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Five thioether-linked hydroxamate/quinazolinone hybrid structures were synthesized and tested for their anti-inflammatory activities. The obtained results indicated that compounds 7a-c and 7e showed the inhibition on LPS-stimulated NO production with the IC50 values ranging from 58.03 to 66.19 mM. Molecular docking results showed that all synthesized compounds displayed affinity towards the 5-LOX, MK2, P2Y12, 15-PGDH, and DNA polymerase receptors based on the observed low binding energies and interactions with the key amino acids in the binding sites of the enzymes. Noticeably, compound 7e exhibited as a potential compound targeting six receptors including 5-LOX, MK2, mPGES-1, P2Y12, 15-PGDH, and DNA polymerase receptors.

Aminoacyl-tRNA synthetases (aaRSs) are one of the leading targets for the development of antibiotic agents. In this paper, we reported the discovery of aaRS inhibitors using a structure-based virtual screening method. The interactions of 52 designed structures with the methionyl-tRNA synthetase (MetRS) target were performed by docking the ligands into the active zone of the MetRS using Autodock Vina. The data revealed 14 compounds displaying interactions with key amino acids (Asp287, Tyr250, Val473, Trp474, Phe522, Ile519, Ala477, Leu478, and His523) at the binding pocket of the enzyme, indicating their potential as MetRS inhibitors. These results could be served as the references for further synthetic work and bioassays experiments for discovering MetRS inhibitors and other pharmaceutical agents that may assist in the generation of new antibiotics.

Purpose The purpose of this study is to assess the factors that motivate young people’s intention to undergo cosmetic surgery in Vietnam. Design/methodology/approach This study applies Ajzen’s theory of planned behavior as a research model. The study is based on a quantitative method that applied exploratory factor analysis. Findings In total, 412 valid responses are used for the statistical analysis. The results confirm that subjective norm (SN), attitude toward cosmetic surgery and perceived behavioral control (PBC) are the main factors that motivate young people’s intention to undergo cosmetic surgery. Originality/value The results show the positive relationship between attitude toward cosmetic surgery, SN, PBC and intention to undergo cosmetic surgery in Vietnam.

Multiple myeloma is a deadly cancer that is a complex and multifactorial disease. In the present study, 12 belinostat derivatives (four resynthesized and eight new), HDAC inhibitors, were resynthesized

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Building in silico models to predict chemical properties and activities is a crucial step in drug discovery. However, drug discovery projects are often characterized by limited labeled data, hindering the applications of deep learning in this setting. Meanwhile advances in meta-learning have enabled state-of-the-art performances in few-shot learning benchmarks, naturally prompting the question: Can meta-learning improve deep learning performance in low-resource drug discovery projects? In this work, we assess the efficiency of the Model-Agnostic Meta-Learning (MAML) algorithm – along with its variants FO-MAML and ANIL – at learning to predict chemical properties and activities. Using the ChEMBL20 dataset to emulate low-resource settings, our benchmark shows that meta-initializations perform comparably to or outperform multi-task pre-training baselines on 16 out of 20 in-distribution tasks and on all out-of-distribution tasks, providing an average improvement in AUPRC of 7.2% and 14.9% respectively. Finally, we observe that meta-initializations consistently result in the best performing models across fine-tuning sets with k ∈ {16, 32, 64, 128, 256} instances.

Detection of bacterial metabolites at low concentrations in fluids with complex background allows for applications ranging from detecting biomarkers of respiratory infections to identifying contaminated medical instruments. Surface-enhanced Raman scattering (SERS) spectroscopy, when utilizing plasmonic nanogaps, has the relatively unique capacity to reach trace molecular detection limits in a label-free format, yet large-area device fabrication incorporating nanogaps with this level of performance has proven difficult. Here, we demonstrate the advantages of using chemical assembly to fabricate SERS surfaces with controlled nanometer gap spacings between plasmonic nanospheres. Control of nanogap spacings via the length of the chemical crosslinker provides uniform SERS signals, exhibiting detection of pyocyanin, a secondary metabolite of Pseudomonas aeruginosa, in aqueous media at concentration of 100 pg·mL-1. When using machine learning algorithms to analyze the SERS data of the conditioned medium from a bacterial culture, having a more complex background, we achieve 1 ng·mL-1 limit of detection of pyocyanin and robust quantification of concentration spanning 5 orders of magnitude. Nanogaps are also incorporated in an in-line microfluidic device, enabling longitudinal monitoring of P. aeruginosa biofilm formation via rapid pyocyanin detection in a medium effluent as early as 3 h after inoculation and quantification in under 9 h. Surface-attached bacteria exposed to a bactericidal antibiotic were differentially less susceptible after 10 h of growth, indicating that these devices may be useful for early intervention of bacterial infections.

Nanoparticles from colloidal solution-with controlled composition, size, and shape-serve as excellent building blocks for plasmonic devices and metasurfaces. However, understanding hierarchical driving forces affecting the geometry of oligomers and interparticle gap spacings is still needed to fabricate high-density architectures over large areas. Here, electrohydrodynamic (EHD) flow is used as a long-range driving force to enable carbodiimide cross-linking between nanospheres and produces oligomers exhibiting sub-nanometer gap spacing over mm2 areas. Anhydride linkers between nanospheres are observed via surface-enhanced Raman scattering (SERS) spectroscopy. The anhydride linkers are cleavable via nucleophilic substitution and enable placement of nucleophilic molecules in electromagnetic hotspots. Atomistic simulations elucidate that the transient attractive force provided by EHD flow is needed to provide a sufficient residence time for anhydride cross-linking to overcome slow reaction kinetics. This synergistic analysis shows assembly involves an interplay between long-range driving forces increasing nanoparticle-nanoparticle interactions and probability that ligands are in proximity to overcome activation energy barriers associated with short-range chemical reactions. Absorption spectroscopy and electromagnetic full-wave simulations show that variations in nanogap spacing have a greater influence on optical response than variations in close-packed oligomer geometry. The EHD flow-anhydride cross-linking assembly method enables close-packed oligomers with uniform gap spacings that produce uniform SERS enhancement factors. These results demonstrate the efficacy of colloidal driving forces to selectively enable chemical reactions leading to future assembly platforms for large-area nanodevices.

Surface enhanced Raman scattering (SERS) is a vibrational spectroscopy method that enables the quantification of the concentration of small molecules. SERS sensing has been demonstrated in a wide variety of applications, from explosive and drug detection, to monitoring of bacteria growth. Underpinning SERS sensing are the sensor surfaces that are composed of vast quantities of metal nanostructures which confine light into small gaps called “hotspots”, enhancing Raman scattering. While these surfaces are essential for increasing Raman scattering intensity so that analyte signal may be observed in small concentrations, they introduce signal variations due to spatial distributions of Raman enhancement and hotspot volume. In this work, we introduce a convolutional neural network model that improves concentration regressions in SERS sensors by learning the distributions of sensor surface dependent latent variables. We demonstrate that this model significantly improves predictions compared to a traditional multilayer perceptron approach, and that the model uses analyte spectral information and is capable of reasonable interpolations.

International audience; As blind audio source separation has remained very challenging in real-world scenarios, some existing works, including ours, have investigated the use of a weakly-informed approach where generic source spectral models (GSSM) can be learned a priori based on nonnegative matrix factorization (NMF). Such approach was derived for single-channel audio mixtures and shown to be efficient in different settings. This paper proposes a multichannel source separation approach where the GSSM is combined with the source spatial covariance model within a unified Gaussian modeling framework. We present the generalized expectation-minimization (EM) algorithm for the parameter estimation. Especially, for guiding the estimation of the intermediate source variances in each EM iteration, we investigate the use of two criteria: (1) the estimated variances of each source are constrained by NMF, and (2) the total variances of all sources are constrained by NMF altogether. While the former can be seen as a source variance denoising step, the latter is viewed as an additional separation step applied to the source variance. We demonstrate the speech separation performance, together with its convergence and stability with respect to parameter setting, of the proposed approach using a benchmark dataset provided within the 2016 Signal Separation Evaluation Campaign.

Pseudomonas aeruginosa is an opportunistic, biofilm-forming pathogen. P. aeruginosa produces pyocyanin, a secondary metabolite as part of its quorum sensing signaling system activated during biofilm formation. Self-organized plasmonic nanogaps provide enhancements to Raman scattering signals to achieve 10 ng/mL limit of detection of pyocyanin that enables early detection of biofilm formation. We report, for the first time, in-line biofilm detection in microfluidic channels as early as three hours from the onset of bacterial culturing and quantification spanning five-orders of magnitude of pyocyanin concentration during biofilm formation of PA14.