Reduced Il17a expression distinguishes a Ly6cloMHCIIhi macrophage population promoting wound healing

Macrophages are the main components of inflammation during skin wound healing. They are critical in wound closure and in excessive inflammation, resulting in defective healing observed in chronic wounds. Given the heterogeneity of macrophage phenotypes and functions, we here hypothesized that different subpopulations of macrophages would have different and sometimes opposing effects on wound healing. Using multimarker flow cytometry and RNA expression array analyses on macrophage subpopulations from wound granulation tissue, we identified a Ly6cloMHCIIhi “noninflammatory” subset that increased both in absolute number and proportion during normal wound healing and was missing in Ob/Ob and MYD88−/− models of delayed healing. We also identified IL17 as the main cytokine distinguishing this population from proinflammatory macrophages and demonstrated that inhibition of IL17 by blocking Ab or in IL17A−/− mice accelerated normal and delayed healing. These findings dissect the complexity of the role and activity of the macrophages during wound inflammation and may contribute to the development of therapeutic approaches to restore healing in chronic wounds.

The effect of polystyrene sodium sulfonate grafting on polyethylene terephthalate artificial ligaments on in vitro mineralisation and in vivo bone tissue integration

This study investigates the impact of polystyrene sodium sulfonate (PolyNaSS) grafting onto the osseo-integration of a polyethylene terephthalate artificial ligament (Ligament Advanced Reinforcement System, LARS™) used for Anterior Cruciate Ligament (ACL). The performance of grafted and non-grafted ligaments was assessed in vitro by culturing human osteoblasts under osteogenic induction and this demonstrated that the surface modification was capable of up-regulating the secretion of ALP and induced higher level of mineralisation as measured 6 weeks post-seeding by Micro-Computed Tomography. Grafted and non-grafted LARS™ were subsequently implanted in an ovine model for ACL reconstruction and the ligament-to-bone interface was evaluated by histology and biomechanical testings 3 and 12 months post-implantation. The grafted ligaments exhibited more frequent direct ligament-to-bone contact and bone formation in the core of the ligament at the later time point than the non-grafted specimens, the grafting also significantly reduced the fibrous encapsulation of the ligament 12 months post-implantation. However, this improved osseo-integration was not translated into a significant increase in the biomechanical pull-out loads. These results provide evidences that PolyNaSS grafting improved the osseo-integration of the artificial ligament within the bone tunnels. This might positively influence the outcome of the surgical reconstructions, as higher ligament stability is believed to limit micro-movement and therefore permits earlier and enhanced healing.

Supramolecular organization in fluorene/indenofluorene-oligothiophene alternating conjugated copolymers

A series of conjugated copolymers containing fluorene or indenofluorene units alternating with oligothiophene segments, with potential interest for use as the active layer in field-effect transistors, is investigated. Atomic force microscopy analysis of the morphology of thin deposits shows either the formation of fibrillar structures, which are the signature of long-range π stacking, or the presence of untextured aggregates, resulting from disordered assembly. These morphologies are interpreted in terms of the supramolecular organization of the conjugated chains. Molecular modeling simulations indicate that the commensurability between the lengths of the monomer units and the presence of alkyl side groups are the two key structural factors governing the chain organization into highly ordered assemblies. The most favorable structures are those combining fluorene (indenofluorene) units with unsubstituted bithiophene (terthiophene) segments.

Hydrogen-bonded supramolecular polymers as self-healing hydrogels: Effect of a bulky adamantyl substituent in the ureido-pyrimidinone monomer

In an attempt to generate supramolecular assemblies able to function as self-healing hydrogels, a novel ureido-pyrimidinone (UPy) monomer, 2-(N ′-methacryloyloxyethylureido)-6-(1-adamantyl)-4[1H]-pyrimidinone, was synthesized and then copolymerized with N,N-dimethylacrylamide at four different feed compositions, using a solution of lithium chloride in N,N-dimethylacetamide as the polymerization medium. The assembling process in the resulting copolymers is based on crosslinking through the reversible quadruple hydrogen bonding between side-chain UPy modules. The adamantyl substituent was introduced in order to create a “hydrophobic pocket” that may protect the hydrogen bonds against the disruptive effect of water molecules. Upon hydration to equilibrium, all copolymers generated typical hydrogels when their concentration in the hydrated system was at least 15%. The small-deformation rheometry showed that all hydrated copolymers were hydrogels that maintained a solid-like behavior, and that their extrusion through a syringe needle did not affect significantly this behavior, suggesting a self-healing capacity in these materials. An application as injectable substitutes for the eye's vitreous humor was proposed

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.