The aim of this study was to characterize the socioeconomic features, as well as disease activity and functional status, treatment use, and quality of life in a cohort of Egyptian population. All are measured by standard instruments. This is a descriptive multicenter; cross-sectional study included consecutive patients with spondyloarthritis (SpAs) diagnosed according to the European spondyloarthritis study group criteria. Four Egyptian centers participated (one from the Upper Egypt, one from the Delta, and two from the West Coast), all adopted the same criteria for patient assessment, and data were collected in the same data base over a 12-month duration. A total of 75 patients were included in the study. The series consisted of 34 ankylosing spondylitis (AS) patients (64%), 23 patients with psoriatic arthritis (45.3%), 15 patients with Juvenile onset AS (18.7%), 2 patients with reactive arthritis (2.7%), and one with inflammatory bowel disease-related arthritis (1.3%). There was predominance of male patients (84%). All were Caucasians; 13% from the Upper Egypt, and 87% from the Delta and West coast Egypt. Their mean age was 37.44 ± 12.8 years; mean disease duration was 11.85 ± 9.27 years. Pure axial disease was reported by 24% of the patients, and pure peripheral involvement was observed in 4%, while the mixed pattern (axial, peripheral, and entheseal) was observed in 34%. Dactylitis was detected in 9.3%, tarsitis in 8%, and enthesitis in 29.3%. The most common extra-articular manifestation was anterior uveitis, reported by 5.3% of patients. Human leukocytic antigen B27 (HLA-B27) was positive in 58.7% of the tested patients (n = 29). Mean Bath Ankylosing Spondylitis Disease activity Index (BASDAI) score was 4.16 ± 2.12, of which 22 patients had a score more than 4; mean Bath Ankylosing Spondylitis Functional Index 5.12 ± 2.40, mean BASMI 4.17 ± 2.95, mean BAS-GI 5.92 ± 2.00, and mean Health assessment questionnaire 1.10 ± 0.65. In addition, patients with AS showed a higher incapacity for work, felt more pain and presented more axial affection than others. The most utilized treatments were the conventional drugs including non-steroidal anti-inflammatory drugs, followed by sulfasalazine (50%), methotrexate (14%), and steroids (6%). Tumor necrosis factor-alpha inhibitors were received by 7 patients (14%), mostly in the form of Infliximab (85.7%) and were used by only 38.7% of patients with BASDAI of 4 or more. This registry provides the first clinical and demographic data of SpA patients in Egypt upon which a large-scale database registration can be initiated. The most frequently diagnosed SpA in Egyptian patients was AS, with mainly combined axial and peripheral involvement. The clinical spectrum of SpAs seen among Egyptians is similar to most other registries; however, Egyptian AS patients showed some differences in the clinical manifestations. The low frequency of HLA-B27 and the clinical variations in AS may be due to different genetic and/or environmental factors in Egypt.

Objectives: Quantitative assessment of 3-dimensional progressive changes of the maxillary geometry in unilateral cleft lip palate (UCLP) with and without nasoalveolar molding (NAM). Methods: The study was designed as a prospective 2-arm randomized controlled clinical trial conducted in parallel. Forty infants with nonsyndromic UCLP were randomly assigned into a NAM-treated group (n = 20) and non–NAM treated group (n = 20). A total of 120 laser-scanned maxillary casts were collected and blindly analyzed via a modified algorithm at T0 (initial visit; baseline), T1 (after 3 wk; first interval), and T2 (after 6 wk; second interval). The main outcome measures were the amount and rate of cleft gap changes, the midline position, and the transverse, sagittal, and vertical growth through intervals. Results: More than 50% of the cleft gap (56.42%; P < 0.001) was reduced in the first 3 wk of alveolar molding (AM). The end point of the AM was obtained in 6 wk (86.25%; P < 0.001); then, the kinks of the greater segment were noticed. The AM effect decreased as far as posterior; the anterior arch width reduced slightly (1.23%; P < 0.001), while the middle and posterior arches increased slightly (P > 0.999 and P = 0.288, respectively). The posterior arch width was the least changing and was considered a baseline, while the anterior was the pivot of the segment rotation. Both groups showed different patterns of segment rotation and sagittal growth. The non–NAM treated group showed a slight increase in cleft gap length, arch width, and midline position. Conclusion: Based on this study, it was concluded that the NAM treatment is effective in minimizing cleft severity and realigning maxillary segments without the deterioration of the transverse and vertical arch growth. Near follow-up visits are recommended to monitor the rapid gap reduction within the first 3 wk. Further trials are recommended to compare the outcomes regarding the sagittal growth to reference values ( ClinicalTrials.gov NCT03029195). Knowledge Transfer Statement: The results of this study will help clinicians understand nasoalveolar molding biomechanics that may improve the treatment outcomes for patients with unilateral cleft lip and palate. The trial data can be a valuable guide to the qualitative and quantitative predictive virtual molding in computer aided design–simulated nasoalveolar molding therapy. The modified algorithm can be used by researchers to quantify the rate, the sequence, and the direction of the maxillary segments movement in unilateral cleft lip and palate.

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Electrocardiogram (ECG) serves as the gold standard for noninvasive diagnosis of several types of heart disorders. In this study, a novel hybrid approach of deep neural network combined with linear and nonlinear features extracted from ECG and heart rate variability (HRV) is proposed for ECG multi-class classification. The proposed system enhances the ECG diagnosis performance by combining optimized deep learning features with an effective aggregation of ECG features and HRV measures using chaos theory and fragmentation analysis. The constant-Q non-stationary Gabor transform technique is employed to convert the 1-D ECG signal into 2-D image which is sent to a pre-trained convolutional neural network structure, called AlexNet. The pair-wise feature proximity algorithm is employed to select the optimal features from the AlexNet output feature vector to be concatenated with the ECG and HRV measures. The concatenated features are sent to different types of classifiers to distinguish three distinct subjects, namely congestive heart failure, arrhythmia, and normal sinus rhythm (NSR). The results reveal that the linear discriminant analysis classifier has the highest accuracy compared to the other classifiers. The proposed system is investigated with real ECG data taken from well-known databases, and the experimental results show that the proposed diagnosis system outperforms other recent state-of-the-art systems in terms of accuracy 98.75%, specificity 99.00%, sensitivity of 98.18%, and computational time 0.15 s. This demonstrates that the proposed system can be used to assist cardiologists in enhancing the accuracy of ECG diagnosis in real-time clinical setting.

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