Purpose: Traditional craniofacial phenotyping of obstructive sleep apnea (OSA) relies on predefined anatomical hypotheses, often yielding incomplete assessments. This study aimed to apply multiple explainable deep learning (DL) approaches to explore novel craniofacial phenotypes. Patients and Methods: A multimodal DL model was trained using frontal and lateral facial images, lateral cephalograms, and demographic data from 130 participants (65 OSA patients diagnosed by overnight polysomnography, and 65 age-and sex-matched controls). Explainability analysis of the model's prediction was conducted in a subset of 56 participants per group using average face analysis, feature importance analysis, and gradient-weighted class activation mapping (Grad-CAM). Targeted measurements were then performed on the identified non-traditional regions to validate morphological differences. Results: The model achieved an area under the curve of 0.87. The explainability analysis identified not only anatomical structures that have been confirmed to predict OSA (such as the mandible, chin, and hyoid regions), but also the middle and upper facial third (e.g. the forehead, eyebrows, and upper eyelids), which has not been fully emphasized in previous studies. Targeted measurements confirmed a significantly smaller inter-eyebrow distance, increased frontal protrusion (p < 0.001) and frontal sinus area (p < 0.001) in OSA patients. Conclusion: Explainable DL expanded OSA-related craniofacial phenotypes. Future studies are needed to validate these upper-face traits in larger cohorts and clarify their relevance to OSA subtyping and mechanisms.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common sleep disorder, but cognitive impairment occurs only in a subset of patients, indicating individual susceptibility. This study aimed to identify oral fluid-derived protein biomarkers potentially related to neurobehavioral vulnerability in OSAHS by integrating clinical and animal model data. Nineteen participants (13 OSAHS, 6 controls) provided gingival crevicular fluid; age, body mass index (BMI), and weight showed no significant differences, while OSAHS patients exhibited significantly higher Epworth Sleepiness Scale (ESS) scores, reflecting increased daytime sleepiness. Fourteen C57BL/6J mice were randomly assigned to chronic intermittent hypoxia or normoxia conditions for 12 weeks; behavioral performance was evaluated using open field and Y-maze tests, and oral fluid was collected for proteomic analysis. 4D-DIA profiling identified 225 human and 105 mouse differentially expressed proteins; enrichment analysis highlighted humoral immunity and complement pathways. Notably, FN1 and JCHAIN were consistently upregulated across species, with expression changes accompanied by behavioral alterations in CIH mice. This clinic-driven, cross-species experimental study revealed FN1 and JCHAIN as shared, upregulated proteins potentially linked to hypoxia-associated neurobehavioral vulnerability in OSAHS. Rather than broadly focusing on differential expression, the study highlights these two proteins as candidates for further mechanistic investigation and future biomarker validation in cognitively vulnerable OSAHS patients.

Clinical evidence supports a link between obstructive sleep apnea (OSA) and periodontitis. However, whether OSA exerts a causal effect on periodontitis and the underlying protein biomarkers remains unclear. This study aimed to investigate their causal relationship and to identify salivary protein markers involved in the shared pathogenesis. First, a bidirectional two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) statistics was conducted to evaluate the causal relationship. Second, data-independent acquisition (DIA)-based salivary proteomics was applied to identify differentially expressed proteins in periodontally healthy patients with OSA (n = 30) and controls (n = 10). The OSA-associated upregulated proteins were screened for consistency with OSA severity and overlap with an independent periodontitis cohort. Network and hub protein analyses were performed. Genetic analysis in the MR suggested that OSA could statistically contribute to periodontitis development, but not in the reverse direction. Salivary proteomics revealed severity-dependent expression changes in OSA, with upregulated proteins enriched in the antigen processing and presentation pathway. Eight proteins detected in OSA patients showed increased expression with periodontal destruction. NAMPT, GSN, and S100A8/A9 emerged as hub proteins, validated by ELISA in independent OSA-associated periodontitis patients. The integrative genetic and proteomic investigations support a potential causal and proteome association between OSA and periodontitis, highlighting NAMPT, GSN, and S100A8/A9 as candidate mediators that may underpin the inflammatory interplay.

To evaluate objective and subjective adherence to oral appliance (OA) therapy in obstructive sleep apnea (OSA) and identify predictors of adherence. Forty-nine adults with OSA (27 males, 22 females) underwent baseline polysomnography, sleep questionnaires, craniofacial measurements, and personality assessment. Objective adherence was monitored for 3 months using an embedded thermal microsensor. Subjective adherence was self-reported at 3 and 12 months. Predictors of adequate adherence (mean nightly use ≥ 4 h) were identified using multivariate logistic regression under a modified intention-to-treat approach. OA therapy reduced AHI from 16.6 ± 10.3 to 4.16 ± 3.05 events/h. Forty-three participants (87.8

OBJECTIVES:Myofunctional therapy (MT) has emerged as an adjunct treatment for obstructive sleep apnea (OSA). This systematic review and network meta-analysis of randomized controlled trials (RCTs) aims to evaluate the efficacy of MT in treating adult and pediatric OSA. METHODS:Four electronic databases were searched until April 30, 2024. Meta-analysis, subgroup, and network meta-analysis using multivariate random effects were performed to estimate pooled differences, focusing on objective and subjective indicators. RESULTS:A total of 15 RCTs involving 473 adults and 139 children were eligible, with 10 adult studies (n = 380) included in the network meta-analysis. Compared to the controls, MT yielded an improved decrease in Epworth sleepiness scale (ESS) of -3.54 (95%CI -5.96 to -1.13, P = .004) and Pittsburgh sleep quality index (PSQI) of -2.24 (95%CI -3.46 to -1.01, P = .0003), though no statistically significant change in apnea-hypopnea index (AHI) (-8.73/h, 95%CI -21.19 to 3.74/h, P = 0.17). Improvements in arousal index and snoring intensity were also noted in adults. Combining MT with continuous positive airway pressure (CPAP) could lead to a pronounced reduction in AHI but did not significantly increase CPAP efficacy. Limited evidence suggests MT may benefit AHI and average SpO2 in pediatric OSA, with high compliance being essential. CONCLUSIONS:The network meta-analysis supports MT as a promising adjunct for improving subjective indicators in adults and suggests that when daily training exceeds 30 minutes, MT can significantly improve AHI. Additionally, MTSP and MT combined with myofascial release may offer further benefits in subjective outcomes.

Objectives:The present study aimed to characterize the salivary microbiota in patients with catathrenia and to longitudinally validate potential biomarkers after treatment with mandibular advancement devices (MAD). Materials and methods:Twenty-two patients with catathrenia (12 M/10 F, median age 28 y) and 22 age-matched control volunteers (8 M/14 F, median age 30 y) were included in the cross-sectional study. Video/audio polysomnography was conducted for diagnosis. All patients received treatment with custom-fit MAD and were followed for one month. Ten patients (6 M/4 F) underwent post-treatment PSG. Salivary samples were collected, and microbial characteristics were analyzed using 16S rRNA gene sequencing. The 10-fold cross-validated XGBoost and nested Random Forest Classifier machine learning algorithms were utilized to identify potential biomarkers. Results:In the cross-sectional study, patients with catathrenia had lower α-diversity represented by Chao 1, Faith's phylogenetic diversity (pd), and observed species. Beta-diversity based on the Bray-Curtis dissimilarities revealed a significant inter-group separation (p = 0.001). The inter-group microbiota distribution was significantly different on the phylum and family levels. The treatment of MAD did not alter salivary microbiota distribution significantly. Among the most important genera in catathrenia and control classification identified by machine learning algorithms, four genera, Alloprevotella, Peptostreptococcaceae_XI_G1, Actinomyces and Rothia, changed significantly with MAD treatment. Correlation analysis revealed that Alloprevotella was negatively related to the severity of catathrenia (r2= -0.63, p < 0.001). Conclusions:High-throughput sequencing revealed that the salivary microbiota composition was significantly altered in patients with catathrenia. Some characteristic genera (Alloprevotella, Peptostreptococcaceae_XI_G1, Actinomyces, and Rothia) could be potential biomarkers sensitive to treatment. Future studies are needed to confirm and determine the mechanisms underlying these findings.

Catathrenia is a rare sleep-related breathing disorder characterized by recurrent episodes of groaning during sleep, which leads to sleep fragmentation and daytime symptoms. Catathrenia could be misclassified as sleep apnea. Integrating omics data with machine learning techniques holds potential for diagnosis and advancing the understanding of its etiology. All participants included in the study underwent full-night polysomnography for evaluation. This study employed in-depth 4D-DIA proteomics to analyze salivary protein profiles in 22 catathrenia patients and 22 matched nonsnoring controls, revealing significant differences in protein characterization. Functional analysis of differentially expressed proteins indicated a predominant association with the extracellular matrix (ECM)-receptor interaction pathway. A machine learning pipeline was used for protein marker selection, and the findings were validated in a separate cohort of five catathrenia patients before and after mandibular advancement device (MAD) treatment. The study identified five potential protein markers with the highest area under the curve (AUC): HATL5 (airway trypsin-like protease 5), B2RBF5 (highly similar to Homo sapiens chitobiase, di-N-acetyl-CTBS), A0A7S5BZF8 (IGH Fragment), PRR27 (proline-rich protein 27), and BCAT2 (branched-chain-amino-acid aminotransferase). The relative abundance of these proteins was found to correlate with the Epworth sleepiness scale (ESS) and polysomnographic parameters related to groaning events. These proteins could be used as potential markers with validation in future studies. Catathrenia may be linked to ECM-related airway rigidity during sleep. Further investigation is required to elucidate the underlying mechanisms.

The microbiota is associated with obstructive sleep apnea (OSA) and hyperuricemia (HUA), but the relationship between oral microbiota and OSA-related HUA remains unclear. Our study investigated salivary microbiota differences between individuals with OSA and those with both OSA and HUA, and explored the link between oral microbiome alterations and uric acid fluctuations in OSA patients. Seventy-two adults were divided into four groups: controls (n = 20, 33.75 ± 9.46 years), OSA (n = 23, 44.08 ± 13.70 years), OSA with comorbid HUA (OSA+HUA, n = 22, 40.18 ± 9.58 years), and OSA with medication-controlled HUA (n = 7, 44.56 ± 15.14 years). Salivary microbiota and proteomic profiles were analyzed using 16S rRNA sequencing and Astral DIA. OSA and OSA+HUA showed reduced alpha-diversity compared to controls. The OSA+HUA group had increased Oribacterium abundance relative to the OSA group, which decreased after uric acid treatment, whereas Rothia, Capnocytophaga, and Aggregatibacter showed the opposite trend. 104 differentiated proteins were identified between the OSA and OSA+HUA groups. Oribacterium was positively correlated with several antioxidant proteins, while the other three genera were negatively correlated. This study identifies non-invasive biomarkers in the OSA+HUA group, as the first of its kind, highlighting the role of oral microbiota in future research and therapies.

OBJECTIVES:This study aimed to summarize the clinical features of non-syndromic late developing supernumerary teeth (LDST) and comparisons with common supernumerary teeth (ST) and explore the association between LDST and the third dentition.MATERIALS AND METHODS:This study retrospected cone-beam computed tomography (CBCT) and medical history of 41,903 consecutive patients from January to December 2021. Comparisons between ST and LDST were evaluated by Chi-square test or Fisher exact test. Correlation between chronological age and dental stage age was evaluated by Spearman's rank correlation coefficient. Binary logistic regression analysis was used to explore the features of LDST originating from the third dentition.RESULTS:Sixty patients with 126 non-syndromic LDST and 1602 patients with 1988 non-syndromic ST were identified. The prevalence of ST and LDST was 3.82% and 0.14%, respectively, with a male-female ratio of 1.78:1 and 1.31:1. LDST patients mainly had LDST in multiple (58.33%) and bilaterally (41.67%), with an average of 2.1/patient. Most LDST were normal-shaped (84.13%), vertically oriented (71.43%), located in the mandible (80.16%), and distributed in the premolar region (82.54%). The study also indicated that the development of LDST was correlated with permanent teeth, with LDST developing 6.48 to 10.45 years later. In this study, 72.22% of LDST met the clinical criteria for the third dentition.CONCLUSIONS:LDST manifested different clinical features from common ST. LDST might be closely related to the third dentition.CLINICAL RELEVANCE:This work would help to comprehend LDST from a clinical perspective, and may be complementary to the criteria of the third dentition.

Nocturnal groaning syndrome is a common sleep disorder characterized by irregular groaning or vocalizations during nighttime sleep, representing a significant area of research in sleep disorders. Nocturnal groaning syndrome is a common sleep disorder characterized by irregular groaning or vocalizations during nighttime sleep, representing a significant area of research in sleep disorders. proposes a multimodal recognition approach based on speech, image, and text modalities. The study analyzes audio features using Mel Frequency Cepstral Coefficients (MFCC), which is the most common method for identifying nocturnal groaning syndrome. Coefficients (MFCC), extracts image features with pretrained MobileNetV2, and identifies key physiological signals from text using TF-IDF algorithm. Subsequently, Multimodal Compact Bilinear Pooling (MCB) is employed to fuse audio and image features, and a Text-Image CNN is used to combine image and text features. Support Vector Machine (SVM) is then used to classify the fused multimodal features, and decision-level fusion is performed using weighting criteria. Experimental results demonstrate an identification accuracy of 89.5% on the test set, significantly enhancing the auxiliary diagnostic effectiveness of nocturnurnal diagnosis. Experimental results demonstrate an identification accuracy of 89.5% on the test set, significantly enhancing the auxiliary diagnostic effectiveness of nocturnal groaning syndrome.

This study aims to review the long-term subjective and objective efficacy of mandibular advancement devices (MAD) in the treatment of adult obstructive sleep apnea (OSA). Electronic databases such as PubMed, Embase, and Cochrane Library were searched. Randomized controlled trials (RCTs) and non-randomized self-controlled trials with a treatment duration of at least 1 year with MAD were included. The quality assessment and data extraction of the included studies were conducted in the meta-analysis. A total of 22 studies were included in this study, of which 20 (546 patients) were included in the meta-analysis. All the studies had some shortcomings, such as small sample sizes, unbalanced sex, and high dropout rates. The results suggested that long-term treatment of MAD can significantly reduce the Epworth sleepiness scale (ESS) by -3.99 (95%CI -5.93 to -2.04, p<0.0001, I2 = 84%), and the apnea-hypopnea index (AHI) -16.77 (95%CI -20.80 to -12.74) events/h (p<0.00001, I2 = 97%). The efficacy remained statistically different in the severity (AHI<30 or >30 events/h) and treatment duration (duration <5y or >5y) subgroups. Long-term use of MAD could also significantly decrease blood pressure and improve the score of functional outcomes of sleep questionnaire (FOSQ). Moderate evidence suggested that the subjective and objective effect of MAD on adult OSA has long-term stability. Limited evidence suggests long-term use of MAD might improve comorbidities and healthcare. In clinical practice, regular follow-up is recommended.

Orthodontic tooth movement (OTM) relies on the remodeling of periodontal tissues, including the periodontal ligament (PDL) and alveolar bone. Collagen remodeling plays a crucial role during this process, allowing for the necessary changes in the PDL’s structure and function. Endo180, an urokinase plasminogen activator receptor-associated protein, is a transmembrane receptor regulated collagen remodeling. This study aims to investigate whether and how Endo180 participates in collagen remodeling within the PDL during OTM. A mechanical force-induced OTM rat model was established using a closed coiled spring to mesially move the right maxillary first molar. The distance of OTM was examined by micro-computed tomography (micro-CT). The collagen remodeling within the PDL was assessed using atomic force microscope (AFM), Hematoxylin-Eosin (HE) staining and Masson staining. Protein expressions of Endo180, collagen I (COL I) and collagen III (COL III) were analyzed via immunofluorescence staining. Additionally, the mRNA expressions of Endo180, COL I, and COL III in force-induced PDL cells were examined by RT-qPCR in vitro. To further illustrate the role of Endo180 in regulating COL I and COL III expressions, Endo180 siRNA (siEndo) was applied to force-stimulated PDL cells. Force application increased OTM distance and disrupted collagen fiber organization, with a greater decrease in collagen elastic modulus on the mesial side than on the distal side of the PDL. After 7 days of force application, Endo180 and COL III expressions significantly increased in PDL tissues, while COL I expression decreased in PDL tissues. Compressive force loading in vitro upregulated the mRNA expressions of Endo180 and COL III, but downregulated COL I mRNA expression. Notably, Endo180 knockdown using siRNA suppressed force-induced COL III expression while restoring the downregulated COL I expression under compressive force stimuli. Force-induced Endo180 expression modulates collagen remodeling in PDL during OTM by upregulating COL III and downregulating COL I. This collagen reorganization facilitates efficient tooth movement, highlighting Endo180 as a potential therapeutic target to optimize orthodontic treatment outcomes.

Objectives To explore the relationship between sleep duration and cognitive functions in older adults using NHANES, a national US population study dataset, and to explore the causal association with Mendelian randomization (MR) using the UK Biobank. Methods First, an observational study was conducted with the NHANES database with participants ≥60 years. Sleep duration was measured with accelerometers for 7 consecutive days. Participants were divided into habitual short sleep (<7 h) and long sleep (>9 h) groups. Cognitive functions were measured with the CERAD Word Learning sub-set, Animal Fluency, and Digit Symbol Substitution test (DSST). Multivariate regression models were used to explore relationships between sleep duration and cognitive functions. Second, bidirectional MR was conducted with data for self-reported sleep duration, which came from a genome-wide association study (GWAS) comprising 446,118 adults from the UK Biobank, and general cognitive performance, which was obtained from a recent GWAS study ( N = 257,841). Inverse-variance weighted (IVW) was used as the primary estimation of the outcome. Results In the observational study, 2687 participants were included. Sleep duration was associated with cognitive functions in a non-linear way. Habitual long sleep (>9°h) was associated with lower scores on DSST (OR = 0.01, p = .003) in the fully-adjusted model. The association between habitual short sleep and cognitive functions was insignificant. For the MR, genetically predicted lower general cognitive performance was causally associated with a higher prevalence of habitual short sleep (OR = 0.97, p = 5.1 × 10 −7 ) and long sleep (OR = 0.97, p = 8.87 × 10 −16 ). Discussion Short and long sleep duration might be both causally associated with worse outcomes of cognitive functions in older adults, highlighting the importance of maintaining sleep health.

ABSTRACT Children diagnosed with severe tonsillar hypertrophy display discernible craniofacial features distinct from those with adenoid hypertrophy, prompting illuminating considerations regarding microbiota regulation in this non-inflammatory condition. The present study aimed to characterize the salivary microbial profile in children with tonsillar hypertrophy and explore the potential functionality therein. A total of 112 children, with a mean age of 7.79 ± 2.41 years, were enrolled and divided into the tonsillar hypertrophy (TH) group ( n = 46, 8.4 ± 2.5 years old), adenoid hypertrophy (AH) group ( n = 21, 7.6 ± 2.8 years old), adenotonsillar hypertrophy (ATH) group ( n = 23, 7.2 ± 2.1 years old), and control group ( n = 22, 8.6 ± 2.1 years old). Unstimulated saliva samples were collected, and microbial profiles were analyzed by 16S rRNA sequencing of V3–V4 regions. Diversity and composition of salivary microbiome and the correlation with parameters of overnight polysomnography and complete blood count were investigated. As a result, children with tonsillar hypertrophy had significantly higher α-diversity indices ( P ＜0.05). β-diversity based on Bray–Curtis distance revealed that the salivary microbiome of the tonsillar hypertrophy group had a slight separation from the other three groups ( P ＜0.05). The linear discriminant analysis effect size (LEfSe) analysis indicated that Gemella was most closely related to tonsillar hypertrophy, and higher abundance of Gemella , Parvimonas , Dialister , and Lactobacillus may reflect an active state of immune regulation. Meanwhile, children with different degrees of tonsillar hypertrophy shared similar salivary microbiome diversity. This study demonstrated that the salivary microbiome in pediatric tonsillar hypertrophy patients had different signatures, highlighting that the site of upper airway obstruction primarily influences the salivary microbiome rather than hypertrophy severity. IMPORTANCE Tonsillar hypertrophy is the most frequent cause of upper airway obstruction and one of the primary risk factors for pediatric obstructive sleep apnea (OSA). Studies have discovered that children with isolated tonsillar hypertrophy exhibit different craniofacial morphology features compared with those with isolated adenoid hypertrophy or adenotonsillar hypertrophy. Furthermore, characteristic salivary microbiota from children with OSA compared with healthy children has been identified in our previous research. However, few studies provided insight into the relationship between the different sites of upper airway obstruction resulting from the enlargement of pharyngeal lymphoid tissue at different sites and the alterations in the microbiome. Here, to investigate the differences in the salivary microbiome of children with tonsillar hypertrophy and/or adenoid hypertrophy, we conducted a cross-sectional study and depicted the unique microbiome profile of pediatric tonsillar hypertrophy, which was mainly characterized by a significantly higher abundance of genera belonging to phyla Firmicutes and certain bacteria involving in the immune response in tonsillar hypertrophy, offering novel perspectives for future related research.

The purpose of this study is to improve the performance of existing OSA screening tools for pregnant women with machine learning algorithms. A total of 296 pregnant women who complained of snoring OSA were recruited to complete four traditional OSA screening questionnaires: Berlin, STOP, STOP-Bang questionnaires, and Epworth Sleepiness Scale. OSA status was confirmed using an overnight type III home sleep test. 76 of the participants repeated the procedure at different trimesters, generating a total of 402 records. The participants were randomly split into a training set (n = 207) and a test set (n = 89) in a 7:3 ratio. We applied a logistic regression model to build Mixture of Models for OSA screen (MoMOSA) based on demographic data and selected questions from all the questionnaires. Finally, we transformed the MoMOSA into a new questionnaire with a nomogram. MoMOSA, with 13 features, achieved the highest performance among the traditional questionnaires and built models.

BACKGROUND:Painful temporomandibular disorder (TMD) is the common cause of chronic oro-facial pain, which may interfere with sleep. Previous studies have documented an association between sleep and TMD.OBJECTIVES:This study aimed to further explore the association of night-time sleep and daytime napping with painful TMD.METHODS:A total of 419 patients (aged 31.88 ± 11.54 years with women forming 85.4%) from a TMD/Orofacial Pain center were enrolled. Patients' sleep conditions were evaluated with the Pittsburgh Sleep Quality Index (PSQI) questionnaire, and information on night-time sleep duration, napping duration and napping frequency was interviewed. TMD was diagnosed according to the Diagnostic Criteria for TMD protocol and stratified into myalgia (muscle pain), arthralgia (joint pain) and combined (muscle and joint pain) subgroups. The severity of TMD was measured with the Fonseca Anamnestic Index (FAI) questionnaire. Restricted cubic spline (RCS) regression models were established to explore relationships between sleep and painful TMD subgroups.RESULTS:Patients with poor sleep quality (PSQI≥6) had higher FAI scores (median 60, p < .001) and higher proportions of painful TMDs. The myalgia subgroup had higher PSQI scores (median 8, p < .001) than the arthralgia subgroup. The RCS models indicated a non-linear relationship between night-time sleep duration and myalgia (p < .001), which was not observed in arthralgia. However, there were no significant findings concerning napping and painful TMD subgroups.CONCLUSION:This study found that the association between sleep and TMD is mainly related to painful TMD conditions, which are associated with night-time sleep duration.

Background/purposeOrofacial pain is common in dental practices. This study aimed to explore relationships between orofacial pain and sleep using the UK Biobank dataset and, based on epidemiological associations, to investigate the causal association using genome-wide association studies data.Materials and methodsFirst, a cross-sectional study was conducted with 196,490 participants from UK Biobank. Information on pain conditions and sleep traits was collected. Multivariable models were used to explore the relationships with odds ratio (OR). Second, Mendelian randomization analyses were conducted using data for orofacial pain, including temporomandibular joint disorders-related pain (n = 377,277) and atypical facial pain (n = 331,749), and sleep traits, including sleep duration (n = 446,118), short sleep (n = 411,934), long sleep (n = 339,926), snoring (n = 359,916), ease of getting up (n = 385,949), insomnia (n = 453,379), daytime dozing (n = 452,071), daytime napping (n = 452,633), and chronotype (n = 403,195).ResultsThe cross-sectional study confirmed the bidirectionality between pain and sleep. Participants experiencing pain all over the body showed a significant association with an unhealthy sleep pattern (OR = 1.18, P < 0.001) and other sleep traits (P < 0.05). Risks of chronic orofacial pain were associated with sleep duration in a non-linear relationship (P = 0.032). The Mendelian randomization analyses indicated that long sleep was causally associated with temporomandibular joint disorders-related pain (OR = 6.77, P = 0.006).ConclusionThe relationship between pain and sleep is bidirectional. Long sleep is found to be causally associated with chronic orofacial pain.

Abstract Introduction Catathrenia is a rare sleep-related breathing disorder characterized by recurrent monotonous groaning during sleep. The acoustic characteristics of groaning and snoring sounds have been investigated. This study aims to propose a deep convolutional neural network (CNN) for automatic binary classification. Methods This study consisted of 3728 episodes of groaning sounds and 4577 episodes of snoring sounds obtained from synchronized audio of full-night polysomnography. Four features extracted from log-scaled mel-spectrograms were used as input. The background gaussian noise and the time-shifting were used to augment the dataset. The dataset was randomly split into training (70%), validation (15%), and testing datasets (15%). A deep learning convolutional neural network architecture was trained and evaluated. Results The proposed CNN model achieves an accuracy of 95.0% on the binary classification of groaning and snoring sounds. The model attains a sensitivity/recall of 96.4%, a specificity of 93.4%, and an F1 score of 94.84%. Conclusion The proposed CNN architecture has performed well in the automatic binary classification of groaning and snoring sounds, which could reduce difficulties in acoustic analyses of groaning episode detection. The model needs to be verified with more audio data before it can be put into clinical use better. Support (if any)

Purpose:Catathrenia is a rare sleeping disorder characterized by repetitive nocturnal groaning during prolonged expirations. Patients with catathrenia had heterogeneous polysomnographic, comorbidity, craniofacial characteristics, and responses to treatment. Identifying phenotypes of catathrenia might benefit the exploration of etiology and personalized therapy. Patients and Methods:Sixty-six patients diagnosed with catathrenia by full-night audio/video polysomnography seeking treatment with mandibular advancement devices (MAD) or continuous positive airway pressure (CPAP) were included in the cohort. Polysomnographic characteristics including sleep architecture, respiratory, groaning, and arousal events were analyzed. Three-dimensional (3D) and 2D craniofacial hard tissue and upper airway structures were evaluated with cone-beam computed tomography and lateral cephalometry. Phenotypes of catathrenia were identified by K-mean cluster analysis, and inter-group comparisons were assessed. Results:Two distinct clusters of catathrenia were identified: cluster 1 (n=17) was characterized to have more males (71%), a longer average duration of groaning events (18.5±4.8 and 12.8±5.7s, p=0.005), and broader upper airway (volume 41,386±10,543 and 26,661±6700 mm3, p<0.001); cluster 2 (n=49) was characterized to have more females (73%), higher respiratory disturbance index (RDI) (median 1.0 [0.3, 2.0] and 5.2 [1.2, 13.3]/h, p=0.009), more respiratory effort-related arousals (RERA)(1 [1, 109] and 32 [13, 57)], p=0.005), smaller upper airway (cross-sectional area of velopharynx 512±87 and 339±84 mm2, p<0.001) and better response to treatment (41.2% and 82.6%, p=0.004). Conclusion:Two distinct phenotypes were identified in patients with catathrenia, primary catathrenia, and catathrenia associated with upper airway obstruction, suggesting respiratory events and upper airway structures might be related to the etiology of catathrenia, with implications for its treatment.

Background: Studies have linked gut microbiota dysbiosis with sleep apnea; however, no causal relationship was found in human subjects. Finding new targets for the pathophysiology of sleep apnea might be made possible by systematically investigating the causal relationship between the human gut microbiota and sleep apnea. Methods: A two-sample Mendelian randomization analysis was conducted. The human gut microbiome composition data, spanning five taxonomic levels, were acquired from a genome-wide association study that included 18,340 participants from 24 cohorts. Genome-wide association study data for sleep apnea were obtained from the Sleep Disorder Knowledge Portal for primary analysis and the FinnGen consortium for meta-analysis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Results: Using inverse-variance weighted analysis, eight microbial taxa were initially found to be substantially linked with the apnea-hypopnea index. Only three microbial taxa remained significant associations with sleep apnea when combined with the FinnGen consortium (the class Bacilli: B = 8.21%, 95% CI = 0.93%–15.49%; p = 0.03; the order Lactobacillales: B = 7.55%, 95% CI = 0.25%–4.85%; p = 0.04; the genus RuminococcaceaeUCG009: B = −21.63%, 95% CI = −41.47% to −1.80%; p = 0.03). Conclusions: Sleep apnea may lead to gut dysbiosis as significant reductions in butyrate-producing bacteria and increases in lactate-producing bacteria. By integrating genomes and metabolism, the evidence that three microbiome species are causally linked to sleep apnea may offer a fresh perspective on the underlying mechanisms of the condition.