PURPOSE: The aim of this study was to assess the clinical usefulness and added diagnostic value of specific IgE antibodies to wheat, gluten, and ω-5 gliadin in diagnosing wheat allergy and distinguishing wheat anaphylaxis. METHODS: This study included 196 children who visited Ajou University Hospital for suspicious food allergy. The subjects were divided into 2 groups: the wheat allergy (WA) and non-wheat allergy (non-WA) groups. Patients with wheat allergy were further divided into 2 subgroups according to their symptoms: the wheat allergy with anaphylaxis (WA(Ana)) and wheat allergy without anaphylaxis (WA(Non-Ana)) groups. Serum concentrations of total IgE and specific IgE antibodies to wheat, gluten and ω-5 gliadin were measured. RESULTS: The median values of specific IgE antibodies to wheat, gluten and ω-5 gliadin were significantly higher in the WA group than in the non-WA group, and the positive decision points (95% specificity) were at 3.12, 2.61, and 0.21 kUA/L, respectively. The combination of specific IgE antibodies to wheat and ω-5 gliadin resulted in the highest accuracy of 93.9% in diagnosing wheat allergy. In differentiating the WA(Ana) group from the WA(Non-Ana) group, only specific IgE antibody to ω-5 gliadin showed a significant difference at the optimal cutoff point of 1.56 kUA/L. CONCLUSION: Our results show that the individual levels of specific IgE antibodies to wheat, gluten or ω-5 gliadin may have a considerably high accuracy in diagnosing wheat allergy and that specific IgE antibody to ω-5 gliadin may be particularly useful in predicting wheat anaphylaxis.
Anaphylaxis ; Antibodies ; Child* ; Food Hypersensitivity ; Gliadin* ; Glutens* ; Humans ; Hypersensitivity ; Immunoglobulin E* ; Triticum* ; Wheat Hypersensitivity*

Objective: Diagnosis and assessment of depression rely on scoring systems based on questionnaires, either self-reported by patients or administered by clinicians, and observation of patient facial expressions during the interviews plays a crucial role in making impressions in clinical settings. Deep learning driven approaches can assist clinicians in the course of diagnosis of depression by recognizing subtle facial expressions and emotions in depression patients. Methods: Seventeen simulated patients who acted as depressed patients participated in this study. A trained psychiatrist structurally interviewed each participant with moderate depression in accordance with a prepared scenario and without depressive features. Interviews were video-recorded, and a facial emotion recognition algorithm was used to classify emotions of each frame. Results: Among seven emotions (anger, disgust, fear, happiness, neutral, sadness, and surprise), sadness was expressed in a higher proportion on average in the depression-simulated group compared to the normal group. Neutral and fear were expressed in higher proportions on average in the normal group compared to the normal group. The overall distribution of emotions between the two groups was significantly different (p ＜ 0.001). Variance in emotion was significantly less in the depression-simulated group (p ＜ 0.05). Conclusion This study suggests a novel and practical approach to understand the emotional expression of depression patients based on deep learning techniques. Further research would allow us to obtain more perspectives on the emotional profiles of clinical patients, potentially providing helpful insights in making diagnosis of depression patients.