Endothelial progenitor cell secretome delivered by novel polymeric nanoparticles in ischemic hindlimb.

Endothelial progenitor cells (EPCs) contribute to ischemic tissue repair by paracrine secretion up-regulated by hypoxia. In this study we use novel nanoparticles (NPs) as carriers for a controlled release of EPC secretome (CM) to improve their angiogenic properties. The in vivo effect in ischemic hindlimb rat model was evaluated, comparing hypoxic EPC-CM-NPs with hypoxic EPC-CM alone. A proteomic characterization of hypoxic CM and the in vitro effect on endothelial cells (HUVECs) were also performed. Up to 647 protein, 17 of which with angiogenic properties, were upregulated by hypoxia. Moreover, hypoxic EPC-CM significantly promoted capillary-like structures on Matrigel. A significant increase of blood perfusion in ischemic limbs at 2 weeks with EPC-CM-loaded NPs as compared to both EPC-CM and control and a significant increase of capillary formation were observed. The use of EPC-CM-NPs significantly improved neoangiogenesis in vivo, underlining the advantages of controlled release in regenerative medicine.

QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random Copolymers.

The glass transition temperature (Tg ) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing predefined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275 copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9 K, a standard error of prediction (S) of 6.1 K and a squared correlation coefficient (R(2) ) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting.

Size effects in nanoindentation of hard and soft surfaces.

Nanoindentation experiments carried out with atomic force microscopes (AFMs) open the way to understand size-related mechanical effects that are not present at the macro- or micro-scale. Several issues, currently the subject of a wide and open debate, must be carefully considered in order to measure quantities and retrieve trends genuinely associated with the material behaviour. The shape of the nanoindenter (the AFM tip) is crucial for a correct data analysis; we have recently developed a simple geometrical model to properly describe the tip effect in the nanoindentation process. Here, we demonstrate that this model is valid in indentation of both soft and hard, or relatively hard, materials carried out by two distinct, commercially available, AFM probes. Moreover, we implement the model with a data interpretation approach aimed at preventing underestimation of the tip penetration into the material. Experiments on soft polymeric materials (poly(methyl methacrylate) and polystyrene) and hard or relatively hard (Si, Au, Al) materials are reported. The results demonstrate that true hardness data can be attained also in shallow indentations and that the appearance of size effects strongly depends on data interpretation issues. In addition, we report on stiffness data measured on the considered materials during their nanoindentation.

Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks.

This paper reports some recent results from the empirical evaluation of different types of structured molecular representations used in QSPR analysis through a recursive neural network (RNN) model, which allows for their direct use without the need for measuring or computing molecular descriptors. This RNN methodology has been applied to the prediction of the properties of small molecules and polymers. In particular, three different descriptions of cyclic moieties, namely group, template and cyclebreak have been proposed. The effectiveness of the proposed method in dealing with different representations of chemical structures, either specifically designed or of more general use, has been demonstrated by its application to data sets encompassing various types of cyclic structures. For each class of experiments a test set with data that were not used for the development of the model was used for validation, and the comparisons have been based on the test results. The reported results highlight the flexibility of the RNN in directly treating different classes of structured input data without using input descriptors.

An atomic force microscopy tip model for investigating the mechanical properties of materials at the nanoscale.

Investigation of the mechanical properties of materials at the nanoscale is often performed by atomic force microscopy nanoindentation. However, substrates with large surface roughness and heterogeneity demand careful data analysis. This requirement is even more stringent when surface indentations with a typical depth of a few nanometers are produced to test material hardness. Accordingly, we developed a geometrical model of the nanoindenter, which was first validated by measurements on a reference gold sample. Then we used this technique to investigate the mechanical properties of a coating layer made of Balinit C, a commercially available alloy with superior anti-wear features deposited on steel. The reported results support the feasibility of reliable hardness measurements with truly nanosized indents.

AFM characterization of rabbit spermatozoa.

Atomic force microscopy (AFM) has been applied for determining the topological and structural features of rabbit spermatozoa. Fresh ejaculated spermatozoa were adsorbed passively onto a silicon slide or by motility from suspension onto a poly(L-lysine)-coated glass coverslip and then imaged in air and in buffer saline, respectively. AFM images clearly highlighted many details of spermatozoa head, neck, and tail. Distinct features were observed in the plasmatic membrane of spermatozoa. In particular, head topography easily recognized the acrosome, equatorial segment, equatorial subsegment, and postacrosome regions. Moreover, AFM images revealed the presence of double belt of invaginations around the spermatozoa head, at the boundary between equatorial subsegment and postacrosome regions. All together, the collected AFM images clearly defined a detailed map of spermatozoa morphology while giving some hints on the internal structure.

Ionic peptide aggregation: exploration of conformational dynamics in aqueous solution by computational techniques.

The effects of end groups on KEK peptide conformational characteristics and self-assembling properties in water solution are investigated by using long lasting all-atom molecular dynamics simulations. The analysis of the structural macroscopic and microscopic properties and the examination of intra- and intermolecular interactions suggest, in agreement with experimental observations, the role played by side chains and terminal regions in determining the characteristic features of the assemblages. Competition between intra- and interchain interactions greatly affects the diffusivity of peptide molecules and the conformational space that they can sample, ultimately controlling the shape, size, and distribution of the aggregate configurations. Different peptide end groups influence peptide flexibility and seem to play a crucial role in determining the aggregates' supramolecular architectures.

Bioerodible polymeric nanoparticles for targeted delivery of proteic drugs.

Significant efforts are being devoted to develop nanotechnology for drug delivery, mainly because of the distinct advantages offered by nanometer-size polymeric systems. Moreover, targeted drug delivery can be obtained by polymer conjugation to biospecific ligands. The present investigation was aimed mainly at determining the targeting ability of hybrid nanoparticles based on synthetic polymer/protein hybrid matrices. These nanoparticles were designed for liver targeted release of proteic drugs with antiviral activity, such as alpha-interferon. Human serum albumin and the monoesters of alternating copolymers of maleic anhydride/alkyl vinyl ethers of oligo(ethylene glycol) were selected as proteic and synthetic components, respectively. Digalactosyl diacyl glycerol, a natural glycolipid selectively recognized by the asialofetuin receptor present on liver hepatocytes was used as active targeting agent. Nanoparticles of 100-300 nm average size were obtained by controlled coprecipitation method. Investigation of nanoparticle surface properties by spectroscopic analysis and by biological tests indicated that the synthesized nanoparticles do expose on their surface targeting moieties that selectively interact with liver hepatocytes receptors.

New perspectives for (S)-dolichol and (S)- nor dolichol synthesis and biological functions.

A procedure is described for the preparation of (S)-dolichol and (S)- nor dolichol starting from the polyprenyl fraction extracted from Gingko biloba integer or exhausted leaves. The procedure appears extremely valuable in obtaining the two chiral isoprenoid compounds in good chemical yields and retention of a high degree of enantiomeric excess. Also, the (S)- nor dolichol represents a good chiral precursor for the preparation of (14)C-labelled (S)-dolichol to be used in biological investigations into the (S)-dolichol catabolism in the functional living cell. Furthermore, the possible role of (S)-dolichol as a free radical scavenger in the cell membrane was preliminarily evaluated by means of a (1)H-NMR analytical method. Apparently, experimental results substantiate this hypothesis.