RESUMO
Background: Rabson-Mendenhall syndrome (RMS), a rare disorder characterized by severe insulin resistance due to biallelic loss-of-function variants of the insulin receptor gene (INSR), presents therapeutic challenges (OMIM: 262190). This case study explores the efficacy of adjunctive therapy with sodium-glucose cotransporter 2 inhibitors (SGLT2is) in the management of RMS in an 11-year-old male patient with compound heterozygous pathogenic variants of INSR. Methods: Despite initial efforts to regulate glycemia with insulin therapy followed by metformin treatment, achieving stable glycemic control presented a critical challenge, characterized by persistent hyperinsulinism and variable fluctuations in glucose levels. Upon the addition of empagliflozin to metformin, notable improvements in glycated hemoglobin (HbA1c) and time in range (TIR) were observed over a 10-month period. Results: After 10 months of treatment, empagliflozin therapy led to a clinically meaningful reduction in HbA1c levels, decreasing from 8.5% to 7.1%, along with an improvement in TIR from 47% to 74%. Furthermore, regular monitoring effectively averted normoglycemic ketoacidosis, a rare complication associated with SGLT2 inhibitor therapy. Conclusion: This case highlights the potential of SGLT2i as adjunctive therapy in RMS management, particularly in stabilizing glycemic variability. However, further research is warranted to elucidate the long-term efficacy and safety of this therapeutic approach in RMS and similar insulin resistance syndromes.
RESUMO
Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials.
