BACKGROUND:Sleep-disordered breathing (SDB) in children is increasingly associated with cognitive impairment, though the mechanisms remain unclear. This study explored associations among SDB severity, sleep architecture, metabolic biomarkers, and declarative memory. METHODS:A total of 117 children who presented with snoring were enrolled in the study. All participants underwent overnight polysomnography. Declarative memory was assessed using a modified paired-associate learning test. Fasting venous blood samples were collected to measure serum levels of leptin, adiponectin, and soluble leptin receptor. The leptin/adiponectin (L/A) ratio and free leptin index (FLI) were also calculated. RESULTS:REM-related respiratory disturbance indices (OAHIREM, AHIREM, ODIREM) were significantly negatively correlated with immediate and delayed recognition accuracy (p < 0.05). Higher levels of leptin and FLI were significantly associated with poorer memory performance (p < 0.05), whereas adiponectin levels were lower in children with severe SDB and negatively correlated with multiple respiratory parameters. The L/A ratio and FLI were positively correlated with REM-specific respiratory disturbances and negatively associated with cognitive performance. CONCLUSION:Children with SDB show REM sleep disruption and metabolic dysregulation linked to impaired declarative memory. Leptin resistance may mediate these effects via hippocampal dysfunction. Findings underscore the importance of early detection and metabolic targeting to prevent cognitive deficits in pediatric SDB. IMPACT:REM-predominant sleep-disordered breathing is associated with poorer declarative memory performance in children. Higher leptin/free leptin index (FLI) is linked to worse memory outcomes in this pediatric cohort. We highlight sleep-stage specificity by evaluating REM-related PSG indices (including REM/NREM ratios), beyond global AHI/OAHI measures. We integrate PSG phenotyping with metabolic biomarkers (leptin, FLI, adiponectin) to better characterize neurocognitive risk. Our findings support improved risk stratification and motivate longitudinal and interventional studies targeting REM-related SDB and metabolic dysfunction to protect cognition.

ObjectiveTo investigate the neuroelectrophysiological characteristics of children with obstructive sleep apnea (OSA) accompanied by attention deficit hyperactivity disorder (ADHD)-like symptoms using attention network test (ANT) task-state electroencephalography analysis.MethodsAll participants completed the Attention Deficit Hyperactivity Disorder rating scale. Electroencephalography data were collected using 32 leads while the participants performed the ANT, followed by polysomnography.ResultsOf the 87 children, 21 were in the control group, 56 were in the OSA group, and 10 were in the OSA accompanied by ADHD-like symptoms group (OSA-co-ADHD). Each group had similar response time and accuracy for the different ANT conditions. At the O2 electrode, compared with the controls, the latency of the P1 component in the OSA group was shorter (P < 0.05). During the alerting network stage, in the PZ electrode, the beta band energy of the OSA-co-ADHD group was greater than that of the control group (Z   -3.067, P = 0.006). During the executive control network stage, at the CZ electrode, the OSA-co-ADHD group exhibited higher alpha band energy than the OSA group (P < 0.05). During the alerting and orienting network phases, the connections of multiple brain regions changed (all P < 0.05), while the executive control network remained unchanged.ConclusionExcessive activation of the alerting network, reorganization of the orienting network in the brain, and compensation for inhibitory control during the process of the executive control network may be important characteristics of children with OSA accompanied by ADHD-like symptoms.

This real-world study evaluated the effectiveness and safety of pitolisant, a histamine H₃ receptor inverse agonist, in Chinese pediatric patients with narcolepsy. This retrospective, observational, single-center study enrolled 40 pediatric patients treated with pitolisant between May and August 2024. Data collection encompassed demographic profiles, sleep questionnaires (Pediatric Epworth Sleepiness Scale [PESS] and Ullanlinna Narcolepsy Scale [UNS]), polysomnographic parameters, concomitant medications, and laboratory evaluations. Efficacy endpoints included changes in PESS and UNS scores from baseline, and caregiver-reported improvement in core narcolepsy manifestations other than excessive daytime sleepiness (EDS). Safety was assessed through adverse event monitoring. The cohort had a mean age at onset of 9.1 ± 1.9 years, a median disease duration of 6 (IQR: 2, 24)months and a mean follow-up period of 5.6 months (range:4.5–7months). Patients received either pitolisant monotherapy (n = 20, mean dose: 12.8 mg) or add-on therapy (n = 20, mean dose: 9.4 mg). Post-treatment, significant reductions were observed in both PESS (14.3 ± 2.8 vs. 10.7 ± 2.6, P < 0.001) and UNS scores (22.8 ± 5.8 vs. 17.0 ± 3.4, P < 0.001). Subgroup analysis demonstrated a greater PESS improvement in patients with baseline scores > 13 compared to those with scores ≤ 13 (P < 0.005). Additionally, 73.7

STUDY OBJECTIVES:Due to the high cost, complexity, and workload of polysomnography, a radar-based sleep monitoring device, QSA600, has been developed as a more simplified alternative for children. This study evaluates its agreement with polysomnography for obstructive sleep apnea diagnosis and sleep staging. METHODS:This diagnostic accuracy study included 281 children (1-18 years) who underwent simultaneous polysomnography and QSA600 monitoring at Beijing Children's Hospital from September-November 2023. QSA600 recordings were automatically analyzed using a deep learning model, while polysomnography data were manually scored. RESULTS:The obstructive apnea-hypopnea index (OAHI) obtained from QSA600 and polysomnography demonstrates a high level of agreement with an intraclass correlation coefficient of 0.945 (95% CI: 0.93-0.96). Bland-Altman analysis indicated that the mean difference of obstructive apnea-hypopnea index between QSA600 and polysomnography was -0.10 events/h (95% CI: -11.15 to 10.96). The deep learning model evaluated through cross-validation showed good sensitivity (81.8%, 84.3%, and 89.7%) and specificity (90.5%, 95.3%, and 97.1%) values for diagnosing children with OAHI >1, OAHI >5, and OAHI >10. The area under the receiver operating characteristic curve was 0.923, 0.955, and 0.988, respectively. For sleep stage classification, the model achieved Kappa coefficients of 0.854, 0.781, and 0.734, with corresponding overall accuracies of 95.0%, 84.8%, and 79.7% for Wake-Sleep classification, Wake-REM-Light-Deep classification, and Wake-REM-N1-N2-N3 classification, respectively. CONCLUSIONS:QSA600 has demonstrated high agreement with polysomnography in diagnosing obstructive sleep apnea and performing sleep staging in children. The device is portable, low-burden, and suitable for follow-up and long-term pediatric sleep assessment.

Detecting sleep spindles in Electroencephalography (EEG) is crucial for assessing cognitive function and neurological disorders. How to provide robust detection across diverse datasets and clinical settings has long remained an open problem. Aiming at this challenge, this study introduces BiLSTM-Attention Spindle neT (BLAST), a deep learning framework focusing on the localization of individual spindle events operating in three phases: (1) Representation: BLAST first enhances morphological and temporal representations through the U-Net to learn hierarchical features, followed by BiLSTM (Bidirectional Long Short-Term Memory) to model the intricate temporal dynamics within EEG; (2) Highlighting: It then enforces attention to spindle bodies with an attention mechanism, which diminishes irrelevant background signals and magnifies salient features that discriminate spindles from other activities; and (3) Detection: These refined features are efficiently integrated through skip connections, preserving both abstract representations and fine-grained details for precise localization of each spindle event based on predictions at each sampling point. BLAST has been extensively evaluated against seven state-of-the-art counterparts (e.g., SEED and CDTSD) using three distinct datasets. The results demonstrate the following: (1) BLAST achieves the best overall performance, with precision, recall, and F1-score of 86.4 %, 86.9 %, and 85.9 %, respectively, on the publicly available MASS dataset; (2) It exhibits the highest consistency with expert evaluation on downstream tasks, such as spindle density and average duration, with correlation coefficients of 0.828 and 0.821, respectively; and (3) Transfer learning on the open-access DREAMS and private BCH-Children datasets further highlights BLAST's strong generalization potential across various clinical settings.

Obstructive sleep apnea syndrome (OSA), characterized by chronic intermittent hypoxia (CIH), is linked to cognitive impairment and neuronal damage. Leptin, a peptide hormone involved in energy metabolism, has shown neuroprotective effects in various neurological disorders, yet its role in CIH-induced cognitive impairment remains unclear. This study used a CIH mouse model to investigate the effects of leptin on cognitive function and neuronal damage, with a particular focus on Sirtuin 1 (SIRT1) as a potential molecular mediator. Mice were exposed to CIH and treated with leptin, with or without a SIRT1 inhibitor. Cognitive performance was assessed using the Morris Water Maze (MWM) and Y-maze tests. Neuronal damage was evaluated by H&E and Nissl staining, while oxidative stress was measured by Reactive Oxygen Species (ROS) and malondialdehyde (MDA) levels, and Superoxide Dismutase (SOD) activity. SIRT1 expression was determined via western blot analysis. Leptin treatment significantly improved cognitive performance in CIH mice, evidenced by reduced escape latency, increased platform crossings in the MWM, and enhanced alternation rates in the Y-maze. Additionally, leptin mitigated CIH-induced neuronal damage and oxidative stress, demonstrated by improved neuronal structure and reduced ROS and MDA levels. These benefits were associated with increased SIRT1 expression, whereas SIRT1 inhibition reversed leptin's positive effects, highlighting the crucial role of SIRT1 in mediating leptin's neuroprotective effects. These findings suggest that leptin alleviates CIH-induced cognitive dysfunction and neuronal damage through SIRT1 upregulation, offering insights into potential therapeutic mechanisms and future clinical applications.

ABSTRACT To investigate the effective connectivity between the bilateral insulae and other regions of the whole brain in children with obstructive sleep apnea (OSA), and to reveal the relationships between these abnormal connections and cognitive dysfunction in this condition. Resting‐state functional magnetic resonance imaging (rs‐fMRI) data and clinical variables were collected from 55 children with OSA [5.0 (5.0, 8.0) years, 32 males, 28 pre‐school children] and 25 healthy controls [6.0 (5.0, 9.0) years, 11 males, 9 pre‐school children], matched for age, gender, and education. Rs‐fMRI data were analysed to investigative group‐difference in the effective connectivity between the bilateral insulae and other regions of the brain of children with OSA with those of controls. Spearman correlation analysis was conducted between these abnormal connections and clinical variables among children with OSA. Compared with controls, children with OSA showed abnormal clinical variables (i.e., increased OAHI, AHI, OAI, HI, ODI, time of SpO 2 < 90%, total AI, and respiratory‐related AI, while decreased minimal SpO 2 , FIQ, VIQ, and PIQ). Additionally, significant alterations were observed in the effective connectivity between the bilateral insulae and other regions of brain, such as frontal, parietal, occipital, and cerebellum and so forth. Furthermore, the mean values of the effective connectivity in children with OSA were significantly correlated with several sleep‐related and neurocognitive parameters. There exist abnormal causal interactions between the bilateral insulae and other regions throughout the brain in OSA children, accompanied by impaired cognitive function, suggesting that the former may be a potential neural mechanism underlying the latter.

Objective:To investigate the differences in assessment of clinical characteristics between children with obstructive sleep apnea (OSA) diagnosed according to the 2007 and 2020 guidelines and those without OSA, together with the relationships between polysomnography (PSG) parameters and cognitive tests scores in preschool and school-aged children with OSA. Methods:Eighty children were totally recruited and divided into OSA and non-OSA groups based on two distinct guidelines, with further subclassification into preschool and school-aged subgroups. Differences in PSG parameters and cognitive tests scores between groups and subgroups were analyzed and compared, followed by partial correlation analysis to determine the correlations between these characteristics. Results:Compared to the 2007 guideline, the 2020 guideline demonstrated more significant between-group differences in clinical characteristics assessments, especially verbal intelligent quotient (VIQ). For preschool children in the OSA and non-OSA subgroups, there were significant differences in PSG parameters and Block Diagram between the two guidelines. Additionally, the 2007 guideline showed difference in Picture Vocabulary, where the 2020 guideline exhibited differences in performance IQ (PIQ) and Geometric Figure For school-aged children in the OSA and non-OSA subgroups, both guidelines showed significant differences in PSG parameters, full-scale IQ (FIQ) and Block Diagram. The 2007 guideline had significant differences in PIQ, while the 2020 guideline had difference in VIQ. Furthermore, significant correlations were observed between PSG parameters and cognitive tests scores across different subgroups. Conclusion:The 2020 guideline has advantages in assessing the clinical characteristics of children with OSA, especially for verbal function, and is worthy of clinical promotion and application.

BACKGROUND:Adenoid hypertrophy significantly impacts pediatric upper airway function, but its molecular mechanisms remain elusive. This study characterizes the cellular landscape in adenoid hypertrophy using single-cell RNA sequencing (scRNA-seq). METHODS:Adenoid tissue samples were obtained from pediatric patients diagnosed with either severe hypertrophic adenoids (SHA) and mild to moderate hypertrophic adenoids (MHA). scRNA-seq was performed to generate transcriptomic profiles at single-cell resolution. Clustering and differential expression analyses were performed, with pseudotime trajectory, cell-cell communication, and copy number variation analyses. RESULTS:Analysis of 45,917 single-cell transcriptomes revealed 18 distinct cell clusters with B cells predominating. SHA samples exhibited significant enrichment of naïve B cells with corresponding reduction of memory B cells, suggesting disrupted B cell differentiation. Pseudotime analysis confirmed B cell differentiation disruption in SHA, with cells accumulating at the naïve B cell stage. Notably, paired box gene 5 (PAX5), a master transcription factor essential for B cell lineage commitment but typically downregulated during terminal differentiation, was significantly upregulated in SHA. CNV analysis showed no evidence of clonal expansion or malignant transformation. CONCLUSIONS:Our findings reveal that adenoid hypertrophy is characterized by impaired B cell differentiation with accumulation of naïve B cells and PAX5 overexpression, suggesting a differentiation blockade as a novel pathogenic mechanism. These insights offer potential therapeutic targets within the B cell maturation pathway, including modulation of PAX5 activity.

Objectives Identification of suitable biomarkers that facilitate the screening and evaluation of pediatric obstructive sleep apnea (OSA) and its severity was explored. Methods Data-independent acquisition quantitative proteomic analysis was employed to identify serum and urine proteins with differential expression patterns between children with OSA and controls. Differentially expressed proteins that gradually increased or decreased with the severity of OSA were retained as potential biomarkers and underwent ELISA validation. Results We found that with increasing severity of OSA, there was a gradual upregulation of 34 proteins in the serum and 124 proteins in the urine, along with a respective downregulation of 10 serum proteins and 64 urinary proteins in the initial cohort of 40 children. These proteins primarily participate in immune activation, the complement pathway, oxygen transport, and reactive oxygen metabolism. Notably, cathepsin Z exhibited a positive correlation with the obstructive apnea hypopnea index, whereas sex hormone-binding globulin (SHBG) was negatively correlated. These proteins were then validated by ELISA in an independent cohort (n=21). Circulating cathepsin Z and SHBG levels displayed acceptable diagnostic performance of OSA with AUC values of 0.863 and 0.738, respectively. Conclusions We identified two promising circulating proteins as novel biomarkers for clinical diagnosis and assessment of pediatric OSA severity. Furthermore, the comprehensive proteomic profile in pediatric OSA should aid in exploring the underlying pathophysiological mechanisms associated with this prevalent condition.

Objective To investigate the association between sleep quality/stability and memory in children with sleep-disordered breathing. Methods Children aged 5-12 years with suspected sleep-disordered breathing who visited the Sleep Center of Beijing Children's Hospital, from June 2022 to March 2023 were enrolled. All patients underwent polysomnography (PSG) and cardiopulmonary coupling monitoring (CPC) analysis based on the photoplethysmogram, and memory tests (immediate and delayed recognition and recall) before sleep and after sleep, respectively. In the CPC analysis, high frequency coupling (HFC) as percentage of total sleep time is stable sleep. A sleep quality index (SQI) integrates HFC, sleep duration and sleep fragmentation. The correlation between memory function and sleep quality/stability was analyzed. Cyclic variation in heart rate was quantified as a sleep apnea indicator (SAI). Results Patients were divided into three groups based on HFC: low (<60), moderate (60-80) and high (> 80). A total of 152 children were included in the study, 100 males and 52 females, with an average age of 8.2 ± 1.7years.HFC% was negatively correlated with AHI and OAHI (r: -0.32,p: <0.01; r: -0.31, p: <0.01), while LFC% was positively correlated with AHI and OAHI (r: 0.29, p: <0.01; r: 0.28, p: <0.01). The SQI and HFC was positively correlated with the delayed recall test score(r: 0.19, p: <0.05), and with the recognition consolidation rate (r: 0.23, p: < 0.05). In contrast, LFC was negatively correlated with delayed recall test score (r:0.19, p: <0.05), delayed recognition score (r:0.15,p<0.05), and recognition consolidation rate (r:0.21, p: <0.01). SAI was negatively correlated with Recognition consolidation rate score (r: -0.17, p: <0.05). Conclusions Sleep stability assessed via CPC may reflect a risk biomarker formemory function in children with OSA.

Objective: To investigate daytime attentional function in children with rapid eye movement (REM) sleep-related obstructive sleep apnea (OSA; REM-OSA) and its relationship with sleep electroencephalography theta/beta ratio (TBR). Methods: One-hundred-and-three children (aged 6-11 years) with snoring/mouth breathing were recruited from Beijing Children's Hospital. Participants completed the Attentional Networks Test for Interactions and Vigilance executive and arousal components (ANTI-Vea) and underwent polysomnography. Groups were classified according to the obstructive apnea/hypopnea index (OAHI): 20 non-OSA, 53 non-REM-OSA, and 30 REM-OSA. The TBR of the frontal, central, and occipital regions was analyzed. Results: Children with REM-OSA exhibited higher reaction time variability for executive vigilance and more errors during invalid condition ANTI-Vea trials than the non-REM-OSA group. The REM-OSA group exhibited higher TBRs in the frontal and central regions during all sleep stages than the non-OSA and non-REM-OSA groups and a higher TBR in the frontal and central region during REM sleep than the non-OSA group. Occipital TBR did not differ among the three groups. Reaction time variability for executive vigilance correlated positively with OAHI during REM sleep (OAHIREM). TBR in the frontal and central regions during all sleep stages and TBR in the frontal region during REM sleep correlated positively with the number of errors during invalid condition ANTIVea trials. Conclusions: Children with REM-OSA exhibited impaired attentional function, characterized by increased reaction time variability for executive vigilance and poorer performance in the invalid condition attention task trials. The OAHIREM and TBR reflect distinct dimensions of attentional impairment in pediatric OSA.

ObjectiveThe objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.Patients and methodsThis study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3–18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG). Participants’ data were randomly divided into a training set and a testing set at a ratio of 8:2. The elastic net algorithm was used for feature selection to simplify the model. Stratified 10-fold cross-validation was repeated five times to ensure the robustness of the results.ResultsFeature selection using Elastic Net resulted in 47 features for AHI ≥5 and 31 features for AHI ≥10 being retained. The machine learning model using these selected features achieved an average AUC of 0.73 for AHI ≥5 and 0.78 for AHI ≥10 when tested externally, outperforming models based on PSG questionnaire features. Linear Discriminant Analysis using the selected features identified OSA with a sensitivity of 44% and specificity of 90%, providing a feasible clinical alternative to PSG for stratifying OSA severity.ConclusionsThis study shows that a machine learning model based on children's clinical features effectively identifies OSA in children. Establishing a machine learning screening model based on the clinical features of the target population may be a feasible clinical alternative to nocturnal OSA sleep diagnosis.

Artificial intelligence (AI) is quickly advancing in the field of sleep medicine, which bodes well for the potential of actual clinical use. In this study, an analysis of the 2nd China Intelligent Sleep Staging Competition was conducted to gain insights into the general level and constraints of AI-assisted sleep staging in China. The outcomes of 10 teams from the children‘s track and 13 teams from the adult track were investigated in this study. The analysis included overall performance, differences between five different sleep stages, variations across subjects, and performance during stage transitions. The adult track's accuracy peaked at 80.46

We explored whether declarative memory consolidation is impaired in children with rapid eye movement sleep–related obstructive sleep apnea (REM-OSA) and investigated the correlation between memory consolidation and sleep-related respiratory parameters. Participants were children with habitual snoring aged 6–14 years and control children. Participants underwent polysomnography and declarative memory testing. Participants with snoring were categorized as primary snoring (PS), non-rapid eye movement sleep–related obstructive sleep apnea (NREM-OSA), stage-independent (SI)-OSA, and REM-OSA according to obstructive apnea-hypopnea index (OAHI), OAHI in REM sleep (OAHIREM), and OAHI in NREM sleep (OAHINREM). Declarative memory consolidation level was assessed by recall and recognition rates. There were 34 controls and 228 children with sleep-disordered breathing: 73 PS, 48 NREM-OSA, 59 SI-OSA, and 48 REM-OSA. Total arousal index was lower in the REM-OSA group than in the NREM-OSA group. In all groups, retest scores were higher than immediate test scores. Recall consolidation in PS, SI-OSA, and REM-OSA groups was lower than for controls and lower in REM-OSA than in NREM-OSA. There were no correlations between recall consolidation or recognition consolidation and OAHI, OAHINREM, oxygen desaturation index in REM sleep, total arousal index, or REM sleep percent. Recognition consolidation was negatively correlated with OAHIREM. Memory consolidation is impaired in children with REM-OSA compared with NREM-OSA and controls. There was no significant correlation between memory consolidation and OAHI, and recognition consolidation was negatively correlated with OAHIREM. It is important to pay attention to the OSA subtype in children.

The widespread prevalence of sleep problems in children highlights the importance of timely and accurate sleep staging in the diagnosis and treatment of pediatric sleep disorders. However, most existing sleep staging methods rely on one-dimensional raw polysomnograms or two-dimensional spectrograms, which omit critical details due to single-view processing. This shortcoming is particularly apparent in pediatric sleep staging, where the lack of a specialized network fails to meet the needs of precision medicine. Therefore, we introduce AFSleepNet, a novel attention-based multi-view feature fusion network tailored for pediatric sleep analysis. The model utilizes multimodal data (EEG, EOG, EMG), combining one-dimensional convolutional neural networks to extract time-invariant features and bidirectional-long-short-term memory to learn the transition rules among sleep stages, as well as employing short-time Fourier transform to generate two-dimensional spectral maps. This network employs a fusion method with self-attention mechanism and innovative pre-training strategy. This strategy can maintain the feature extraction capabilities of AFSleepNet from different views, enhancing the robustness of the multi-view model while effectively preventing model overfitting, thereby achieving efficient and accurate automatic sleep stage analysis. A "leave-one-subject-out" cross-validation on CHAT and clinical datasets demonstrated the excellent performance of AFSleepNet, with mean accuracies of 87.5% and 88.1%, respectively. Superiority over existing methods improves the accuracy and reliability of pediatric sleep staging.

Purpose:Chronic intermittent hypoxia (CIH) is considered one of the main pathophysiological mechanisms of obstructive sleep apnea (OSA). CIH can further lead to cognitive dysfunction by inducing processes such as neuroinflammation and oxidative stress. The hippocampus is primarily associated with cognitive functions such as learning and memory. This study aimed to explore the effects of CIH on cognitive function and hippocampal neurons in mice and to reveal its potential molecular mechanisms. Methods:SPF-grade C57BL/6J mice (n=36) were selected as subjects and divided into control, mild CIH, and severe CIH groups (12 mice per group). Cognitive function was assessed using the Morris water maze test, and hippocampal neuron numbers and morphological changes were observed using HE staining and Nissl staining. Additionally, differential genes and pathways were revealed through RNA sequencing (RNA-seq) and bioinformatics analysis. We examined oxidative stress-related biochemical markers in the hippocampal tissue and used Western Blot to verify changes in the expression of potential key genes. Statistical analyses were performed using ANOVA and post hoc tests to ensure robust comparisons between groups. Results:CIH mice exhibited significant cognitive impairment, including decreased learning and memory abilities. The severe CIH group had a longer escape latency compared to the mild CIH group (p < 0.001) and the control group (p < 0.01), while the mild CIH group took longer than the control group (p < 0.01). In the probe test, the severe CIH group showed a significant decrease in platform crossings (p < 0.01) and target quadrant dwell time (p < 0.05), while the mild CIH group exhibited a reduction in target quadrant dwell time (p < 0.05). Abnormal hippocampal neuron morphology was observed, with a significant reduction in hippocampal neurons (p < 0.05). RNA-seq analysis revealed numerous differentially expressed genes, mainly enriched in biological processes such as inflammation and oxidative stress, as well as multiple signaling pathways. Specifically, downregulated LepR, SIRT1, and Nrf2 genes were found to exacerbate oxidative stress and neuroinflammation, impairing neuronal integrity and cognitive function. Further validation showed increased oxidative stress levels in hippocampal tissue and downregulation of key gene expression. Western blot analysis confirmed significantly reduced expression of LepR (p < 0.01), SIRT1 (p < 0.001), and Nrf2 (p < 0.001) in the severe CIH group. Conclusion:While oxidative stress and inflammation are well-established mechanisms in CIH-induced cognitive impairment, our study provides novel insights by identifying the specific roles of LepR, SIRT1, and Nrf2 in this process. The downregulation of these key genes suggests potential new targets for therapeutic intervention. Importantly, the differential expression patterns observed in varying degrees of hypoxia severity highlight the potential for tailored therapeutic strategies that modulate these pathways in response to the intensity of hypoxic exposure. These findings offer unique opportunities for developing targeted therapies aimed at mitigating CIH-related cognitive decline and neural damage. However, a key limitation of this study is the exclusive use of animal models, which may not fully replicate human pathophysiology. Further studies are needed to validate these findings in clinical settings and to explore the regulatory relationships between the key genes involved.

Objective: To investigate how attention is affected in children with obstructive sleep apnea (OSA) using the attention network test (ANT) combined with event-related potential (ERP) and time-frequency analysis. Methods: Eighty-seven children aged 6-11 years with symptoms of snoring or mouth breathing during sleep were recruited from the Sleep Center of Beijing Children's Hospital from May to July, 2023. All participants completed the Mini-mental State Examination and Attention Deficit Hyperactivity Disorder rating scale. We acquired 32-lead electroencephalography (EEG) data while participants performed the ANT, followed by Polysomnography. Results: Of the 87 children, 21 had no OSA, 49 had mild OSA, and 17 had moderate to severe (MS) OSA. Each group had similar questionnaire scores, similar response time and accuracy for the different ANT conditions. There are alterations in the processing of three separate components of the attentional network in children with OSA. The amplitude of the N3 component at the FZ electrode in the MS OSA group was lower than that of the non-OSA and mild OSA groups (all P <0.05). In the executi control network phase, the energy of alpha band was higher in the MS OSA group than in the mild OSA group (Z=-2.624, P =0.026). The mean amplitude of the N3 component at the FZ electrode was correlated with the obstructive apnea-hypopnea index (OAHI) (r=0.232, P =0.038). Conclusion: Attention impairment was observed as a reduced N3 in the frontal area in the MS OSA group, which was correlated with the OAHI. However, questionnaire and behavioral performance did not differ significantly between groups. These findings suggest that the N3 amplitude is a sensitive neuroelectrophysiological marker of OSA-related cognitive impairment.