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L Grippo

Publications and source records attributed to L Grippo.

2 recordsLinked to original sources

Decreased protein tyrosine phosphorylation and membrane fluidity in spermatozoa from infertile men with varicocele.

Varicocele is a prevalent pathology among infertile men. The mechanisms linking this condition to infertility, however, are poorly understood. Our previous work showed a relationship between sperm functional quality and the ability of spermatozoa to respond to capacitating conditions with increased membrane fluidity and protein tyrosine phosphorylation. Given the reported association between varicocele, oxidative stress, and sperm dysfunction, we hypothesized that spermatozoa from infertile patients with varicocele might have a combined defect at the level of membrane fluidity and protein tyrosine phosphorylation. Semen samples from infertile patients with and without grade II/III left varicocele were evaluated for motion parameters (computer-assisted semen analysis [CASA]), hyperactivation (CASA), incidence and intensity of protein tyrosine phosphorylation (phosphotyrosine immunofluorescence and western blotting), and membrane fluidity (Laurdan fluorometry), before and after a capacitating incubation (6 hr at 37 degrees C in Ham's F10/BSA, 5% CO(2)). Spermatozoa from varicocele samples presented a decreased response to the capacitating challenge, showing significantly lower motility, hyperactivation, incidence and intensity of tyrosine phosphorylation, and membrane fluidity. The findings reported in this article indicate that the sperm dysfunction associated to infertile varicocele coexists with decreased sperm plasma membrane fluidity and tyrosine phosphorylation. These deficiencies represent potential new pathophysiological mechanisms underlying varicocele-related infertility.

Adult↗

Convergent decomposition techniques for training RBF neural networks.

In this article we define globally convergent decomposition algorithms for supervised training of generalized radial basis function neural networks. First, we consider training algorithms based on the two-block decomposition of the network parameters into the vector of weights and the vector of centers. Then we define a decomposition algorithm in which the selection of the center locations is split into sequential minimizations with respect to each center, and we give a suitable criterion for choosing the centers that must be updated at each step. We prove the global convergence of the proposed algorithms and report the computational results obtained for a set of test problems.

Algorithms↗