Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

Subgraph spotting in graph representations of comic book images

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Graph-based representations are the most powerful data structures for extracting, representing and preserving the structural information of underlying data. Subgraph spotting is an interesting research problem, especially for studying and investigating the structural information based content-based image retrieval (CBIR) and query by example (QBE) in image databases. In this paper we address the problem of lack of freely available ground-truthed datasets for subgraph spotting and present a new dataset for subgraph spotting in graph representations of comic book images (SSGCI) with its ground-truth and evaluation protocol. Experimental results of two state-of-the-art methods of subgraph spotting are presented on the new SSGCI dataset.University of La Rochelle (France

Polyelectrolyte multilayer films with pegylated polypeptides as a new type of anti-microbial protection for biomaterials.

Adhesion of bacteria at the surface of implanted materials is the first step in microbial infection, leading to post-surgical complications. In order to reduce this adhesion, we show that poly(L-lysine)/poly(L-glutamic acid) (PLL/PGA) multilayers ending by several PLL/PGA-g-PEG bilayers can be used, PGA-g-PEG corresponding to PGA grafted by poly(ethylene glycol). Streaming potential and quartz crystal microbalance-dissipation measurements were used to characterize the buildup of these films. The multilayer films terminated by PGA and PGA-g-PEG were found to adsorb an extremely small amount of serum proteins as compared to a bare silica surface but the PGA ending films do not reduce bacterial adhesion. On the other hand, the adhesion of Escherichia coli bacteria is reduced by 72% on films ending by one (PLL/PGA-g-PEG) bilayer and by 92% for films ending by three (PLL/PGA-g-PEG) bilayers compared to bare substrate. Thus, our results show the ability of PGA-g-PEG to be inserted into multilayer films and to drastically reduce both protein adsorption and bacterial adhesion. This kind of anti-adhesive films represents a new and very simple method to coat any type of biomaterials for protection against bacterial adhesion and therefore limiting its pathological consequences.comparative studyevaluation studiesjournal articleresearch support, non-u.s. gov't2004 Mayimporte

Click-modified cyclodextrins as non-viral vectors for neuronal siRNA delivery

RNA interference (RNAi) holds great promise as a strategy to further our understanding of gene function in the central nervous system (CNS) and as a therapeutic approach for neurological and neurodegenerative diseases. However, the potential for its use is hampered by the lack of siRNA delivery vectors, which are both safe and highly efficient. Cyclodextrins have been shown to be efficient and low toxicity gene delivery vectors in various cell types in vitro. However, to date they have not been exploited for delivery of oligonucleotides to neurons. To this end, a modified β-cyclodextrin (CD) vector was synthesised, which complexed siRNA to form cationic nanoparticles of less than 200nm in size. Furthermore, it conferred stability in serum to the siRNA cargo. The in vitro performance of the CD in both immortalised hypothalamic neurons and primary hippocampal neurons was evaluated. The CD facilitated high levels of intracellular delivery of labelled siRNA, whilst maintaining at least 80% cell viability. Significant gene knockdown was achieved, with a reduction in luciferase expression of up to 68% and a reduction in endogenous glyceraldehyde phosphate dehydrogenase (GAPDH) expression of up to 40%. To our knowledge, this is the first time that a modified CD has been used as a safe and efficacious vector for siRNA delivery into neuronal cells

Early-stage development of novel cyclodextrin-siRNA nanocomplexes allows for successful postnebulization transfection of bronchial epithelial cells.

BACKGROUND: Successful delivery of small interfering RNA (siRNA) to the lungs remains hampered by poor intracellular delivery, vector-mediated cytotoxicity, and an inability to withstand nebulization. Recently, a novel cyclodextrin (CD), SC12CDClickpropylamine, consisting of distinct lipophilic and cationic subunits, has been shown to transfect a number of cell types. However, the suitability of this vector for pulmonary siRNA delivery has not been assessed to date. To address this, a series of high-content analysis (HCA) and postnebulization assays were devised to determine the potential for CD-siRNA delivery to the lungs. METHODS: SC12CDClickpropylamine-siRNA mass ratios (MRs) were examined for size and zeta potential. In-depth analysis of nanocomplex uptake and toxicity in Calu-3 bronchial epithelial cells was examined using IN Cell(®) HCA assays. Nebulized SC12CDClickpropylamine nanocomplexes were assessed for volumetric median diameter (VMD) and fine particle fraction (FPF) and compared with saline controls. Finally, postnebulization stability was determined by comparing luciferase knockdown elicited by SC12CDClickpropylamine nanocomplexes before and after nebulization. RESULTS: SC12CDClickpropylamine-siRNA complexation formed cationic nanocomplexes of ≤200 nm in size depending on the medium and led to significantly higher levels of siRNA associated with Calu-3 cells compared with RNAiFect-siRNA-treated cells at all MRs (p CONCLUSIONS: SC12CDClickpropylamine nanocomplexes can be effectively nebulized for pulmonary delivery of siRNA using Aeroneb technology to mediate knockdown in airway cells. To the best of our knowledge, this is the first study examining the suitability of SC12CDClickpropylamine-siRNA nanocomplexes for pulmonary delivery. Furthermore, this work provides an integrated nanomedicine-device combination for future in vitro and in vivo preclinical and clinical studies of inhaled siRNA therapeutics