Type 1 diabetes (T1D) is the most common cause of diabetes in childhood but type 2 diabetes (T2D) and maturity onset diabetes of the young (MODY) are emerging as noteworthy causes of diabetes at young ages. The aim is to determine the distribution, trends and clinical features of the different types of diabetes in childhood in one tertiary center. The records of children and adolescents aged 0-18 years who were diagnosed as “diabetes/persistent hyperglycemia” between January 1999 and December 2016, were reviewed. Clinical and laboratory characteristics of the patients at diagnosis and type of diabetes were recorded. The mean ± standard deviation age of 835 patients (48.7% females) at diagnosis was 8.8±4.4 years. Eighty-four percent of the patients were diagnosed as T1D, 5.7% as T2D, 5.3% as clinical MODY and 5% as being cases of other types of diabetes. The frequency of diabetic ketoacidosis (DKA) and severe DKA in T1D were 48.4% and 11.6%, respectively. Fourteen patients (29.2%) with T2D presented with ketosis and two of them (4.2%) had DKA at diagnosis. Antibody positivity was 83.1% in T1D and 14.8% in T2D. A statistically significant increase in the frequency of T2D has clearly been demonstrated in recent years with a frequency of 1.9%, 2.4% and 7.9% in 1999-2004, 2005-2010 and 2011-2016, respectively (p<0.001). In MODY, genetic analysis was performed in 26 (59%) patients and HNF1A and GCK gene mutations were detected in 3 (11.5%) and 14 (53.8%) patients, respectively. Although the most frequent cause of DM is T1D in childhood, a trend towards increase in the frequency of T2D in recent years is notable in our population.

Background/Objectives: Children with atopic dermatitis (AD) are at increased risk of food allergy (FA). This study aimed to develop and evaluate machine learning (ML) models for predicting FA in children aged <2 years with AD and to assess feature contribution. Methods: This retrospective study included children aged 1 month to 2 years who were diagnosed with AD and underwent evaluation for FA. Demographic characteristics, clinical features, and laboratory findings were extracted from electronic medical records. Four ML algorithms—CatBoost, XGBoost, LightGBM, and logistic regression—were evaluated for predicting FA. Results: A total of 435 children with AD were included, of whom 185 (42.5%) were female. The median age at presentation was 7.4 months (IQR, 4.8–10.8), and FA was present in 101 patients (23.2%). Among all evaluated models, CatBoost using clinical features alone demonstrated the best overall performance, achieving an AUC of 0.91 (95% CI, 0.84–0.97), with a sensitivity of 0.85 and a specificity of 0.91. Using combined clinical and laboratory features, XGBoost achieved an AUC of 0.88 (95% CI, 0.79–0.95), with a sensitivity of 0.70 and a specificity of 0.96. Using clinical features alone, LightGBM and logistic regression achieved AUCs of 0.89 (95% CI, 0.82–0.96) and 0.86 (95% CI, 0.75–0.95), respectively, with sensitivities of 0.85 and 0.65 and specificities of 0.82 and 0.88. Conclusions: CatBoost demonstrated the best overall performance in predicting FA in children aged <2 years with AD using clinical features alone. ML models may help identify children with AD who require further diagnostic evaluation for FA.

Real-life studies are needed to evaluate the clinical outcomes of add-on tiotropium therapy in patients with asthma. The effects of adding tiotropium bromide to the treatment of asthmatic patients on pulmonary functions and asthma control using real-life data. In a retrospective study, spirometric measures and asthma control states were compared before and one year after of tiotropium treatment in asthmatic adults whose disease was not adequately controlled with a combination of inhaled corticosteroids and long-acting β2-agonists. One year after tiotropium treatment, mean FEV1, FEV1%, and FEV1/FVC ratio increased significantly compared to pretreatment values. Among 32 patients added tiotropium due to symptomatic asthma, 28 (87.5%) patients achieved well-controlled (ACT ≥ 20) end of the year and GINA treatment step-down in 4 (12.5%) patients. Monoclonal antibody therapies (mepolizumab or omalizumab) were initiated in 9 patients (28.1%). FEV1 values and FEV1/FVC ratios showed a statistically significant improvement from baseline measurements obtained prior to the initiation of tiotropium therapy, independent of monoclonal antibody use (p < 0.001 for each). The mean age of these patients was 48.78 ± 11.64 (range: 28 - 81) years, and 25 (78.1%) of them were female. Tiotropium bromide is an effective and reliable add-on therapy for symptomatic asthma when combined with ICS plus LABA, also leads to improvements in respiratory function and asthma control.

Objective: Large language models are increasingly used to obtain medical information; however, their accuracy in asthma-related board-review questions, particularly those requiring guideline-based clinical reasoning, remains uncertain. This study aimed to compare the performance of different ChatGPT model configurations in asthma-related multiple-choice questions.Methods: Forty asthma-related questions obtained from an educational board-review resource were submitted separately to seven ChatGPT model configurations in May 2026: GPT-5.2 Instant, GPT-5.2 Standard Thinking, GPT-5.2 Extended Thinking, OpenAI o3, GPT-5.5 Instant, GPT-5.5 Standard Thinking, and GPT-5.5 Extended Thinking. Each question was entered individually in a new temporary chat session. Responses were compared with reference answers reviewed by adult and pediatric allergist-immunologists according to current guideline recommendations and relevant literature. Accuracy was compared using Cochran’s Q test, McNemar’s test, and the Wilcoxon signed-rank test.Results: Model accuracy ranged from 85.0% to 92.5%. GPT-5.5 Standard Thinking achieved the highest accuracy, with 37 correct responses out of 40, whereas GPT-5.2 Standard Thinking had the lowest accuracy, with 34 correct responses. No statistically significant difference was observed among the seven configurations (p = 0.638). Family-level accuracy was numerically higher for GPT-5.5 than for GPT-5.2, but this difference was not statistically significant (90.8% vs. 86.7%; p = 0.276). Pairwise analyses also showed no statistically significant differences between model comparisons.Conclusion: ChatGPT models showed generally high accuracy in asthma-related questions, although errors occurred in selected guideline- and context-dependent scenarios. ChatGPT may serve as a supportive tool for medical education and clinical information seeking, but its outputs require expert verification.

Objective: This study aimed to characterize children evaluated for rash in a pediatric allergy and immunology clinic, with particular emphasis on infectious etiologies and atopic features. Methods: This retrospective observational study included children aged 0–18 years who were evaluated for rash in a pediatric allergy and immunology clinic and in whom an infectious agent was identified. Results: A total of 166 children were included; 68 patients (41.0%) were female, and the mean age was 7.7±3.4 years. The most common atopic diseases were asthma (24.7%) and allergic rhinitis (15.1%). Among 158 patients with a single detected infectious agent, Parvovirus B19 was the most common etiology (n=100), followed by Mycoplasma pneumoniae (n=30). Urticaria was most frequent in Mycoplasma pneumoniae infection and least frequent in Parvovirus B19 infection (p <0.001). Macular and lacy/reticular rashes were significantly more frequent in the Parvovirus B19 group (p <0.001 and 0.001, respectively). Periorbital edema, known as Hoagland sign, was observed in 4 of 11 patients with single Epstein–Barr virus (EBV) infection. Median IgG, IgM, IgA, and total IgE levels did not differ significantly among the infectious-agent groups. Among 122 patients who underwent allergic evaluation, 37 (30.3%) were sensitized to at least one allergen. Pollen was the most frequent sensitizing allergen group (n=28, 23.0%). The frequencies of allergen sensitization did not differ significantly among the infectious-agent groups. Conclusion: Allergen sensitization did not differ according to the infectious agent detected in children with rash. EBV-associated periorbital edema may mimic allergic angioedema but represents a distinct infection-related clinical finding.