on multiple downstream tasks. However, integrating explicit and implicit knowledge independently hinders their understanding of molecules. Results: We propose DeepEIK, a unified deep learning framework that incorporates both explicit and implicit knowledge for AI drug discovery. We adopt feature fusion to process the multi-modal inputs, and leverage the attention mechanism to denoise the text information. Experiments show that DeepEIK significantly outperforms state-of-the-art methods on crucial tasks in AI drug discovery including drug-target interaction prediction, drug property prediction and protein-protein interaction prediction. Further studies show that benefiting from explicit and implicit knowledge, our framework achieves a deeper understanding of molecules and shows promising potential in facilitating drug discovery applications.Comment: Bioinformatic

7-Benzyl-3-(4-fluoro­phen­yl)-2-(pyrrol­idin-1-yl)-5,6,7,8-tetra­hydro­pyrido[4′,3′:4,5]thieno[2,3-d]pyrimidin-4(3H)-one

In the title compound, C26H25FN4OS, the thienopyrimidine fused-ring system is close to planar (r.m.s. deviation = 0.066 Å), with a maximum deviation of 0.1243 (17) Å for the N atom adjacent to the carbonyl group. This ring system forms dihedral angles of 67.5 (1) and 88.9 (1) ° with the adjacent six-membered rings. Inter­molecular C—H⋯O hydrogen bonding and C—H⋯π inter­actions help to stabilize the crystal structure

Optimizing the construction of ecological networks in Beijing using a morphological spatial pattern analysis—minimal cumulative resistance model

Introduction: With the increasing fragmentation of landscapes caused by rapid urbanisation, constructing ecological networks strengthen the connectivity between fragmented habitat patches. As the capital of China, Beijing has a rapid development, resulting in a serious landscape fragmentation, and needing an urgent demand for this study to improve the ecological network system.Methods: In this study, we choose the elevation, slope, Normalized Difference Vegetation Index and land use data of Beijing in 2020 as the data use. Morphological spatial pattern analysis (MSPA) was used to identify ecological source areas for Beijing, Minimal cumulative resistance (MCR) and gravity models were used to construct ecological network, and stepping stones to improve it.Results: The core area of Beijing had the highest proportion (96.17%) of all landscape types, forest accounting for 82.01% thereof. Ten core areas were identified as ecological source areas. Forty-five ecological corridors (8 major and 37 ordinary) were constructed. The ecological corridors are mainly concentrated in the middle and eastern regions where ecological mobility is limited. Constructing stepping stones would help uphold the region’s ecological service functions and ecosystem balance. Twenty-nine stepping stones and 32 ecological obstacles were used to create the optimised ecological network, consisting of 171.Discussion: The results provide an optimised ecological model for Beijing and a reference constructing ecological spatial networks for the sustainable development of ecological environments in high-density urban areas

Efficacy and safety of guselkumab for the treatment of patients with moderate-to-severe plaque psoriasis: A metaanalysis of randomized clinical trials

Purpose: To conduct a systematic analysis on data from randomized controlled trials (RCTs) on different doses of guselkumab, and provide high-quality evidence for its use in the treatment of patients with moderate-to-severe plaque psoriasis (PsO). Methods: Related studies were searched using online search engines including MEDLINE, PubMed, and central registry of Cochrane controlled trials from January 2001 to October 2017. Only randomized, placebo-controlled, double-blind clinical trials involving guselkumab- and placebo-treated PsO subjects were included. Results: Five eligible double-blind, randomized, and placebo-controlled trials involving patients with moderate-to-severe PsO subjects treated with guselkumab were included. Compared with the placebo groups, the proportion of patients with improvements in Psoriasis Area and Severity Index (PASI) 75 (RR= 12.14; 95% CI= 9.11-16.16; p < 0.001); PASI 90 (RR= 23.26; 95% CI =14.57-37.13; p < 0.001), and PASI 100 (RR = 37.66; 95% CI = 15.81-89.69; p < 0.001) were significantly higher than those in guselkumab-treated groups. Furthermore, the guselkumab-treated groups showed significant decreases in Physician’s Global Assessment (PGA) score (RR = 10.46; 95% CI = 7.96-13.83; p < 0.001) and the Dermatology Life Quality Index (DLQI) score (SMD = -1.3; 95% CL = -1.4 to -1.19; p < 0.001), when compared with the placebo groups. However, there were no significant differences in adverse events (AEs) (RR = 1.01; 95% CL = 0.93-1.11; p > 0.05); severe adverse events (SAEs) (RR = 1.32; 95% CI =0.69-2.54; p > 0.05) and study discontinuations (RR = 0.79; 95% CI = 0.42-1.48; p > 0.05) between the two groups. Conclusion: This meta-analysis summarizes available evidence for the use of guselkumab in psoriasis. The results suggest that guselkumab is superior to placebo in moderate-to-severe psoriasis, and is welltolerated, effective, and safe in improving the severity of disease and quality of life. Keywords: Guselkumab, Effectiveness, Safety, Plaque psoriasis, Meta-analysis, Quality of lif