Background:The aim of this study was to synthesize Gd 3+ -based silica nanoparticles that conjugate easily with glucosamine and to investigate their use as a nanoprobe for detection of human fibrosarcoma cells. Methods: Based on the structure of the 2-fluoro-2-deoxy-D-glucose molecule ( 18 FDG), a new compound consisting of D-glucose (1.1 nm) was conjugated with a Gd 3+ -based mesoporous silica nanoparticle using an N-5-azido-2-nitrobenzoyloxy succinimide (ANB-NOS) crosslinker. The contrast agent obtained was characterized using a variety of methods, including Fourier transform infrared spectroscopy, nitrogen physisorption, thermogravimetric analysis, scanning and transmission electron microscopy, and inductively coupled plasma atomic emission spectrometry (ICP-AES). In vitro studies included cell toxicity, apoptosis, tumor necrosis factor-alpha, and hexokinase assays, and in vivo tests consisted of evaluation of blood glucose levels using the contrast compound and tumor imaging. The cellular uptake study was validated using ICP-AES. Magnetic resonance relaxivity of the contrast agent was determined using a 1.5 Tesla scanner. Results: ANB-NOS was found to be the preferred linker for attaching glucosamine onto the surface of the mesoporous silica nanospheres.
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Two-dimensional transition-metal
dichalcogenide monolayers have
remarkably large optical nonlinearity. However, the nonlinear optical
conversion efficiency in monolayer transition-metal dichalcogenides
is typically low due to small light–matter interaction length
at the atomic thickness, which significantly obstructs their applications.
Here, for the first time, we report broadband (up to ∼150 nm)
enhancement of optical nonlinearity in monolayer MoS
2
with
plasmonic structures. Substantial enhancement of four-wave mixing
is demonstrated with the enhancement factor up to three orders of
magnitude for broadband frequency conversion, covering the major visible
spectral region. The equivalent third-order nonlinearity of the hybrid
MoS
2
-plasmonic structure is in the order of 10
–17
m
2
/V
2
, far superior (∼10–100-times
larger) to the widely used conventional bulk materials (e.g., LiNbO
3
, BBO) and nanomaterials (e.g., gold nanofilms). Such a considerable
and broadband enhancement arises from the strongly confined electric
field in the plasmonic structure, promising for numerous nonlinear
photonic applications of two-dimensional materials.
Finding potent compounds for a given target in silico can be viewed as a constraint global optimization problem. This requires the use of an optimization function for which evaluations might be costly. The major task is maximizing the function while minimizing the number of evaluation steps. To solve this problem, we propose a machine learning algorithm, which first builds a statistical QSAR-model of the SAR landscape and then uses the model to identify regions in compound space having a high probability to contain a highly potent compound. For this purpose, we devise the so-called expected potency improvement (EI) criterion to rank candidate compounds with respect to their likelihood to exhibit higher potency than the most active compound in the training data. Therefore, this approach significantly differs from a purely prediction-oriented classical QSAR model. The method is superior to a nearest neighbor approach as significantly fewer evaluation steps are needed to identify the most potent compound for the given target.
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