Serum profiles of insulin-like growth factors and their binding proteins in adults with growth hormone receptor deficiency treated with insulin like growth factor I

Six adult patients with growth hormone receptor deficiency (GHRD) (2 men, 4 women) with an identical defect in the growth hormone receptor (GHR) gene, were treated with recombinant human insulin-like growth factor I (IGF-I), 40 μgikg S.C. twice daily, for 7 days. Serum concentrations of IGF peptide and IGF binding protein-3 (IGFBP-3) were measured by specific radioimmunoassays; serum IGFBPs were also measured by Western ligand blotting. The size distribution of both IGF-I and IGF-II was measured in serum following size-exclusion fast-performance liquid chromatography. IGF-I treatment resulted in a normalization of serum IGF-I levels on days 1–7 of treatment and a decrease in serum IGF-II levels. The fall in IGF-II levels and the simultaneous rise in IGF-I levels, however, resulted in an unchanged total serum IGF level. The low IGFBP-3 values did not significantly change during treatment, whereas there was a slight increase in IGFBP-2 levels. Preliminary analysis of size-fractionated sera suggested an increase in IGF-I levels in the 40 and 150 kDa regions at the expense of IGF-II levels. The results suggest that despite the failure of IGF-I treatment to increase IGFBPs significantly, serum IGFBP concentrations were sufficient to maintain normal levels of IGF-I. 0 Laron syndrome, growth hormone receptor deficiency, insulin-like growth factors, insulin-like growth factor binding protein     

Multispectral analysis of magnetic resonance images

Magnetic resonance (MR) imaging systems produce spatial distribution estimates of proton density, relaxation time, and flow, in a two dimensional matrix form that is analogous to that of the image data obtained from multispectral imaging satellites. Advanced NASA satellite image processing offers sophisticated multispectral analysis of MR images. Spin echo and inversion recovery pulse sequence images were entered in a digital format compatible with satellite images and accurately registered pixel by pixel. Signatures of each tissue class were automatically determined using both supervised and unsupervised classification. Overall tissue classification was obtained in the form of a theme map. In MR images of the brain, for example, the classes included CSF, gray matter, white matter, subcutaneous fat, muscle, and bone. These methods provide an efficient means of identifying subtle relationships in a multi-image MR study.