Obstructive Sleep Apnea-hypopnea Syndrome Research Articles

With the continuous development of digital technology, three-dimensional (3D) facial scanning technology is becoming more and more widely used in dentistry, where it enhances the precision of various processes of clinical diagnosis and treatment, and provides new perspectives and tools for dental research and application. Nowadays, 3D facial scanning technology meets clinical needs in terms of accuracy, and most facial scanners have an accuracy of 140-1 330 μm, and the 3D facial scanning related applications of smartphones can also meet the clinical application needs to a certain extent. In the clinical field, the 3D scanner can be combined with a variety of technologies to achieve multi-modal data fusion and create virtual patients, and its prediction rate in obstructive sleep apnea hypopnea syndrome is as high as 91%, which has great clinical application value. This article aims to explore the research status, clinical applications, and accuracy analysis of 3D facial scanning techniques in the field of dentistry.

The impact of preoperative carbohydrate ingestion one hour before surgery on children's preoperative anxiety and postoperative pain under ultrasound monitoring: A randomized controlled trial.

Atraric acid attenuates chronic intermittent hypoxia-induced lung injury by inhibiting ferroptosis through activation of the NRF2 pathway.

Evaluating the impact of obstructive sleep apnea-hypopnea syndrome on irritable bowel syndrome.

Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) is a prevalent systemic disorder affecting approximately 1 billion people worldwide, associated with severe outcomes such as sudden death and traffic accidents. Despite its significant impact, OSAHS is frequently underdiagnosed. The current gold standard for assessing OSAHS severity, overnight polysomnography, is both costly and inconvenient. This study aims to develop an automated method for detecting apnea and hypopnea events using a temporal convolutional network (TCN) to improve diagnostic accuracy and reduce computational costs. We introduce a novel Temporal Convolutional Network with a Linearly Scalable Attention Mechanism (ECG-TCN) designed to simultaneously detect both apnea and hypopnea events. The model was trained and validated using the University College Dublin Sleep Apnea Database. The performance of ECG-TCN was evaluated based on per-segment classification accuracy and generalization capacity. The ECG-TCN model achieved an accuracy of 91.6% in per-segment classification, demonstrating superior performance compared to traditional classification models. Additionally, the model exhibited high generalization capacity, indicating its robustness across different datasets. This study presents the first application of a temporal convolutional network with a linearly scalable attention mechanism for the simultaneous detection of apnea and hypopnea events. The ECG-TCN model offers a cost-effective and accurate alternative to traditional diagnostic methods, with the potential to enhance the early detection and management of OSAHS.

JWA mediates oxidative stress to promote vascular endothelial repair in obstructive sleep apnea-hypopnea syndrome.

Modified uvulopalatopharyngoplasty: "8" suture abduction-suspension of the palatopharyngeal muscle.

Introduction: Obstructive sleep apnea-hypopnea syndrome (OSAHS) is characterized by total (apnea) or partial (hypopnea) obstruction of the upper airway during sleep, resulting in hypoxemia and hypercapnia and leading to compensatory respiratory effort. Symptoms include loud snoring, nocturnal choking, witnessed apneas, frequent awakenings, daytime sleepiness, and cardiovascular comorbidities. Diagnosis is based on patient history, clinical examination, polysomnography, and upper airway computed tomography. This study aimed to evaluate pre- and postoperative changes in the apnea-hypopnea...

BACKGROUNDObstructive sleep apnea-hypopnea syndrome (OSAHS) is a highly prevalent sleep-related respiratory disorder associated with serious health risks. Although polysomnography is the clinical gold standard for diagnosis, it is expensive, inconvenient, and unsuitable for population-level screening due to the need for professional scoring and overnight monitoring.AIMTo address these limitations, this review aims to systematically analyze recent advances in deep learning–based OSAHS detection methods using snoring sounds, particularly focusing on graphical signal representations and network architectures.METHODSA comprehensive literature search was conducted following the PRISMA 2009 guidelines, covering publications from 2010 to 2025. Studies were included based on predefined criteria involving the use of deep learning models on snoring sounds transformed into graphical representations such as spectrograms and scalograms. A total of 14 studies were selected for in-depth analysis.RESULTSThis review summarizes the types of signal modalities, datasets, feature extraction methods, and classification frameworks used in the current literatures. The strengths and limitations of different deep network architectures are evaluated.CONCLUSIONChallenges such as dataset variability, generalizability, model interpretability, and deployment feasibility are also discussed. Future directions highlight the importance of explainable artificial intelligence and domain-adaptive learning for clinically viable OSAHS diagnostic tools.

ObjectiveTo develop and validate a clinical prediction model for olfactory dysfunction in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), evaluating the combined predictive value of polysomnography (PSG) parameters and clinical symptoms.MethodsWe retrospectively analyzed 546 OSAHS patients, including 420 from the Affiliated Hospital of North Sichuan Medical College were [randomly divided into training (n = 294) and internal validation (n = 126) sets], and 126 from the Sixth People's Hospital of Chengdu (external validation set). All patients underwent overnight PSG for sleep parameter assessment and Sniffin' Sticks tests for olfactory evaluation. Predictors were selected using LASSO regression with subsequent logistic regression modeling, followed by nomogram construction. Model performance was assessed through ROC analysis, calibration curves and DCA curves.ResultsAmong 546 enrolled patients, with OSAHS were included in this study. The overall olfactory dysfunction incidence was 38.64% (211/546). Multivariable analysis identified seven independent predictors: gender, age, AHI, N3%, REM%, TS90%, and MoCA. The predictive efficacy AUC of the training set model was 0.832 (95% CI: 0.784–0.880); good calibration (slope = 0.89, Hosmer–Lemeshow P = 0.41); and clinical utility across threshold probabilities of 0.06–0.97.ConclusionOur prediction model constructed based on gender, age, AHI, N3%, REM%, TS90%, and MoCA can effectively identify OSAHS patients at high risk for olfactory dysfunction. With robust discrimination and calibration, this tool provides a clinically useful, non-invasive method for early risk stratification and intervention planning.

PurposeThis study aimed to clarify the value of upper airway dynamic magnetic resonance (UADMR) in assessing the level and degree of obstruction in the upper airway during sleep in patients with obstructive sleep apnea hypopnea syndrome (OSAHS).Patients and MethodsPatients with OSAHS diagnosed by polysomnography (PSG) from February 2025 to March 2025 were included in this study, and UADMR was performed to detect the obstructive levels (palatopharyngeal level, root of tongue level, and epiglottic level) and assess the degree of obstruction at the root of the tongue and epiglottic levels. Subgroup analyses were performed according to different obstructive characteristics and the results of UADMR were assessed for correlation analysis with the results of PSG or manual titration of positive airway pressure. This study used Müller maneuver to simulate the airway collapse during sleep.ResultsA total of 34 OSAHS patients were included in this study. It was observed that the hypopnea index, apnea hypopnea index, and tongue axial change ratio were significantly higher in OSAHS patients with multiple layers of airway obstruction than in those with palatopharyngeal obstruction only. The anteroposterior diameter change ratio at the level of the root of the tongue and the area change ratio at the level of the epiglottis in patients with OSAHS were positively correlated with the results of the PSG, and the tongue axial change ratio was positively associated with the results of the manual titration of positive airway pressure.ConclusionUADMR is an effective method for assessing the level and degree of upper airway obstruction during sleep in patients with OSAHS.

ObjectiveTo examine the effects of metabolic and bariatric surgery on physical outcomes, mental health, and quality of life (QOL) among obese patients in China.MethodsIncluded were 182 patients who underwent laparoscopic sleeve gastroplasty (LSG, a form of metabolic and bariatric surgery). Effects on postoperative weight, waist circumference, hip circumference, BMI, and the percentage of excess weight loss (%EWL) were observed during the 12months after surgery. Changes in physical comorbidities were also determined. Anxiety symptoms were assessed by the Self-rated Anxiety Scale (SAS); depressive symptoms by the Self-rated Depression Scale (SDS); and QOL by the SF-36 scale. Assessment points were at baseline and 3, 6, and 12months after surgery.ResultsSignificant improvements were observed in weight, waist circumference, hip circumference, BMI, and %EWL after surgery (P < 0.05). The remission rates for hypertension, diabetes, fatty liver, hyperlipidemia, and obstructive sleep apnea-hypopnea syndrome (OSAHS) were 89.5%, 95.5%, 82.6%, 81.7%, and 61.7%, respectively, 1 year after surgery (P < 0.05). Anxiety (SAS) and depressive (SDS) symptoms decreased significantly over time (P < 0.05). SF-36 scores in the physical and psychological domains increased significantly (P < 0.05). However, overall SF-36 scores were higher at 6months compared to 12months (P < 0.05).ConclusionLaparoscopic sleeve gastroplasty results in significant weight loss in the short term, which can improve anxiety symptoms, depressive symptoms, and QOL in obese patients. However, some social and psychological improvements appear to plateau at 6months following surgery.

BackgroundNumerous studies have confirmed a significant association between obstructive sleep apnea hypopnea syndrome (OSAHS) and both the increased prevalence and incidence of atrial fibrillation (AF). This study advanced the endpoint event to complex atrial arrhythmias, a group that potentially develops into AF. It innovatively used non-invasive P-wave parameters (PWPs) as predictive indicators for the occurrence of complex atrial arrhythmias in OSAHS, thereby achieving early identification.MethodsA retrospective analysis was performed on the medical records of patients presenting with sleep disorders who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University between June 2019 and June 2022. Based on their apnea-hypopnea index (AHI), patients were categorized into control, mild, moderate, and severe OSAHS groups. Clinical characteristics, PWPs, occurrences of atrial arrhythmias, and echocardiographic findings were collected for analysis. Using the Kleiger grading method, atrial arrhythmias were classified into simple and complex groups. Analysis of variance (ANOVA) was employed to examine differences among the groups, while independent sample t-tests were used for pairwise comparisons. Logistic regression analysis was applied to identify independent risk factors contributing to complex atrial arrhythmias, and receiver operating characteristic (ROC) curves were generated to evaluate the predictive value of PWPs.ResultsPatients with severe OSAHS exhibited significantly prolonged P-wave duration (PWD) and elevated Macruz Index compared to those with mild or moderate OSAHS (p < 0.01), while the P terminal force in lead V1 (PtfV1) was notably higher in moderate and severe groups relative to the mild and control groups (p < 0.01). Logistic regression analysis identified PtfV1 (odds ratio [OR] = 1.61) and the Macruz Index (OR = 2.95) as independent predictors of complex atrial arrhythmias. Furthermore, ROC curve analysis demonstrated that both PtfV1 and the Macruz Index had moderate predictive value, with area under the curve (AUC) values of 0.701 and 0.681, respectively, for identifying complex atrial arrhythmias.ConclusionPWPs, especially the PtfV1 and Macruz index, provide a powerful non-invasive predictor of atrial arrhythmia risk in patients with OSAHS.

ABSTRACTBackgroundEpigenetic regulation significantly affects immune responses in lung adenocarcinoma (LUAD). However, the role of RNA N6‐methyladenosine (m6A) modification, especially in obstructive sleep apnea‐hypopnea syndrome (OSAHS) within LUAD, is not well understood.MethodsThis study examined m6A modification patterns in 973 LUAD patients using 23 regulatory genes. Unsupervised clustering categorized patients by m6A profiles, quantified by an m6A score. Associations with clinical outcomes, including survival, immune infiltration, tumor microenvironment, OSAHS incidence, and drug sensitivity, were analyzed.Resultsm6A regulator mutations were linked to LUAD, revealing two distinct modification patterns and three gene clusters, all tied to immune phenotypes and clinical outcomes. Higher m6A scores correlated with lower survival, more frequent OSAHS, and increased immune activity. Patients with high m6A scores were more sensitive to PD‐L1/PD‐1 inhibitors and Phenformin, while those with low scores responded better to Cisplatin, Epothilone B, and Talazoparib.ConclusionAnalyses confirmed m6A modification's role in LUAD progression, immune regulation, and outcomes. The m6A score is a valuable prognostic marker associated with survival, OSAHS incidence, and drug sensitivity. These findings highlight m6A's potential as a therapeutic target and biomarker, particularly for LUAD patients with OSAHS.

Obstructive sleep apnea–hypopnea syndrome (OSAHS) is a prevalent sleep disorder strongly linked to increased cardiovascular and metabolic risk. While prior studies have explored snore-based analysis for OSAHS, they have largely focused on either detection or classification in isolation. Here, we present a two-stage framework that integrates precise snoring event detection with deep learning-based classification. In the first stage, we develop an Adaptive Multi-Feature Fusion Endpoint Detection algorithm (AMFF-ED), which leverages short-time energy, spectral entropy, zero-crossing rate, and spectral centroid to accurately isolate snore segments following spectral subtraction noise reduction. Through adaptive statistical thresholding, joint decision-making, and post-processing, our method achieves a segmentation accuracy of 96.4%. Building upon this, we construct a balanced dataset comprising 6830 normal and 6814 OSAHS-related snore samples, which are transformed into Mel spectrograms and input into ERBG-Net—a hybrid deep neural network combining ECA-enhanced ResNet18 with bidirectional GRUs. This architecture captures both spectral patterns and temporal dynamics of snoring sounds. The experimental results demonstrate a classification accuracy of 95.84% and an F1 score of 94.82% on the test set, highlighting the model’s robust performance and its potential as a foundation for automated, at-home OSAHS screening.

Objective:To analyze the efficacy, influencing factors, and risk warning of multi-plane low-temperature plasma radiofrequency ablation（MLT-RFA） in the treatment of obstructive sleep apnea hypopnea syndrome（OSAHS）. Methods:A total of 118 OSAHS patients admitted from October 2022 to June 2024 were selected as the research subjects. They were divided into mild group（n=46）, moderate group（n=52）, and severe group（n=20） according to the severity of their condition. MLT-RFA treatment was used for all patients. After surgery, the results of polysomnography（PSG） and the changes in the Calier Sleep Apnea Quality of Life Index（SAQLI） were observed before and after treatment. The incidence of complications after treatment was recorded, and the clinical efficacy of the patients was evaluated. At the same time, they were divided into a treatment effective group（n=106） and an ineffective group（n=12） according to their effects. The general clinical data of the two groups were compared, and binary logistics regression analysis was conducted to identify independent factors that affect treatment efficacy and construct a model. ROC curve analysis was used to evaluate the diagnostic efficacy of the model. Results:The treatment effectiveness rate of the mild group was 93.48%, the moderate group was 90.38%, and the severe group was 80.00%. There was no statistically significant difference in the treatment effectiveness rate among the three groups（P>0.05）. The AHI of the mild group, moderate group, and severe group increased sequentially, while the LSaO2and SAQLI scores decreased sequentially. After treatment, the AHI of all three groups decreased compared to before treatment, while the LSaO2and SAQLI scores increased compared to before treatment, and the differences were statistically significant（P<0.05）. The pre-treatment AHI of the effective group was lower than that of the ineffective group, and the pre-treatment LSaO2and SAQLI were higher than those of the ineffective group, with statistically significant differences（P<0.05）. Pre-treatment LSaO2and pre-treatment SAQLI are independent factors affecting the efficacy of MLT-RFA（P<0.05）. The AUC of pre-treatment LSaO2, pre-treatment SAQLI, and combined prediction were 0.907, 0.763, and 0.947, respectively, with sensitivities of 0.896, 0.840, and 0.917, and specificities of 0.833, 0.667, and 0.887, respectively. Conclusion:MLT-RFA has a significant effect on the treatment of OSAHS, and the AHI, LSaO2, and SAQLI of patients before treatment can predict the treatment effect, with LSaO2 and SAQLI being independent influencing factors. The combinerd prediction model exhibits high diagnostic efficiency, sensitivity, and specificity.

Use of the da Vinci robot in surgery for obstructive sleep apnea-hypopnea syndrome: A case report

ImportanceCervical tracheal characteristics in OSAHS patients remain unclear.ObjectiveThis study aimed to analysis the cervical tracheal characteristics of obstructive sleep apnoea-hypopnoea syndrome (OSAHS) patients using ultrasound.MethodsOne hundred and thirteen patients with OSAHS and 113 age-, sex-, height-, weight-, and BMI-matched healthy controls underwent cervical tracheal sonographic examination. Tracheal wall motion was monitored and sonographic measurements of the airway lumen were obtained during quiet respiration, deep respiration, and the Müller maneuver. Tracheal displacement during the Müller maneuver was monitored and quantified. Measurements were compared between groups.ResultsAdequate visualization of cervical tracheal dimensional changes during the Müller maneuver was obtained in 91.15% of patients with OSAHS and 99.12% of healthy subjects. The cervical trachea had the smallest lateral diameter during the Müller maneuver (P < 0.001) in patients with OSAHS and healthy controls. The cervical trachea moved caudally during the Müller maneuver, and its peak tracheal displacement was greater in patients with OSAHS than that in the healthy controls (P < 0.001). There was a positive relationship between peak tracheal displacement during the Müller maneuver and apnoea-hypopnoea index (AHI; P < 0.001).ConclusionOSAHS patients have greater caudal tracheal displacement and greater tracheal size than healthy controls. Ultrasonography may be especially suited for precisely evaluating the trachea and can provide a reference for the airway assessment and management of OSAHS.

Objective: This study aimed to evaluate the association between obstructive sleep apnea-hypopnea syndrome (OSAHS) and reflux esophagitis (RE). Methods: This cross-sectional study retrospectively analyzed 218 patients diagnosed with OSAHS by polysomnography (PSG) and who also had undergone gastroscopy at the First People's Hospital of Yunnan Province from January 2021 to December 2021. The cohort comprised 91 males and 127 females, aged from 19 to 78 years (40.7±13.2). Clinical data, PSG parameters, and gastroscopy findings were collected. The prevalence of RE among OSAHS patients was calculated, potential risk factors for RE were evaluated. Differences in PSG parameters between patients with and without RE were analyzed. Statistical analyses were conducted using SPSS 26.0. Results: The prevalence of RE in OSAHS patients was 20.6% (45/218). Males had a significantly higher RE prevalence than females (31.9% vs. 12.6%, χ2=12.02, P<0.05). The difference remained significant after adjusting for confounding factors (34.9% vs. 11.1%, χ2=10.08, P<0.05). No significant variation in RE prevalence was observed across age groups. However, after adjusting for confounding factors, a significant difference was found between overweight and obese BMI groups (12.5% vs. 29.2%, χ2=4.04, P<0.05). When stratified by apnea-hypopnea index (AHI) severity, RE prevalence increased progressively in mild (7.1%), moderate (18.8%), and severe (30.1%) groups, with statistically significant differences (χ2=11.45, P<0.05). Positive correlations were found between RE and male sex, AHI, longest apnea time (LAT), and time spent with oxygen saturation below 90% (TS90%) (rs=0.24, 0.18, 0.17, 0.14, respectively, P<0.05). Regression analysis showed that identified male sex was the primary independent predictor of RE. Patients with RE exhibited higher AHI, TS90%, and LAT compared to those without RE (P<0.05). Conclusion: This single-center hospital-based study revealed a relatively high prevalence of reflux esophagitis (20.6%) among patients with OSAHS. Male sex was identified as the main independent factor associated with RE. Furthermore, RE prevalence increased with greater AHI, BMI, LAT and TS90%.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is significantly correlated with hypertension. This investigation aimed to explore the effect of ferroptosis on OSAHS-hypertension. Ferroptosis-associated genes were screened based on the GSE205050 dataset and FerrDb database. An OSAHS-hypertension model was established by exposing Sprague Dawley rats to chronic intermittent hypoxia for 8 weeks, and human umbilical vein endothelial cells (HUVECs) were exposed to intermittent hypoxia invitro. CDC25A was overexpressed using recombinant adeno-associated virus invivo and plasmid transfection invitro. Ferroptosis markers, oxidative stress indicators, blood pressure, abdominal aortic tissue histopathology, and endothelial cell viability/apoptosis were then assessed. Six ferroptosis-associated hub genes were identified, including CDC25A, EZH2, PARP1, HELLS, FANCD2, and RRM2, all of which were lowly expressed. In the rat model of OSAHS-hypertension, overexpression of CDC25A significantly reduced systolic and diastolic blood pressure as well as vascular wall thickness, while increasing α-SMA expression. Biochemical analyses showed that CDC25A decreased malondialdehyde (MDA) and Fe2+ levels while increasing glutathione (GSH), superoxide dismutase (SOD), and ferroptosis-associated proteins (FTH1, SLC7A11, GPX4). CDC25A overexpression in HUVECs ameliorated hypoxia-induced endothelial dysfunction by inhibiting ferroptosis and apoptosis and promoting cell survival; however, these protective effects were significantly abrogated by co-treatment with erastin. CDC25A inhibits OSAHS-hypertension progression and modulates ferroptosis-related pathways. This study identifies ferroptosis as a potential therapeutic target in OSAHS-associated hypertension, with CDC25A acting as a key regulatory factor.