Accelerating protein release from microparticles for regenerative medicine applications

There is a need to control the spatio-temporal release kinetics of growth factors in order to mitigate current usage of high doses. A novel delivery system, capable of providing both structural support and controlled release kinetics, has been developed from PLGA microparticles. The inclusion of a hydrophilic PLGA–PEG–PLGA triblock copolymer altered release kinetics such that they were decoupled from polymer degradation. A quasi zero order release profile over four weeks was produced using 10% w/w PLGA–PEG–PLGA with 50:50 PLGA whereas complete and sustained release was achieved over ten days using 30% w/w PLGA–PEG–PLGA with 85:15 PLGA and over four days using 30% w/w PLGA–PEG–PLGA with 50:50 PLGA. These three formulations are promising candidates for delivery of growth factors such as BMP-2, PDGF and VEGF. Release profiles were also modified by mixing microparticles of two different formulations providing another route, not previously reported, for controlling release kinetics. This system provides customisable, localised and controlled delivery with adjustable release profiles, which will improve the efficacy and safety of recombinant growth factor delivery.     

Chitosan‐based hydrogels: recent design concepts to tailor properties and functions

Chitosan (CS) has received much attention as a functional biopolymer for designing various hydrogels for biomedical applications. This review provides an overview of the different types of CS‐based hydrogels, the approaches that can be used to fabricate hydrogel matrices with specific features and their applications in controlled drug delivery and tissue engineering. Emphasis is laid on the recent design concepts of hybrid hydrogels based on mixtures of CS and natural or synthetic polymers, interpenetrating polymer networks as well as composite hydrogels prepared by embedding nanoparticles into CS matrices. © 2017 Society of Chemical Industry     

Driving behaviors in early stage dementia: A study using in-vehicle technology

According to the Alzheimer's Association (2011), (1) in 8 people age 65 and older, and about one-half of people age 85 and older, have Alzheimer's disease in the United States (US). There is evidence that drivers with Alzheimer's disease and related dementias are at an increased risk for unsafe driving. Recent advances in sensor, computer, and telecommunication technologies provide a method for automatically collecting detailed, objective information about the driving performance of drivers, including those with early stage dementia. The objective of this project was to use in-vehicle technology to describe a set of driving behaviors that may be common in individuals with early stage dementia (i.e., a diagnosis of memory loss) and compare these behaviors to a group of drivers without cognitive impairment. Seventeen drivers with a diagnosis of early stage dementia, who had completed a comprehensive driving assessment and were cleared to drive, participated in the study. Participants had their vehicles instrumented with a suite of sensors and a data acquisition system, and drove 1–2 months as they would under normal circumstances. Data from the in-vehicle instrumentation were reduced and analyzed, using a set of algorithms/heuristics developed by the research team. Data from the early stage dementia group were compared to similar data from an existing dataset of 26 older drivers without dementia. The early stage dementia group was found to have significantly restricted driving space relative to the comparison group. At the same time, the early stage dementia group (which had been previously cleared by an occupational therapist as safe to drive) drove as safely as the comparison group. Few safety-related behavioral errors were found for either group. Wayfinding problems were rare among both groups, but the early stage dementia group was significantly more likely to get lost.     

Fluorescent Probe DCVJ Shows High Sensitivity for Characterization of Amyloid β-Peptide Early in the Lag Phase

The aggregation of intrinsically disordered and misfolded proteins in the form of oligomers and fibrils plays a crucial role in a number of neurological and neurodegenerative diseases. Currently, most probes and biophysical techniques that detect and characterize fibrils at high resolution fail to show sensitivity and binding for oligomers. Here, we show that 9-(dicyano-vinyl)julolidine (DCVJ), a class of molecular rotor, binds amyloid beta (Aβ) early aggregates, and we report the kinetics as well as packing of the oligomer formation. The binding of DCVJ to Aβ40 increased its emission intensity with time at 510 nm and produced a second excimer peak at 575 nm. However, DCVJ did not bind to the prefibrillar aggregates of Aβ42, which indicated that the oligomers formed by Aβ40 and Aβ42 were not the same. The F4C F19W mutant of Aβ40, which did not form fibrils, also bound DCVJ, but the emission spectral profile varied from that of the wild-type (WT). Atomic force microscopy images of WT Aβ40, the F4C F19W mutant, and Aβ42 oligomers displayed differences in size and shape, confirming the difference in their DCVJ spectra. The effect of epigallocatechin-3-gallate (EGCG) on the reduction of Aβ42 fibrils was also observed with finer detail than with other techniques. The results of this study show that DCVJ detects early aggregates and provides valuable information regarding the oligomer kinetics, packing, and mechanism of formation.     

Use of cryogenic machining to improve the adhesion of sphene bioceramic coatings on titanium substrates for dental and orthopaedic applications

Rods of commercially pure titanium were machined using standard oil-based emulsion and cryogenic cooling, and were then coated with sphene (CaTiSiO₅) bioceramic by spray coating using an automatic airbrush. The sphene bioceramic was synthesized in-situ starting from a suspension of polysiloxane that used as SiO₂ precursor, CaCO₃ and TiO₂ nanoparticles. The suspension was deposited on the machined substrates, which were heat treated up to 950?°C in order to promote the formation of sphene ceramic. The produced coated prototypes were characterized to evaluate the effect of the machining conditions on surface roughness and microstructure of the substrate, and thereby their effect on coating adhesion. Nanoindentation tests were employed to determine the hardness and elastic modulus of the coating through its thickness. Results showed that the reduced amount of defects on the surface of the cryo-machined substrates, contributed to increase the hardness, elastic modulus and adhesion strength of the coating-substrate interfaces compared to standard machined samples, therefore improving adhesion of the coating to the underlying substrate.     

A decomposition-based approach for the selection of standardized modular containers

The standardisation of packaging and containers for product handling has potential benefits from transportation, material handling and environmental perspectives. Likewise, standardised modular containers play an important role in the Physical Internet (PI). To support implementation of standard modular containers, we introduce a mathematical model to select a requisite number of modular containers to pack a set of products to maximise space utilisation. A decomposition-based solution methodology is developed and presented. Computational results from industry-based problem instances indicate that using standardised modular containers results in increased space utilisation at the unit load level. This finding substantiates the use of modular containers in moving toward the PI.     

Temporal generalization of sub-pixel vegetation mapping with multiple machine learning and atmospheric correction algorithms

Temporal generalization allows a trained classification algorithm to be applied to multiple images across time to derive reliable classification map products. It is a challenging remote-sensing research topic since the results are dependent on the selection of atmospheric correction methods, classification algorithms, validation processes, and their varying combinations. This study examined the temporal generalization of sub-pixel vegetation mapping using multiple Landsat images (1990, 1996, 2004, and 2010). All Landsat images were processed with two atmospheric correction methods: simple dark object subtraction (DOS) and the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm. For the sub-pixel vegetation mapping of the 2004 Landsat image, we used high-resolution OrbView-3 images as a training/validation data set and compared three machine learning algorithms (neural networks, random forests, and classification and regression trees) for their classification performance. The trained classifiers were then applied to other Landsat images (1990, 1996, and 2010) to derive sub-pixel vegetation map products. For the 2004 Landsat image classification, cross-validation shows similar classification results for neural networks (root mean square error (RMSE) = 0.099) and random forests (RMSE = 0.100) algorithms, and both are better than classification and regression trees (RMSE = 0.123). Pseudo-invariant pixels between 2004 and 2010 were used as validation points to evaluate the temporal generalizability of classification algorithms. Simple DOS and LEDAPS atmospheric correction resulted in similar accuracy statistics. The neural-network-based classifier performed best in generating reliable sub-pixel vegetation map products across time.     

Zinc supplementation of Friesian cows: Effect on chemical-nutritional composition and aromatic profile of dairy products

Zinc represents an essential microelement for several biochemical mechanisms. The body's inability to store zinc necessarily requires a constant dietary supply to avoid alteration of physiological functions. The aim of the present study was to investigate the effect of dietary enrichment with zinc on chemical-nutritional and aromatic properties of milk and cheese. Thirty commercial dairy cows, balanced for parity, milk production, and days in milk, were randomly assigned to 2 groups. The control group was fed with a conventional complete diet (22 kg of dry matter/animal per day), whereas the experimental group received a daily zinc supplementation of 60 mg per kg of dry complete feed. During the experimental period, the milk yield was monitored and samples of milk and caciotta cheese were collected to obtain information about the chemical-nutritional composition and aromatic profile. Dietary zinc integration did not influence milk yield and composition, but induced a marked reduction of somatic cell count and improved the oxidative stability of ripened caciotta cheese. In both milk and cheese, the experimental group samples were characterized by a lower concentration of saturated fatty acids and an increase in oleic acid, vaccenic acid, and rumenic acid. The aromatic profile of dairy products was also positively affected by dietary zinc intake, with an increase in concentration of carboxylic acids, aldehydes, and esters. The present results suggest a positive role of zinc in improving animal health and nutraceutical properties of milk and corresponding cheese. Taking into account the analysis of volatile compounds, zinc dietary supplementation of dairy cows should contribute to the production of cheeses with interesting organoleptic properties, although more studies are necessary to confirm the consumer acceptability of these changes.