Efficacy of ultraviolet B radiation versus vitamin D3 on postural control and cognitive functions in relapsing-remitting multiple sclerosis: A randomized controlled study.

BACKGROUND: The relapsing-remitting multiple sclerosis (RRMS) is the most common type of MS with prevalence rate 20-60 patients/100.000 individuals in Egypt. Poor postural control and cognitive dysfunctions are well-established complications of RRMS without potent remedy yet. The latest evidence highlighted the potential and independent immune-modulating effects of vitamin D3 and ultraviolet radiation in the management of RRMS. OBJECTIVE: To investigate the efficacy of broadband ultraviolet B radiation (UVBR) versus moderate loading dose of vitamin D3 supplementation in improving postural control and cognitive functions. DESIGN: Pretest-posttest randomized controlled study. SETTING: Multiple sclerosis outpatient unit of Kasr Al-Ainy Hospital. PARTICIPANTS: Forty-seven patients with RRMS were recruited from both genders, yet only 40 completed the study. INTERVENTIONS: Patients were randomized into two groups: UVBR group involved 24 patients, received sessions for 4 weeks and vitamin D3 group involved 23 patients, took vitamin D3 supplementation (50 000 IU/week) for 12 weeks. MAIN OUTCOME MEASURES: Overall balance system index (OSI) and symbol digit modalities test (SDMT). RESULTS: Highly significant decrease (P < 0.001) of the OSI in both groups post-treatment, indicating improved postural control. Moreover, highly significant improvement in the SDMT scores was noted, indicating information processing speed enhancement. Nonetheless, no statistically significant (P ≥ 0.05) differences were evident between the two groups post-treatment in all tested measures. CONCLUSION: Both therapeutic programs were statistically equal in improving postural control and cognitive functions. However, clinically, UVBR therapy was more convenient owing to its shorter treatment time and higher percentage of change for all tested measures.

Stratification of methotrexate-induced oral ulcers in rheumatoid arthritis patients.

AIM: There is a deficiency in the data concerning the clinical forms of methotrexate-induced oral ulcers. This study was conducted to stratify clinical forms of methotrexate-induced oral ulcers in rheumatoid arthritis patients. METHODS: This study included rheumatoid arthritis patients receiving methotrexate as monotherapy. All eligible patients were subjected to thorough clinical examination and full history to identify oral events. Drug history, dose, and duration of MTX were recorded. RESULTS: Among 794 rheumatoid arthritis patients, mean methotrexate dose and duration were 14.3 mg/week and 5.2 years, respectively. Oral ulcers were detected in 6.2% of the patients and 30% of the patients reported previous oral ulcers. Among the detected oral ulcers, 44.9% manifested as deep irregular ulcers, 30.6% presented as aphthous-like ulcers, 14.3% were diffuse mucositis, and 10.2% appeared as lichenoid reaction. CONCLUSION: Methotrexate-induced oral ulceration could be localized or generalized. Localized forms were more noticed than generalized forms. Higher doses and longer durations of methotrexate were detected among patients with generalized oral ulcers.

Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features.

Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naïve Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.