Different patterns of esophageal motility disorders among patients with dysphagia and normal endoscopy: A 2-center experience

Esophageal motility disorders (EMDs) are the main etiology of nonobstructive dysphagia (NOD), but they are underestimated in Egypt. High-resolution manometry (HRM) with Chicago Classification version 3.0 (CC v3.0) is the current gold standard diagnostic modality to assess EMD in patients with NOD. In this HRM-based study, we aimed to classify EMD among Egyptian patients and explore the relationship between the severity of symptoms and the various groups of EMD. From January 2020 to January 2021, patients with dysphagia were subjected to diagnostic workup, which included symptom questionnaire for Eckardt score, esophagogastroduodenoscopy, barium esophagogram, and HRM. All patients were categorized based on the HRM results using CC version 3.0 after exclusion of those with obstructive esophageal lesions. Of 252 patients with dysphagia, 55 patients with NOD were analyzed according to CC version 3.0. Achalasia was diagnosed in 31 patients (56.4%) (type I: 18 [58.06%]; type II: 9 [29.03%], and type III: 4 [12.9%]), 3 patients (5.5%) with esophagogastric junction outflow obstruction, 2 patients (3.6%) with absent contractility, 4 patients (7.3%) with distal esophageal spasm, 7 patients (12.7%) with ineffective esophageal motility, and 8 patients (14.5%) with normal manometry. Patients with achalasia experienced significantly high regurgitation (96.8% vs 70.8%; P = .016) compared with those without achalasia. Achalasia was the most common EMD in Egyptian patients with NOD. Eckardt score was higher in patients with outflow obstruction and major motor disorder, but it could not differentiate different categories of CC of EMD. HRM is effective in characterization of EMD.     

Eruptive lentigines confined to psoriatic plaques following biologic therapy

Clinical Rheumatology -     

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.