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Gelatin based adhesives have been used in the last decades in different biomedical applications due to the excellent biocompatibility, easy processability, transparency, non-toxicity, and reasonable mechanical properties to mimic the extracellular matrix (ECM). Gelatin adhesives can be easily tuned to gain different viscoelastic and mechanical properties that facilitate its ocular application. We herein grafted glycidyl methacrylate on the gelatin backbone with a simple chemical modification of the precursor, utilizing epoxide ring-opening reactions and visible light-crosslinking. This chemical modification allows the obtaining of an elastic protein-based hydrogel (GELGYM) with excellent biomimetic properties, approaching those of the native tissue. GELGYM can be modulated to be stretched up to 4 times its initial length and withstand high tensile stresses up to 1.95 MPa with compressive strains as high as 80% compared to Gelatin-methacryloyl (GeIMA), the most studied derivative of gelatin used as a bioadhesive. GELGYM is also highly biocompatible and supports cellular adhesion, proliferation, and migration in both 2 and 3-dimensional cell-cultures. These characteristics along with its super adhesion to biological tissues such as cornea, aorta, heart, muscle, kidney, liver, and spleen suggest widespread applications of this hydrogel in many biomedical areas such as transplantation, tissue adhesive, wound dressing, bioprinting, and drug and cell delivery., Graphical abstract Image 1, Highlights • Gelatin based adhesives have been used in the last decades in different biomedical applications. • Gelatin adhesives can be easily tuned to gain different viscoelastic and mechanical properties that facilitate its ocular application. • We herein grafted glycidyl methacrylate on the gelatin backbone with a simple chemical modification of the precursor. • Epoxide ring-opening reactions and visible light-crosslinking allows the obtaining of an elastic gelatin-based hydrogel (GELGYM). • GELGYM shows excellent biomimetic properties, approaching those of the native tissue.

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/982531, This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry., The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project.