IgG4 and IgE co-positive group found in idiopathic orbital inflammatory disease

AIM： To reveal the cytokines involved in idiopathic orbital inflammatory disease（IOID） and the relationship between Th17 cells, IgE and IOID pathogenesis.METHODS： Whole blood samples were processed immediately after collection and serological IgG4, IgG, and IgE antibodies were tested using ELISA. IOID and orbital cavernous hemangioma（CH） tissue samples underwent Bio-Plex multiplex cytokine detection. Hematoxylin-Eosin（HE） staining of all paraffin samples suggested the histological features of IOIDs, and expressions of IgG4 and IL-17 A in affected tissues were detected by immunohistochemistry. RESULTS： Among 40 IOID plasma samples, 52.5%（21/40） were positive for IgG4 and 25%（10/40） were positive for IgE. Overlapped IgG4 or IgE positive samples accounted for 22.5%（9/40）. Therefore, IOID samples were separated into three groups. The IgE＋/IgG4＋ group had a relevantly lower level of pro-inflammatory cytokine expression. IL-4（Th2 cell related）, IL-10 and TGF-β1（Treg cell immunity related） were elevated in all three groups. Some of the Th17 cell related cytokines（i.e. IL-17 A/F, IL-25, IL-23, and IL-33） displayed higher expression levels in the IgE-/IgG4-group compared to the other two groups.CONCLUSION： We discovered an IgG4-IgE co-positive group as well as Th17 cell immune involvement in IgG4-IgE co-negative subgtroup in IOID for the first time. The pathogenesis of IOID could differ from different subgroups according to the IgG4 and IgE detection. Therefore, we recommend that, Treatment stratagy should be made according to the clinical assessment of IgG4-IgE and Th17 profile detection.

Orbit Fitting Based on Helmert Transformation

Orbit fitting is used in many GPS applications. For example, in Precise Point Positioning (PPP), GPS orbits (SP3 orbits) are normally retrieved either from IGS or from one of its Analysis Centers (ACs) with 15 minutes’ sampling, which is much bigger than the normal observation sampling. Therefore, algorithms should be derived to fit GPS orbits to the observation time. Many methods based on interpolation were developed. Using these methods the orbits fit well at the sampling points. However, these methods ignore the physical motion model of GPS satellites. Therefore, the trajectories may not fit the true orbits at the periods in between 2 sampling epochs. To solve this problem, we develop a dynamic approach, in which a model based on Helmert transformation is developed in GPS orbit fitting. In this orbit fitting approach, GPS orbits at sampling points are treated as pseudo-observations. Thereafter, Helmert transformation is built up between the pseudo-observations and dynamically integrated orbits at each epoch. A set of Helmert parameters together with corrections of GPS initial orbits are then modeled as unknown parameters. Results show that the final fit orbits have the same precision as the IGS final orbits.