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Purpose: To evaluate the characteristics and frequency of remission in pediatric patients with Graves' disease (GD) treated with antithyroid drug (ATD) and to identify factors that may be associated with relapse. Methods: Medical records of patients younger than 19 years who presented to the Department of Pediatrics of Queen Elizabeth Hospital Hong Kong with newly diagnosed GD from 1st January 2007 to 31st December 2017 were retrospectively reviewed. Remission was defined as euthyroidism for 12 months or more after discontinuation of ATD treatment and no relapses during the follow-up period. Patients who successfully achieved remission were compared to those who suffered relapse. Factors that may predict occurrence of relapse after ATD treatments were studied, and their odds ratios (ORs) were calculated. Results: A total of 101 patients was included in this study. Eighty-one patients completed one course of ATD. Eighteen patients (17.8%) successfully achieved remission, and 58 patients (57.4%) experienced relapse after discontinuation of ATD. The remission group received a significantly longer course of ATD therapy than the relapse group (median, 28 months; interquartile range [IQR], 18-48 months in remission group vs. median, 21 months; IQR, 17-26; P=0.024). The OR for relapse was 0.971 (95% confidence interval [CI], 0.946-0.997) in univariate analysis and remained significant after adjustments in the multivariate regression model (OR, 0.961; 95% CI, 0.933-0.989; P=0.008). Conclusion: The remission rate in pediatric patients with GD treated with ATD was low. A longer ATD course was associated with a greater chance of remission in this population. [ABSTRACT FROM AUTHOR]

Purpose: We sought to evaluate features of partial remission (PR) in children with type 1 diabetes mellitus (T1DM) using the insulin-dose adjusted A1c (IDAA1c) definition and to identify risk factors associated with nonremission., Methods: Medical records of patients with newly diagnosed T1DM between January 1, 2008, and June 30, 2018, were retrospectively reviewed. Hemoglobin A1c (HbA1c) readings and insulin total daily doses (TDDs) of each patient at each follow-up visit were obtained with IDAA1c values calculated. PR was defined as an IDAA1c score of 9 points or less within 6 months of diagnosis. The trends of HbA1c and TDD within 2 years after diagnosis were compared between remitters and nonremitters. Factors that may predict the occurrence of PR were studied, with their relative risks of nonremission calculated., Results: PR occurred in 26 patients (45.6%), including 8 girls and 18 boys, with a median duration of 8 months. The frequency of remission in male patients was significantly higher (P=0.002) and the relative risk of female sex with nonremission was 2.20 (95% confidence interval [CI], 1.24-3.91), which remained significant when adjusted by multivariate regression modeling. The initial HbA1c level at diagnosis was also significantly higher in the nonremission group (P=0.029), with a relative risk of 1.12 (95% CI, 1.01-1.25). Both HbA1c (P=0.012) and TDD (P=0.006) were significantly lower within 2 years after diagnosis among remitters than in nonremitters. TDD was significantly lower in male patients (P=0.029) during the same period, while there was no significant difference in HbA1c level between male and female patients (P=0.163)., Conclusion: Both the initial HbA1c level at diagnosis and sex were factors associated with the occurrence of PR. Female sex was an independent risk factor of nonremission, likely resulting from a higher insulin requirement in female T1DM patients.

We develop a new algorithm to automatically register hippocampal (HP) surfaces with complete geometric matching, avoiding the need to manually label landmark features. A good registration depends on a reasonable choice of shape energy that measures the dissimilarity between surfaces. In our work, we first propose a complete shape index using the Beltrami coefficient and curvatures, which measures subtle local differences. The proposed shape energy is zero if and only if two shapes are identical up to a rigid motion. We then seek the best surface registration by minimizing the shape energy. We propose a simple representation of surface diffeomorphisms using Beltrami coefficients, which simplifies the optimization process. We then iteratively minimize the shape energy using the proposed Beltrami Holomorphic flow (BHF) method. Experimental results on 212 HP of normal and diseased (Alzheimer's disease) subjects show our proposed algorithm is effective in registering HP surfaces with complete geometric matching. The proposed shape energy can also capture local shape differences between HP for disease analysis.