Age-related functional and structural retinal modifications in the Igf1−/− null mouse

Background. Mutations in the gene encoding human insulin-like growth factor-I (IGF-I) cause syndromic neurosensorial deafness. To understand the precise role of IGF-I in retinal physiology, we have studied the morphology and electrophysiology of the retina of the Igf1−/− mice in comparison with that of the Igf1+/− and Igf1+/+ animals during aging. Methods. Serological concentrations of IGF-I, glycemia and body weight were determined in Igf1+/+, Igf1+/− and Igf1−/− mice at different times up to 360 days of age. We have analyzed hearing by recording the auditory brainstem responses (ABR), the retinal function by electroretinographic (ERG) responses and the retinal morphology by immunohistochemical labeling on retinal preparations at different ages. Results. IGF-I levels are gradually reduced with aging in the mouse. Deaf Igf1−/− mice had an almost flat scotopic ERG response and a photopic ERG response of very small amplitude at postnatal age 360 days (P360). At the same age, Igf1+/− mice still showed both scotopic and photopic ERG responses, but a significant decrease in the ERG wave amplitudes was observed when compared with those of Igf1+/+ mice. Immunohistochemical analysis showed that P360 Igf1−/− mice suffered important structural modifications in the first synapse of the retinal pathway, that affected mainly the postsynaptic processes from horizontal and bipolar cells. A decrease in bassoon and synaptophysin staining in both rod and cone synaptic terminals suggested a reduced photoreceptor output to the inner retina. Retinal morphology of the P360 Igf1+/− mice showed only small alterations in the horizontal and bipolar cell processes, when compared with Igf1+/+ mice of matched age. Conclusions. In the mouse, IGF-I deficit causes an age-related visual loss, besides a congenital deafness. The present results support the use of the Igf1−/− mouse as a new model for the study of human syndromic deaf-blindness.

Aid decision algorithms to estimate the risk in congenital heart surgery

Background and objective: In this paper, we have tested the suitability of using different artificial intelligence-based algorithms for decision support when classifying the risk of congenital heart surgery. In this sense, classification of those surgical risks provides enormous benefits as the a priori estimation of surgical outcomes depending on either the type of disease or the type of repair, and other elements that influence the final result. This preventive estimation may help to avoid future complications, or even death. Methods: We have evaluated four machine learning algorithms to achieve our objective: multilayer perceptron, self-organizing map, radial basis function networks and decision trees. The architectures implemented have the aim of classifying among three types of surgical risk: low complexity, medium complexity and high complexity. Results: Accuracy outcomes achieved range between 80% and 99%, being the multilayer perceptron method the one that offered a higher hit ratio. Conclusions: According to the results, it is feasible to develop a clinical decision support system using the evaluated algorithms. Such system would help cardiology specialists, paediatricians and surgeons to forecast the level of risk related to a congenital heart disease surgery.