Seropositive Neuromyelitis Optica: A Pediatric Case Report and 6-Year Follow-Up

BACKGROUND: Neuromyelitis optica is an autoimmune demyelinating disorder of the central nervous system. Although it has some features in common with multiple sclerosis, it has different clinical features, prognosis, and treatment. We describe a boy with seropositive neuromyelitis optica and his 6-year follow-up. PATIENT: A boy aged 5 years 8 months presented with relapsing optic neuritis, short segment transverse myelitis, and brain involvement. He met the diagnostic criteria for multiple sclerosis fulfilling the McDonald 2010 criteria; however, neuromyelitis optica immunoglobulin-G was detected, and the patient was diagnosed with neuromyelitis optica. He had frequent relapses until immunosuppressive treatment with azathioprine and low-dose prednisone was started. After he was asymptomatic for 2.5 years, prednisone was withdrawn, but he had a new attack soon after withdrawal of the steroid. CONCLUSIONS: It is important to differentiate neuromyelitis optica from multiple sclerosis because early immunosuppressive treatment prevents further disability, and longer periods of immunosuppressive treatment should be planned to prevent relapses. (C) 2013 Elsevier Inc. All rights reserved

Seropositive Neuromyelitis Optica: A Pediatric Case Report and 6-Year Follow-Up

BACKGROUND: Neuromyelitis optica is an autoimmune demyelinating disorder of the central nervous system. Although it has some features in common with multiple sclerosis, it has different clinical features, prognosis, and treatment. We describe a boy with seropositive neuromyelitis optica and his 6-year follow-up. PATIENT: A boy aged 5 years 8 months presented with relapsing optic neuritis, short segment transverse myelitis, and brain involvement. He met the diagnostic criteria for multiple sclerosis fulfilling the McDonald 2010 criteria; however, neuromyelitis optica immunoglobulin-G was detected, and the patient was diagnosed with neuromyelitis optica. He had frequent relapses until immunosuppressive treatment with azathioprine and low-dose prednisone was started. After he was asymptomatic for 2.5 years, prednisone was withdrawn, but he had a new attack soon after withdrawal of the steroid. CONCLUSIONS: It is important to differentiate neuromyelitis optica from multiple sclerosis because early immunosuppressive treatment prevents further disability, and longer periods of immunosuppressive treatment should be planned to prevent relapses. (C) 2013 Elsevier Inc. All rights reserved

beta(2)-microglobulin amyloidosis in hemodialysis and peritoneal dialysis patients

beta(2)-microglobulin (beta(2)-M) amyloidosis is an important cause of morbidity in patients on dialysis. In this cross-sectional study, we evaluated supraspinatus tendon thickness (as a measure of shoulder involvement from beta(2)-M amyloidosis) in patients who are on hemodialysis (HD) compared with those on continuous ambulatory peritoneal dialysis (CAPD). In 27 patients on HD who were treated with high-flux dialyzers, 31 patients on CAPD, and 31 healthy volunteers, we performed bilateral shoulder magnetic resonance imaging and measured the supraspinatus tendon thickness using electronic calipers. There were no statistically significant differences in age or dialysis duration between the HD and CAPD patients. Each patient was asked about the presence or absence of shoulder pain. The supraspinatus tendon thickness in HD patients (mean thickness 6.6 +/- 1.3 mm, range 3.20-8.80 mm, N = 53) and CAPD patients (6.8 +/- 0.9 mm, range 4.9-8.8 mm, N = 61) was not significantly different (P = 0.289); however, the mean thickness in either group was higher than in the healthy controls (5.5 +/- 0.6 mm, range 4.3-6.8 mm, N = 61) (HD patients vs. controls: P = 0.000; CAPD patients vs. controls: P = 0.000). Patients with shoulder pain had higher mean supraspinatus tendon thickness measurements than patients without shoulder pain (P = 0.042). The thickness of supraspinatus tendons is not significantly different between patients on CAPD and HD. An association exists between shoulder pain and mean supraspinatus tendon thickness. This hidden complication of ESRD should be further studied in larger populations of dialysis patients

Low-dose gadobenate dimeglumine versus standard-dose gadopentate dimeglumine for delayed contrast-enhanced cardiac magnetic resonance imaging

Rationale and Objectives. The purpose of our study was to compare gadopentate dimeglumine (Gd-DTPA) and gadobenate dimeglumine (Gd-BOPTA) for the evaluation of myocardial infarction (MI) and in the grading transmural extent on late-contrast enhanced cardiac magnetic resonance imaging

Prognostic factors and treatment results of pediatric Hodgkin's lymphoma: A single center experience

The aim of this study was to assess the demographic, clinic data, prognostic factors and treatment/follow-up results of children who were diagnosed with Hodgkin lymphoma and followed in our center of Pediatric Oncology, Kocaeli University Medical Faculty, Kocaeli, Turkey, for 10 years. This retrospective study evaluated 41 patients with Hodgkin lymphoma who were younger than 18 years-old. All patients were treated with risked adapted ABVD (Adriamycin, Bleomycin, Vincristine, Dacarbazine) chemotherapy and also received involved field radiotherapy

A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts

Objectives: To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke’s cleft cysts (RCCs). Materials and Methods: The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results: Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion: Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs