Effect of Cholecalciferol as Adjunctive Therapy With Insulin on Protective Immunologic Profile and Decline of Residual beta-Cell Function in New-Onset Type 1 Diabetes Mellitus

Objective: To evaluate the effect of vitamin D-3 on cytokine levels, regulatory T cells, and residual beta-cell function decline when cholecalciferol (vitamin D-3 administered therapeutically) is given as adjunctive therapy with insulin in new-onset type 1 diabetes mellitus (T1DM). Design and Setting: An 18-month (March 10, 2006, to October 28, 2010) randomized, double-blind, placebo-controlled trial was conducted at the Diabetes Center of Sao Paulo Federal University, Sao Paulo, Brazil. Participants: Thirty-eight patients with new-onset T1DM with fasting serum C-peptide levels greater than or equal to 0.6 ng/mL were randomly assigned to receive daily oral therapy of cholecalciferol, 2000 IU, or placebo. Main Outcome Measure: Levels of proinflammatory and anti-inflammatory cytokines, chemokines, regulatory T cells, hemoglobin A(1c), and C-peptide; body mass index; and insulin daily dose. Results: Mean (SD) chemokine ligand 2 (monocyte chemoattractant protein 1) levels were significantly higher (184.6 [101.1] vs 121.4 [55.8] pg/mL) at 12 months, as well as the increase in regulatory T-cell percentage (4.55%[1.5%] vs 3.34%[1.8%]) with cholecalciferol vs placebo. The cumulative incidence of progression to undetectable (<= 0.1 ng/mL) fasting C-peptide reached 18.7% in the cholecalciferol group and 62.5% in the placebo group; stimulated C-peptide reached 6.2% in the cholecalciferol group and 37.5% in the placebo group at 18 months. Body mass index, hemoglobin A(1c) level, and insulin requirements were similar between the 2 groups. Conclusions: Cholecalciferol used as adjunctive therapy with insulin is safe and associated with a protective immunologic effect and slow decline of residual beta-cell function in patients with new-onset T1DM. Cholecalciferol may be an interesting adjuvant in T1DM prevention trials.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.