In the present study, we investigated possible alterations in red blood cell (RBC) deformability, plasma and whole blood viscosities (WBV) and hematological parameters in response to Yo-Yo intermittent recovery test level 1 (Yo-YoIR1) which is curren

Study Objectives: While the pathophysiology of Narcolepsy Type 1 (NT1) involves hypocretin deficiency, the microstructural electrophysiological features that distinguish NT1 from Narcolepsy Type 2 (NT2) remain incompletely defined. This study evaluated sleep microstructure with a six-subtype K-complex (KC) categorization (Gülhane Classification). Methods: Retrospective polysomnography from the December 2016–January 2026 clinical archive was reviewed. K-complex scoring defined the analyzed sample: NT1 n = 18, NT2 n = 13, and simple-snoring controls with AHI < 5 (n = 15). Complete overnight PSG summaries were available for NT1 n = 16 and NT2 n = 13. Six visually defined KC subtypes (Isolated-KC, KC-Burst, KC-Sigma, KC-Delta, Polyphasic-KC, and Polyphasic-KC-Sigma) were compared across Descendant, Intermediate, and Ascendant portions of the NREM cycle. Results: Compared with controls, NT1 showed lower Isolated-KC and KC-Sigma counts across cycle periods (p < 0.05). NT1 also had higher wake after sleep onset (WASO; 34.7 ± 21.1 min) and lower sleep efficiency (90.7 ± 5.4%) than NT2. NT1–NT2 differences that reached p < 0.05 were KC-Delta in the Intermediate (p = 0.015) and Descendant (p = 0.030) periods, Isolated-KC in the Intermediate period (p = 0.039), and KC-Sigma in the Descendant period (p = 0.041). Conclusions: In this retrospective cohort, morphological KC subtyping showed a coherent reduction in Isolated-KC and KC-Sigma in NT1. The findings are descriptive and require prospective validation; they do not constitute a standalone diagnostic test.

Objective: To compare the accuracy of 2 artificial intelligence models, ChatGPT and NotebookLM, in answering clinical questions regarding narcolepsy management. Methods: A set of 30 clinical questions was developed based on 2 reference documents: the 2021 European guideline on the management of narcolepsy in adults and children, and the American Academy of Sleep Medicine clinical practice guideline for the treatment of central disorders of hypersomnolence. Both models were queried with the question set. Three independent scorers evaluated the responses across 4 categories (Accuracy, Evidence Reasoning, Additional Information, and Information Integration) using a binary scale (1 = criterion met, 0 = criterion not met). Interrater reliability was assessed using Cohen kappa and Fleiss kappa. Performance comparisons between the models were analyzed using the McNemar test, Wilcoxon signed-rank test, and Mann-Whitney U test, while scorer consistency was checked via the Friedman test. Results: ChatGPT achieved 261 of 360 positive ratings (72.5%), whereas NotebookLM achieved 179 of 360 (49.7%). ChatGPT scored significantly higher than NotebookLM in Accuracy (P < .001) and Evidence Reasoning (P < .001). No statistically significant differences were observed between the 2 models in Additional Information or Information Integration. Conclusion: ChatGPT demonstrated superior accuracy and internal consistency in answering narcolepsy-related clinical questions compared with NotebookLM. However, neither model showed high proficiency in providing necessary additional clinical information.