Solar-driven strongly coupled plasmonic Au nanoarrays on mesoporous silica nanodisks enable selective fungal and bacterial inactivation in well water.

Well water is an important water source in isolated rural areas but easily suffers from microbial contamination. Herein, we anchored periodic Au nanoarrays on mesoporous silica nanodisks (Au-MSN) to fabricate a solar-driven nano-stove for well water disinfection. The solar/Au-MSN process completely inactivated 3.98, 6.55, 7.11 log10 cfu/mL, and 3.37 log10 pfu/mL of Aspergillus niger spores, Escherichia coli, chlorine-resistant Spingopyxis sp. BM1-1, and bacteriophage MS2 within 5 min, respectively. Moreover, the complete inactivation of various microorganisms (even at a viable but nonculturable state) was achieved in the flow-through reactor under natural solar light in real well water matrixes. Thorough characterizations and theoretical simulations verified that the densely anchoring strategy of Au-MSN's nanoarray worked on broadband absorption via the photon confinement effect, and trace amounts of Au can induce strong electromagnetic fields and collective localized heating. The resulting surge of 1O2 and heat synergically destroyed membranes, dysfunction cellular self-defense and metabolic system, induced intracellular oxidative stress, and ultimately inactivated microorganisms. Additionally, the 1O2-dominated oxidation and cell adhesion facilitated the selective disinfection in real well water matrixes. This study provides a cost-effective and practical solution for efficient well water disinfection, which assists isolated rural areas in getting safe drinking water.

Brain Activation During Working Memory Task in Amnestic Mild Cognitive Impairment Patients and Its Association with Memory and Attention.

BACKGROUND: Amnestic mild cognitive impairment (aMCI) is regarded as a transitional state of Alzheimer's disease, with working memory (WM) impairment. OBJECTIVE: To investigate the brain activity in aMCI patients during WM tasks with the functional near-infrared spectroscopy (fNIRS) technique, as well as explore the association between brain activity and cognitive function in multiple domains. METHODS: This study is a case-control study of 54 aMCI patients and 33 cognitively healthy elderly (NC). All participants underwent neuropsychological assessments. fNIRS was applied to examine the brain activation during the WM task. Multivariable linear regression analysis was applied to evaluate associations between brain activation and cognitive function in multiple domains. RESULTS: Compared to NC subjects, aMCI patients had lower activation in the bilateral prefrontal, parietal, and occipital cortex during the WM task. Additionally, activation in the left prefrontal, bilateral parietal, and occipital cortex during the encoding and maintenance phase was positively associated with memory function. During memory retrieval, higher activity in the left prefrontal, parietal, and occipital cortex were correlated with higher memory scores. Besides, a positive association also formed between attention function and the activation in the left prefrontal, parietal, and occipital cortex during the WM task. CONCLUSION: These findings demonstrated that reduced activation in the prefrontal, parietal and occipital cortex during WM might reflect the risk of cognitive impairment, especially memory and attention function in aMCI patients. Given the brain activation visualization, fNIRS may be a convenient and alternative tool for screening the risk of Alzheimer's disease.

Pharmacophore modeling and virtual screening studies of checkpoint kinase 1 inhibitors.

In this study, chemical feature-based 3-dimensional (3D) pharmacophore models of Checkpoint kinase 1 (Chk1) inhibitors were developed based on the known inhibitors of Chk1. The best pharmacophore model Hypo1 was characterized by the best correlation coefficient (0.9577), and the lowest root mean square deviation (0.8871). Hypo1 consists of one hydrogen-bond acceptor, one hydrogen-bond donor, and two hydrophobic features, as well as one excluded volume. This pharmacophore model was further validated by both test set and cross validation methods. A comparison analysis of Hypo1 with chemical features in the active site of Chk1 indicates that the pharmacophore model Hypo1 can correctly reflect the interactions between Chk1 and its ligands. Then Hypo1 was used to screen chemical databases, including Specs and Chinese Nature Product Database (CNPD) for potential lead compounds. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking study to refine the retrieved hits. Finally some of the most potent (estimated) compounds were selected from the final refined hits and suggested for further experimental investigation.

Towards more accurate pharmacophore modeling: Multicomplex-based comprehensive pharmacophore map and most-frequent-feature pharmacophore model of CDK2.

Pharmacophore modeling, including ligand- and structure-based approaches, has become an important tool in drug discovery. However, the ligand-based method often strongly depends on the training set selection, and the structure-based pharmacophore model is usually created based on apo structures or a single protein-ligand complex, which might miss some important information. In this study, multicomplex-based method has been suggested to generate a comprehensive pharmacophore map of cyclin-dependent kinase 2 (CDK2) based on a collection of 124 crystal structures of human CDK2-inhibitor complex. Our multicomplex-based comprehensive pharmacophore map contains almost all the chemical features important for CDK2-inhibitor interactions. A comparison with previously reported ligand-based pharmacophores has revealed that the ligand-based models are just a subset of our comprehensive map. Furthermore, one most-frequent-feature pharmacophore model consisting of the most frequent pharmacophore features was constructed based on the statistical frequency information provided by the comprehensive map. Validations to the most-frequent-feature model show that it can not only successfully discriminate between known CDK2 inhibitors and the molecules of focused inactive dataset, but also is capable of correctly predicting the activities of a wide variety of CDK2 inhibitors in an external active dataset. Obviously, this investigation provides some new ideas about how to develop a multicomplex-based pharmacophore model that can be used in virtual screening to discover novel potential lead compounds.