Abdullah Akkuş

Objective: This study aims to characterize the clinical spectrum, outcomes, and factors associated with influenza‐associated encephalopathy in hospitalized children with influenza‐associated neurological complications. Methods: This retrospective cohort study included children younger than 18 years who were hospitalized with laboratory‐confirmed influenza at a tertiary university hospital between 2018 and 2023. We reviewed demographic, clinical, laboratory, neuroimaging, electroencephalographic, cerebrospinal fluid, treatment, and outcome data. Patients with and without encephalopathy were compared, and multivariable logistic regression analysis was performed to identify factors independently associated with influenza‐associated encephalopathy. Results: Among 3040 hospitalized children with laboratory‐confirmed influenza, 115 (3.8%) developed influenza‐associated neurological complications. The median age was 32 months (interquartile range [IQR], 19–58 months), and 67 (58.3%) were male. Influenza A accounted for 80.0% of infections. Seizures were the presenting neurological manifestation in all patients, with generalized tonic–clonic seizures occurring in 101 (87.8%). Encephalopathy and encephalitis were identified in 15 (13.0%) and 11 (9.6%) patients, respectively. Two patients (1.7%) were diagnosed with acute necrotizing encephalopathy (ANE). Most children recovered without permanent neurological deficits; however, one patient developed persistent neurological sequelae and two died during hospitalization. In multivariable analysis, focal seizures (adjusted odds ratio [aOR], 16.98; 95% confidence interval [CI], 3.42–84.27; p = 0.001) and higher lymphocyte counts (aOR, 1.53 per 1000 cells/mm ³ increase; 95% CI, 1.11–2.12; p = 0.009) were independently associated with influenza‐associated encephalopathy. Conclusion: Seizures predominantly characterized influenza‐associated neurological complications in hospitalized children and generally had favorable short‐term outcomes. However, influenza‐associated encephalopathy was associated with substantially greater clinical severity, including prolonged hospitalization, more frequent pediatric intensive care unit admission, and poorer in‐hospital outcomes. Recognition of focal seizures may help identify children who warrant closer assessment for concurrent encephalopathy and timely neurological evaluation.

As participation of children in exercise and organized sports increases worldwide, the health care needs of young athletes differ substantially from those of adults due to age-specific physiological, biomechanical, and psychosocial characteristics related to growth and development. Pediatric Sports Medicine (PSM) is a pediatric-based, multidisciplinary subspecialty that encompasses preparticipation physical evaluation (PPE), exercise prescription, management of sports injuries and overuse syndromes, sport-related concussion, heat-related illnesses, assessment of sudden cardiac event risk, relative energy deficiency in sport (RED-S), planning safe sports participation for children with chronic medical conditions, and safeguarding in sport. In the United States, PSM is formally structured through pediatric-based fellowship training and board certification, whereas Europe lacks a standardized model and relies on heterogeneous approaches integrating pediatrics, sports medicine, and rehabilitation. In Türkiye, sports medicine training remains largely adult-oriented. Given that pediatric subspecialty training is strictly based on prior pediatric specialization and that pediatricians are routinely consulted for comprehensive medical evaluations, the absence of a pediatric-based PSM structure has resulted in notable gaps in pediatric-specific PPE standardization, early sport specialization and load management, RED-S screening, integration of school–club–health care systems, and the health care component of safeguarding policies. This narrative review presents a literature-based synthesis of the conceptual foundations and clinical and public health rationale of PSM, summarizes relevant international guidelines and consensus statements, and proposes a feasible framework for its implementation in Türkiye, where this subspecialty has not yet been established.

We read with great interest the position statement published by Turska-Kmieć and colleagues on behalf of the European Academy of Pediatrics (EAP) and the European Confederation of Primary Care Pediatricians (ECPCP) (1), and we, as the ones in attempt to establish the Division of Pediatric Sports Medicine in Türkiye, commend the authors for producing a comprehensive and clinically valuable framework for preparticipation physical evaluation in young athletes. However, we wish to draw attention to a terminological inaccuracy in the classification of sports types presented in the Definitions section of the manuscript that, in our view, merits correction.The authors present the following typology: "There are various types of sport activity which can be defined under different settings such as: Leisure sports, Competitive sports, Exercise training" and attribute this classification to the WHO glossary of terms. This attribution is factually incorrect and conflates two fundamentally distinct conceptual frameworks -the WHO domain classification of physical activity and the typological classification of sports by organizational structure.According to the WHO's established framework, physical activity is classified by the domain in which it occurs, not by the type of sport. Physical activity can be performed as part of work, domestic chores, transportation, or during leisure time (2). The four canonical domains of physical activity are: occupational, domestic/household, transportation, and leisure-time (2,3). Within this framework, the leisure domain includes voluntary activities undertaken during free time for enjoyment, relaxation or health benefits -such as jogging, swimming (3). Critically, WHO defines "leisure-time physical activity" as discretionary activity performed when one is not working, transporting oneself, or doing household chores -with walking and recreational games cited as examples of this domain (4). The term "leisure sports," as used in the manuscript, does not appear in the WHO glossary of terms and does not represent a WHO-recognized category. What the WHO framework describes is "leisuretime physical activity" -a domain descriptor denoting the context in which activity occurs, not a type of sport

Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe clinical entity emerging after severe acute respiratory syndrome coronavirus 2 infection, often characterized by cardiovascular involvement. This study aimed to evaluate the frequency and spectrum of cardiovascular involvement in children with MIS-C and to assess its association with clinical severity, biomarker profiles, and recovery outcomes. Methods: This study included 54 children diagnosed with MIS-C between June 2020 and December 2023. Clinical features, laboratory parameters, electrocardiographic and echocardiographic findings, treatments, and outcomes were analyzed. Clinical severity was classified as mild, moderate, or severe. Kaplan–Meier and Cox regression analyses were used to evaluate recovery trajectories and identify independent predictors. Results: Cardiovascular involvement was detected in 37% of patients. The most common findings were mitral regurgitation (50%), left ventricular dysfunction (20.4%), and pericardial effusion (18.5%). Patients with cardiovascular involvement were older and more likely to present with dyspnea, chest pain, tachycardia, tachypnea, and hypotension (all p < 0.01). They exhibited significantly higher C-reactive protein (CRP), interleukin-6 (IL-6), B-type natriuretic peptide (BNP), urea, and creatinine levels, and lower lymphocyte counts ( p < 0.05). Moderate-to-severe cases showed markedly higher rates of left ventricular dysfunction (44%), mitral regurgitation (100%), pericardial effusion (36%), arrhythmias (16%), hypotension (44%), and inotropic requirement (44%) compared with mild cases (all p < 0.01). Recovery was significantly delayed in patients with cardiovascular involvement (median 12.5 vs. 8.0 days; p = 0.008) and in moderate-to-severe cases (14.0 vs. 7.5 days; p < 0.01). In Cox regression analysis, independent predictors of delayed recovery included cardiovascular involvement (HR: 0.48), CRP > 200 mg/L (HR: 0.52), lymphocyte count < 1000/µL (HR: 0.41), BNP > 10,000 ng/L (HR: 0.35), and IL-6 > 100 pg/mL (HR: 0.44). Conclusion: Cardiovascular involvement in MIS-C is a strong marker of severe disease, heightened systemic inflammation, and prolonged recovery. Echocardiographic abnormalities, combined with elevations in CRP, IL-6, and BNP provide valuable prognostic information.

Elif Nur Yaşar, Hurşit Ferahkaya, İbrahim Kılınç, Abdullah Akkuş, Ömer Faruk Akça. Clin Psychopharmacol Neurosci -0001;0:. https://doi.org/10.9758/cpn.26.1419

Pediatric exanthematous diseases pose diagnostic challenges because clinical presentations overlap. To determine whether current artificial intelligence (AI) models achieve diagnostic accuracy within or above the performance distribution of pediatric residents and specialists for common rash-associated diseases. Participants and AI models were evaluated against definitive diagnoses confirmed by clinical features, laboratory findings, and consensus of two pediatric infectious disease specialists. A volunteer sample of 263 pediatric clinicians: 107 residents (years 1 through 4) and 156 specialists. Each clinician completed a blinded multiple-choice questionnaire with a clinical photograph and accompanying clinical data per case. The same cases were presented to three AI models: ChatGPT, Gemini, and Copilot. Among 263 clinicians (107 residents, 156 specialists), specialists scored higher than residents (median, 46 [IQR, 42—50] vs 41 [IQR, 36—46]; P < .001; r = 0.32). ChatGPT correctly diagnosed 53 of 61 cases (86.9%), Gemini 50 (82.0%), and Copilot 44 (72.1%). Both ChatGPT and Gemini exceeded the upper bound of the specialist population median 95% CI (47.17). All three AI models scored above the resident 95% CI upper bound (42.76). Disease-level accuracy ranged from 0% (insect bites, all models) to 100% (9 conditions, all models). Fourth-year residents scored higher than first- and second-year residents ( P = .001; ε ² = 0.13). Conclusions : AI models given clinical data alongside images matched or exceeded specialist-level performance for pediatric exanthems. Accuracy varied by disease; failures clustered in conditions that require contextual reasoning. Physician oversight remains necessary where AI accuracy is lowest. What is Known • Childhood exanthematous diseases pose significant diagnostic challenges due to their overlapping clinical presentations. • Artificial intelligence models are becoming increasingly proficient in accurately diagnosing these conditions. What is New • In this diagnostic accuracy study of 61 cases evaluated by 263 clinicians and 3 artificial intelligence models, ChatGPT (86.9%) and Gemini (82.0%) exceeded the 95% CI upper bound of the specialist population median. Disease-level accuracy ranged from 0 to 100% across models • Artificial intelligence models given clinical data alongside images can match or exceed specialist-level accuracy for common pediatric exanthems, but failures in context-dependent diagnoses require physician oversight

Purpose: Neurotrophic pathways and hypothalamic–pituitary–adrenal (HPA) axis regulation have been implicated in autism spectrum disorder (ASD), yet peripheral findings—particularly in early childhood—remain inconsistent. We examined peripheral neurotrophic factors and HPA-axis hormones in medication-naïve young children with ASD and evaluated their associations with clinical severity. Methods: This cross-sectional study included 45 medication-naïve children with ASD (18–72 months) and 52 age-matched healthy controls. ASD diagnosis was established per DSM-5, supported by the Childhood Autism Rating Scale (CARS) and the Autism Behavior Checklist (ABC). Morning samples (08:30–09:30) were assayed for serum BDNF, GDNF, NT-3, VEGF, cortisol, and plasma ACTH. Group comparisons used ANCOVA adjusted for age, sex, and BMI-for-age z score (BAZ), with Benjamini–Hochberg false discovery rate (FDR) correction. Within-ASD associations were tested using covariate-adjusted partial Spearman correlations. Results: BDNF was significantly lower in ASD than controls and remained significant after covariate adjustment and FDR correction. No between-group differences were observed for GDNF, NT-3, VEGF, ACTH, or cortisol. Within the ASD group, lower BDNF correlated with higher CARS total scores and higher ABC relating, social and self-help, and total scores. No significant correlations were found between neurotrophic factors and ACTH/cortisol. Conclusions: In medication-naïve children with ASD aged 18–72 months, peripheral BDNF is reduced and relates to symptom severity and adaptive/social difficulties, whereas other neurotrophic factors and basal HPA-axis hormones do not differ from controls. Longitudinal studies incorporating standardized sampling and diurnal/stress-reactivity indices are needed to clarify developmental dynamics and clinical implications.

To examine factors associated with severe respiratory disease at initial presentation in pediatric hMPV-associated LRTIs. We analyzed patients aged 1 month to 18 years with PCR-confirmed hMPV-associated LRTI (January 2018–January 2024). Clinical severity was stratified via the Modified Tal Score at admission (mild ≤ 5, moderate 6–10, severe ≥ 11). Each patient contributed one episode. Multivariable binary logistic regression identified risk factors for severe respiratory disease; ROC analysis evaluated biomarker diagnostic accuracy. A total of 676 hMPV-positive patients were identified; after restricting to one episode per patient, 421 unique episodes were analyzed. Of these, 54.9% were mild, 38.5% moderate, and 6.7% ( n = 28) severe. Comorbidities were present in 67.9% of severe versus 35.5% of mild cases ( p = .004). Viral co-detections occurred in 41.3% but were unrelated to severity ( p = .235). Median CRP was 4.0, 10.0, and 32.0 mg/L in mild, moderate, and severe groups, respectively (p < .001). In multivariable regression, comorbidity was the strongest risk factor (OR 2.96, 95% CI 1.17–7.49, p = 0.022), followed by CRP (OR 1.55 per 1 SD [≈32 mg/L], p = 0.018). No age group retained significance after adjustment (model AUC 0.733). Conclusions : Severe respiratory disease was infrequent (6.7%) and associated primarily with underlying comorbidities, which increased the odds approximately threefold. Age was not independently associated with severity. CRP showed modest diagnostic accuracy (AUC 0.678, negative likelihood ratio 0.59); low levels alone could not reliably exclude severe respiratory disease. What is Known: • hMPV is an established cause of lower respiratory tract infections in children, Ranging from mild illness to severe pneumonia requiring hospitalization and respiratory support. • Infants younger than 12 months and children with comorbidities such as prematurity, chronic lung disease, congenital heart disease, or neurological disorders carry increased risk for severe hMPV infection. What is New: • Multivariable logistic regression identifies underlying comorbidity as the dominant independent risk factor for severe hMPV respiratory disease (OR 2.96, p = 0.022). No age group retains significance after adjustment, indicating that the apparent severity signal in children older than 5 years reflects a comorbidity-driven population shift rather than older age acting as an independent risk factor. • Elevated CRP, decreased lymphocyte counts, and decreased monocyte counts at admission are associated with severe respiratory disease. CRP retained independent association in multivariable analysis, though diagnostic accuracy for all three biomarkers was modest.

Obsessive–compulsive disorder (OCD) is a chronic neuropsychiatric condition characterized by obsessions and/or compulsions and arises from a complex