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Objectives Patients with IgG4-related disease (IgG4-RD) typically respond well to initial glucocorticoid therapy, but always relapse with tapered or maintenance dosage of steroid. We aimed to identify the risk factors for relapse of IgG4-RD and explore the impact of active intervention on the serologically unstable condition. Methods We performed a retrospective study of 277 IgG4-RD patients at Peking University People’s Hospital from February 2012 through February 2019. They were all followed for >4 months. The primary outcome was patient relapse. Data on recurrence of IgG4-RD symptoms, laboratory and image findings were recorded, along with information on treatment in the serologically unstable condition. Results The cumulative relapse rate was 12.86%, 27.84% and 36.1% at 12, 24 and 36 months, respectively. Younger age at onset, younger age at diagnosis, longer time from diagnosis to treatment and history of allergy were associated with relapse. Identified independent risk factors were longer time from diagnosis to treatment and history of allergy. When serum IgG4 level was 20%, 50% or 100% higher than that of the remission period, similar percentages of patients finally relapsed, regardless of whether they were in the immunosuppression intensified or non-intensified group. Median duration from serum IgG4 level instability to relapse in the intensified and non-intensified group was not statistically different. Conclusion The risk factors of relapse were longer time from diagnosis to treatment and history of allergy. Intervention in the serologically unstable condition was not helpful for reducing relapse rate.

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It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013)(2014)(2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVI MOD , NDVI CCD and NDVI OLI , respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky-Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVI OLI , with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%-36.4% and 5.3%-29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

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