Radioactive iodine treatment for Graves' hyperthyroidism: incidence of Graves orbitopathy.

PURPOSE: There are limited recent data on the effect of radioactive iodine (RAI) for Graves' disease on Graves' orbitopathy (GO) development or reactivation. This audit investigates the GO incidence in patients with Graves' disease after RAI treatment, and explores risk factors present, and steroid prophylaxis use. METHODS: A retrospective audit of Graves' disease patients treated with RAI over a 5-year period. Data collected: smoking status, thyroid-stimulating hormone receptor antibody (TRAb) status, GO history, Graves' disease duration, eye features pre- and post-treatment, prophylactic corticosteroids, RAI dose given, post-RAI thyroid status, duration until hypothyroid. RESULTS: One hundred one patients were included, with a median Graves' disease duration 36 months. 34/101 (33.7%) were active/ex-smokers, 86/101 (85.1%) were TRAb-positive, 11/101 (10.9%) had a GO history; 32 (31.7%) had eye features present. Median RAI dose given was 596MBq. 8/101 (7.9%) patients received prophylactic corticosteroid; 89/101 (88.1%) achieved hypothyroid state in the year after RAI. GO developed in 5/101 (5.0%), of which 4/5 (80%) were de novo in high-risk individuals who did not receive steroids. One was a GO reactivation despite steroids. Two required intravenous steroids with/without orbital radiotherapy, one completed oral steroid taper; the remainder were treated conservatively. CONCLUSION: Our cohort had a lower GO incidence in patients with Graves' disease receiving RAI, with majority arising de novo . It is essential that all patients are assessed for Graves orbitopathy risk factors and counselled adequately prior to RAI. The decision to initiate steroids should be undertaken in a multi-disciplinary setting involving endocrinologists and ophthalmologists.

SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies.

BACKGROUND: Over 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant. RESULTS: A number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript. CONCLUSION: SpliceCenter http://discover.nci.nih.gov/splicecenter provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists.