Collagen Fibrils and Proteoglycans of Peripheral and Central Stroma of the Keratoconus Cornea - Ultrastructure and 3D Transmission Electron Tomography

Keratoconus (KC) is a progressive corneal disorder in which vision gradually deteriorates as a result of continuous conical protrusion and the consequent altered corneal curvature. While the majority of the literature focus on assessing the center of this diseased cornea, there is growing evidence of peripheral involvement in the disease process. Thus, we investigated the organization of collagen fibrils (CFs) and proteoglycans (PGs) in the periphery and center of KC corneal stroma. Three-dimensional transmission electron tomography on four KC corneas showed the degeneration of microfibrils within the CFs and disturbance in the attachment of the PGs. Within the KC corneas, the mean CF diameter of the central-anterior stroma was significantly (p ˂ 0.001) larger than the peripheral-anterior stroma. The interfibrillar distance of CF was significantly (p ˂ 0.001) smaller in the central stroma than in the peripheral stroma. PGs area and the density in the central KC stroma were larger than those in the peripheral stroma. Results of the current study revealed that in the pre- Descemet\u27s membrane stroma of the periphery, the degenerated CFs and PGs constitute biomechanically weak lamellae which are prone to disorganization and this suggests that the peripheral stroma plays an important role in the pathogenicity of the KC cornea

Intraocular Lens Power Calculation after Phototherapeutic Keratectomy: Case Report and a New Method

To report a case of cataract extraction and intraocular lens (IOL) implantation after phototherapeutic keratectomy (PTK). The IOL power was calculated using the single-K and the double-K SRK/T formula, as well as the Haigis formula after modifying the post PTK corneal power using methods described for corneal power measurements after myopic excimer laser treatment (photorefractive keratectomy (PRK) and LASIK). A new method for IOL power calculation after PTK is introduced

Mécanisme de prédiction pour une plateforme de filtrage collaboratif

Face à la quantité et la rapidité d’apparition de nouvelles informations le développement de systèmes d’information pour cibler au mieux les réponses fournies aux utilisateurs afin qu’elles soient plus proches de leurs attentes et de leurs goûts personnels est devenu une nécessité incontournable. Les systèmes de filtrage collaboratif s’inscrivent parmi ces systèmes d’information avec certaines particularités qui font la différence. Le terme de filtrage collaboratif désigne les techniques utilisant les goûts connus d’un groupe d’utilisateurs pour prédire la préférence inconnue d’un nouvel utilisateur. Cet article décrit une plateforme de base de filtrage collaboratif qui permet aux utilisateurs la découverte de documents intéressants, grâce à l’automatisation du processus naturel de recommandation, elle leur permet d’exprimer leurs avis quant à la pertinence des documents, selon leurs goûts et la qualité qu’ils perçoivent des documents ; elle offre la possibilité de bénéficier des évaluations sur les documents que d’autres utilisateurs, de profil proche, ont jugés intéressants. Tous ces avantages sont apportés aux utilisateurs par le principe de collaboration, en contrepartie d’un effort individuel : l’évaluation des documents.Mots clés: Filtrage collaboratif, plateforme, communautés, prédictions, recommandationsEnglish Title: Prediction mechanism for a platform of collaborative filteringEnglish AbstractWith the explosive growth of the quantity of new information the development of information systems to target the best answers provided to users, so they are closer to their expectations and personal taste, has become an unavoidable necessity. The collaborative filtering systems are among these information systems with particular characteristics that make the difference. The term refers to collaborative filtering techniques using the familiar tastes of a group of users to predict the unknown preference of a new user. This article describes a basic platform of collaborative filtering, which allows users to discover interesting documents, through automation of the natural process of recommendation, it allows them to express their opinion about the relevance of documents, according to their tastes and documents’ quality they perceive; it offers the opportunity to benefit from the evaluations on documents of other users, with similar profile, have found interesting. All these benefits are provided to users by the principle of collaboration, in return for an individual effort: evaluating documents.Keywords: Collaborative filtering, community, platform, predictions, recommender system