Using in vitro tensile strength test to monitoring quality and effectiveness of suture in the oral environment

Sutures are medical devices used in surgery. They serve as tissues stabilizers in contact with or near to the surgical site without compromising the healing process. They must keep their physical properties for the necessary time, in particular tensile strength. In view of the wide variety of references offered by all specialtys combined, which supply sutures with all materials described, whose use is indicated for all surgical procedures. The objective of this work is to evaluate the tensile strength of absorbable and non-absorbable sutures for a period of 10 to 28 days under conditions simulated by the oral route. 5 sutures materials were tested with a metric diameter of 1.5 and 4.The tensile strength test was used according to the protocol of the European Pharmacopoeia (Eur.Ph.9.5). 5 fragments of each material were measured before and after their immersion in Artificial Saliva (AS). In AS, the Polypropylene suture significantly maintained (p = 5%) its tensile strength compared to that of Polyamide. For absorbable sutures, a loss of more than 70% of their initial strength was marked on the 7th day of immersion. In view of the results obtained, during oral surgical operations, the material of choice is in favor of Propylene

Bag‐of‐features for image memorability evaluation

Image memorability represents the degree to which images are remembered or forgotten after a period of time. Studying image memorability in computer vision is the task of finding special characteristics in memorable images, in order to develop a representative model of this type of images. Several approaches have been realised to examine features that can affect image memorability. In this study, the authors use bag‐of‐features as another kind of visual feature descriptor to assess image memorability. The authors’ method based on bag‐of‐visual‐words (BoVWs) technique involves four main steps. First, the authors extract local image features from regions/points of interest which are automatically detected. Then, they encode these local features by mapping them to a created visual vocabulary. Later, the authors apply features pooling and normalisation techniques to obtain image BoVW representation. Finally, the authors use this representation to examine image memorability as a problem of classification. They present different implementation choices for each step and compare reached results. The authors’ method performs best significant results in comparison with other approaches found in literature