University of Patras

About: University of Patras is a education organization based out in Pátrai, Greece. It is known for research contribution in the topics: Population & Catalysis. The organization has 13372 authors who have published 31263 publications receiving 677159 citations. The organization is also known as: Panepistímio Patrón.

Genetic Overlap in Kallmann Syndrome, Combined Pituitary Hormone Deficiency, and Septo-Optic Dysplasia

Abstract: Context: Kallmann syndrome (KS), combined pituitary hormone deficiency (CPHD), and septo-optic dysplasia (SOD) all result from development defects of the anterior midline in the human forebrain. Objective: The objective of the study was to investigate whether KS, CPHD, and SOD have shared genetic origins. Design and Participants: A total of 103 patients with either CPHD (n = 35) or SOD (n = 68) were investigated for mutations in genes implicated in the etiology of KS (FGFR1, FGF8, PROKR2, PROK2, and KAL1). Consequences of identified FGFR1, FGF8, and PROKR2 mutations were investigated in vitro. Results: Three patients with SOD had heterozygous mutations in FGFR1; these were either shown to alter receptor signaling (p.S450F, p.P483S) or predicted to affect splicing (c.336C>T, p.T112T). One patient had a synonymous change in FGF8 (c.216G>A, p.T72T) that was shown to affect splicing and ligand signaling activity. Four patients with CPHD/SOD were found to harbor heterozygous rare loss-of-function variants in P...

A multiobjective gradient-based dose optimization algorithm for external beam conformal radiotherapy.

Abstract: A multiobjective gradient-based algorithm has been developed for the purpose of dose distribution optimization in external beam conformal radiotherapy. This algorithm is based on the concept of gathering the values of all objectives into a single value. The weighting factors of the composite objective values are varied in different steps, allowing the reconstruction of the trade-off surfaces (three or more objectives) or curves (two objectives) which define the boundary between the feasible and non-feasible domain regions. The analysis of these curves allows the decision-maker to select the solution that best fits the clinical goals. In contrast to all the other algorithms, our method provides not a single solution but a sample of solutions representing all possible clinical importance factors (weights) for the objectives used. The application of this algorithm to two test cases shows that a correct selection for the importance factors to multiply the individual objectives in the global objective value is not trivial and that the location and shape of the boundary region between the feasible and non-feasible solution regions are case dependent. Provided that the individual objective functions are analytically differentiable and that the number of objectives is the range of two to three, the computation times are acceptable for clinical use. Furthermore, the optimization for a unique combination of importance factors within the aggregate objective function is performed in less than 1 min.