Five-year response to growth hormone in children with Noonan syndrome and growth hormone deficiency

BACKGROUND: Noonan syndrome (NS) is an autosomal dominant disorder characterized by specific features including short stature, distinctive facial dysmorphic features, congenital heart defects, hypertrophic cardiomyopathy, skeletal anomalies and webbing of the neck. Molecular screening has shown that the majority of individuals with NS have a mutation in the PTPN11 gene. Noonan syndrome children may show an impaired growth hormone (GH)/insulin-like growth factor axis. Moreover, recombinant human GH (rhGH) has been shown to improve growth rate in patients with NS, although data are still limited. METHODS: In the present study, we assessed growth response following GH therapy (0.25 mg/Kg/week) in 5 (2 M and 3 F) GH-deficient NS patients (NSGHD, mean age 8.5 years) and in 5 (2 M and 3 F) idiopathic GH deficient (IGHD, mean age 8.6 years) patients. We also evaluated the safety of rhGH therapy in NS patients with GHD. RESULTS: At the beginning of GH treatment, height and growth rate were statistically lower in NSGHD children than in IGHD ones. During the first three years of rhGH therapy, NSGHD patients showed a slight improvement in height (from −2.71 SDS to −2.44 SDS) and growth rate (from −2.42 SDS to −0.23 SDS), although the values were always significantly lower than in IGHD children. After five years of rhGH treatment, height gain was higher in IGHD children (mean 28.3 cm) than in NSGHD patients (mean 23.6 cm). During the first five years of rhGH therapy, regular cardiological and haematological check-ups were performed, leading to the conclusion that rhGH therapy was safe. CONCLUSIONS: In conclusion, pre-pubertal NS children with GHD slightly increased their height and growth rate during the first years of GH therapy, although the response to rhGH treatment was significantly lower than IGHD children. Furthermore, the therapy appeared to be safe since no severe adverse effects were reported, at least during the first five years. However, a close follow-up of these patients is mandatory, especially to monitor cardiac function

Joint deconvolution and unsupervised source separation for data on the sphere

International audienceTackling unsupervised source separation jointly with an additional inverse problem such as deconvolution is central for the analysis of multi-wavelength data. This becomes highly challenging when applied to large data sampled on the sphere such as those provided by wide-field observations in astrophysics, whose analysis requires the design of dedicated robust and yet effective algorithms. We therefore investigate a new joint deconvolution/sparse blind source separation method dedicated for data sampled on the sphere, coined SDecGMCA. It is based on a projected alternate least-squares minimization scheme, whose accuracy is proved to strongly rely on some regularization scheme in the present joint deconvolution/blind source separation setting. To this end, a regularization strategy is introduced that allows designing a new robust and effective algorithm, which is key to analyze large spherical data. Numerical experiments are carried out on toy examples and realistic astronomical data

Non-linear interpolation learning for example-based inverse problem regularization

A large number of signal recovery problems are not well-posed-if not ill-posed-that require extra regularization to be tackled. In this context, the ability to inject physical knowledge is of utmost importance to design effective regularization schemes. However, most physically relevant models are generally nonlinear: signals generally lie on an unknown low-dimensional manifolds structure, which needs to be learnt. This is however quite challenging when available training samples are scarce. To that end, we investigate a novel approach that builds upon learning a non-linear interpolatory scheme from examples. We show how the proposed approach resonates with transportbased methods, but with a learnt metric. This eventually allows to build efficient non-linear regularizations for linear inverse problems. Extensive numerical experiments have been carried out to evaluate the performances of the proposed approach. We further illustrate its application to a blind regression problem in the field of γ-ray spectroscopy