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Nano-particles have been combined with antibiotics in recent studies to overcome multidrug-resistant bacteria. Here, we synthesized a nano-material in which Ag nano-particles were assembled with a ZnO nano-structure to form an Ag-ZnO (AZO) nano-composite at low temperature. This material was combined with erythromycin (Ery), an antibiotic effective towards gram-positive bacteria, using three different approaches (AZO + Ery (AZE) [centrifuged (AZE1), used separately after 1-h gap (AZE2), without centrifugation (AZE3)]) to prepare a nano-antibiotic against clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA). X-ray diffraction analysis and transmission electron microscopy confirmed the presence of Ag nano-particles and ZnO nano-structure. The elemental and chemical state of the elements present in the AZO nano-composite were assessed by X-ray photoelectron spectroscopy. The antibacterial activity of AZE samples against both Escherichia coli and S. aureus strains including MRSA was evaluated in antibacterial and morphological analyses. The AZE3 sample showed greater antibacterial activity than the other samples and was comparable to erythromycin. AZE3 was ~20-fold less prone to developing bacterial resistance following multiple exposures to bacteria compared to erythromycin alone. The AZE3 nano-composite showed good biocompatibility with 293 human embryonic kidney cells. Our newly synthesized nano-platform antibiotics may be useful against multidrug-resistant gram-positive bacteria.The limited access to vaccination and vaccine hesitancy are prevalent even among health care providers in less developed countries. This study assessed the relationships between (i) health care providers' knowledge on human papillomavirus (HPV) and vaccination for HPV and (ii) their perceptions, demographic characteristics, and the use of information sources. In this large-scale online survey, participants (n = 1394) were recruited from hospitals of three big cities in China (Shanghai, Guangzhou, and Shenzhen). Descriptive analysis, the chi-square test, and logistic regression analysis were used to answer different research questions. Health care providers' overall knowledge scores are consistent with their perceptions about HPV and HPV vaccine. Occupation, specialty, the type of hospitals, and the frequency of participants' search for information using professional informational sources are the most significant characteristics that are closely associated with their knowledge of HPV and its vaccine. Targeted educational interventions are needed to improve health care providers' engagement in the promotion of the HPV vaccine. Such interventions, besides increasing knowledge, should also emphasize the importance of appropriate information sources to acquire information.Memantine (3,5-dimethyladamantan-1-amine) is an orally active, noncompetitive N-methyl-D-aspartate receptor (NMDAR) antagonist approved for treatment of moderate-to-severe Alzheimer's disease (AD), a neurodegenerative condition characterized by a progressive cognitive decline. KN62 Unfortunately, memantine as well as the other class of drugs licensed for AD treatment acting as acetylcholinesterase inhibitors (AChEIs), provide only symptomatic relief. Thus, the urgent need in AD drug development is for disease-modifying therapies that may require approaching targets from more than one path at once or multiple targets simultaneously. Indeed, increasing evidence suggests that the modulation of a single neurotransmitter system represents a reductive approach to face the complexity of AD. Memantine is viewed as a privileged NMDAR-directed structure, and therefore, represents the driving motif in the design of a variety of multi-target directed ligands (MTDLs). In this review, we present selected examples of small molecules recently designed as MTDLs to contrast AD, by combining in a single entity the amantadine core of memantine with the pharmacophoric features of known neuroprotectants, such as antioxidant agents, AChEIs and Aβ-aggregation inhibitors.Previous research has demonstrated large amounts of inter-subject variability in downward (unweighting & braking) phase strategies in the countermovement jump (CMJ). The purpose of this study was to characterize downward phase strategies and associated temporal, kinematic and kinetic CMJ variables. One hundred and seventy-eight NBA (National Basketball Association) players (23.6 ± 3.7 years, 200.3 ± 8.0 cm; 99.4 ± 11.7 kg; CMJ height 68.7 ± 7.4 cm) performed three maximal CMJs. Force plate and 3D motion capture data were integrated to obtain kinematic and kinetic outputs. Afterwards, athletes were split into clusters based on downward phase characteristics (k-means cluster analysis). Lower limb joint angular displacement (i.e., delta flexion) explained the highest portion of point variability (89.3%), and three clusters were recommended (Ball Hall Index). Delta flexion was significantly different between clusters and players were characterized as "stiff flexors", "hyper flexors", or "hip flexors". There were no significant differences in jump height between clusters (p > 0.05). Multiple regression analyses indicated that most of the jumping height variance was explained by the same four variables, (i.e., sum concentric relative force, knee extension velocity, knee extension acceleration, and height) regardless of the cluster (p less then 0.05). However, each cluster had its own unique set of secondary predictor variables.Recently, unmanned aerial vehicles (UAVs) have attracted much attention due to their on-demand deployment, high mobility, and low cost. For UAVs navigating in an unknown environment, efficient environment representation is needed due to the storage limitation of the UAVs. Nonetheless, building an accurate and compact environment representation model is highly non-trivial because of the unknown shape of the obstacles and the time-consuming operations such as finding and eliminating the environmental details. To overcome these challenges, a novel vertical strip extraction algorithm is proposed to analyze the probability density function characteristics of the normalized disparity value and segment the obstacles through an adaptive size sliding window. In addition, a plane adjustment algorithm is proposed to represent the obstacle surfaces as polygonal prism profiles while minimizing the redundant obstacle information. By combining these two proposed algorithms, the depth sensor data can be converted into the multi-layer polygonal prism models in real time.
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