Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>Autologous cancellous-bone grafts are the current gold standard for therapeutic interventions in which bone-regeneration is desired. The main limitations of these implants are the need for a secondary surgical site, creating a wound on the patient, the limited availability of harvest-safe bone, and the lack of structural integrity of the grafts. Synthetic, resorbable, bone-regeneration materials could pose a viable treatment alternative, that could be implemented through 3D-printing. We present here the development of a polylactic acid-hydroxyapatite (PLA-HAp) composite that can be processed through a commercial-grade 3D-printer. We have shown that this material could be a viable option for the development of therapeutic implants for bone regeneration. Biocompatibility <jats:italic>in vitro</jats:italic> was demonstrated through cell viability studies using the osteoblastic MG63 cell-line, and we have also provided evidence that the presence of HAp in the polymer matrix enhances cell attachment and osteogenicity of the material. We have also provided guidelines for the optimal PLA-HAp ratio for this application, as well as further characterisation of the mechanical and thermal properties of the composite. This study encompasses the base for further research on the possibilities and safety of 3D-printable, polymer-based, resorbable composites for bone regeneration.</jats:p>

<jats:title>Abstract</jats:title> <jats:p> <jats:italic>Objective</jats:italic>: The 2017 PhysioNet/CinC Challenge focused on automatic classification of atrial fibrillation (AF) in short ECGs. This study aimed to evaluate the use of the data and results from the challenge for detection of AF in longer ECGs, taken from three other PhysioNet datasets. <jats:italic>Approach</jats:italic>: The used data-driven models were based on features extracted from ECG recordings, calculated according to three solutions from the challenge. A Random Forest classifier was trained with the data from the challenge. The performance was evaluated on all non-overlapping 30 s segments in all recordings from three MIT-BIH datasets. Fifty-six models were trained using different feature sets, both before and after applying three feature reduction techniques. <jats:italic>Main Results</jats:italic>: Based on rhythm annotations, the AF proportion was 0.00 in the MIT-BIH Normal Sinus Rhythm (<jats:italic>N</jats:italic> = 46083 segments), 0.10 in the MIT-BIH Arrhythmia (<jats:italic>N</jats:italic> = 2880), and 0.41 in the MIT-BIH Atrial Fibrillation (<jats:italic>N</jats:italic> = 28104) dataset. For the best performing model, the corresponding detected proportions of AF were 0.00, 0.11 and 0.36 using all features, and 0.01, 0.10 and 0.38 when using the 15 best performing features. <jats:italic>Significance</jats:italic>: The results obtained on the MIT-BIH datasets indicate that the training data and solutions from the 2017 Physionet/Cinc Challenge can be useful tools for developing robust AF detectors also in longer ECG recordings, even when using a low number of carefully selected features. The use of feature selection allows significantly reducing the number of features while preserving the classification performance, which can be important when building low-complexity AF classifiers on ECG devices with constrained computational and energy resources.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This research work explores the Content-Based Medical Image Retrieval system (CBMIR) to categorization and retrieval of different types of common thoracic diseases such as Atelectasis, cardiomegaly, Effusion, Infiltration etc, based on local patch representation of ‘Bag of Visual Words’ approach, when performing patch-based image representation, the selected patch size has significant impact on image categorization and retrieval process. It is a challenging task in selecting the appropriate patch size to the current experimental dataset. Chest Xray8 medical image database is used, to analyze the impact of different patch size to categorize and retrieval of eight common thorax diseases. 1000 frontal view x-ray images is obtained (100 images from each category and 200 images combination of more than one disease) from the database. Different sizes of image patches (16 × 16 and 32 × 32) and different codebook sizes (500, 1000, 1500, 2000) created to identify best precision and recall values. From the excremental result, 32 × 32 patch size and 1500 codebook size gives the good precision and recall value using Radial Basis Function SVM kernel.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>SAXS-CT is an emerging powerful imaging technique which bridges the gap between information retrieved from high-resolution local techniques and information from low-resolution, large field-of-view imaging, to determine the nanostructure characteristics of well-ordered tissues, <jats:italic>e.g</jats:italic>., mineralized collagen in bone. However, in the case of soft tissues, features such as poor nanostructural organization and high susceptibility to radiation-induced damage limit the use of SAXS-CT. Here, by combining the freeze-drying the specimen, preceded by formalin fixation, with the nanostructure survey we identified and monitored alterations on the hierarchical arrangement of triglycerides and collagen fibrils three-dimensionally in breast tumor specimens without requiring sample staining. A high density of aligned collagen was observed precisely on the invasion front of the breast carcinoma, showing the direction of cancer spread, whereas substantial content of triglycerides was identified, where the healthy tissue was located. Finally, the approach developed here provides a path to high-resolution nanostructural probing with a large field-of-view, which was demonstrated through the visualization of characteristic nanostructural arrangement and quantification of content and degree of organization of collagen fibrils in normal, benign and malignant human breast tissue.</jats:p>