Association between overnight repetitive respiratory events and the accumulation of genioglossus fatigue in male patients with severe obstructive sleep apnea.

PURPOSE: To evaluate the correlation between median frequency (MF) as a measure of genioglossus (GG) fatigue and overnight repetitive respiratory events in male patients with severe obstructive sleep apnea (OSA). METHODS: GG electromyography (EMG) data were collected synchronously with polysomnography (PSG). Overnight respiratory events were divided based on whether they occurred during the first or second halves of the total number of overnight respiratory events, and differences in MF in the respiratory phase were compared in the same segments. Events were then sampled in pairs to compare MF. The correlation between MF and the order of respiratory events, as well as interindividual differences, were analyzed. RESULTS: Twenty-two male patients were enrolled in this study and 2210 respiratory events were recorded. Before and during respiratory events, MF decreased significantly in the second half, especially during the inspiratory phase (segments 1-4: P = 0.014, P < 0.001, P < 0.001, P < 0.001, respectively). This trend was observed in non-rapid eye movement sleep and lateral position, but not in rapid eye movement sleep or the supine position, and remained after pairing for duration, stage, and position. MF correlated negatively with the order of respiratory events during the inspiratory phase. The trend of decrease in MF only existed in patients with apnea-hypopnea index > 30 events/h. CONCLUSION: Overnight repetitive respiratory events were associated with increased GG fatigue, influenced by sleep stage and body position in male patients with severe OSA. GG fatigue depends on the order and frequency of respiratory events.

Evaluation of obstructive sleep apnea: an analysis based on aberrant genes.

BACKGROUND: Obstructive sleep apnea (OSA) is an upstream disorder that frequently causes multisystem disorders. Much research has revealed the pathogenesis of OSA, but there is still a lack of research on the complications caused by OSA. METHODS: The mRNA expression and methylation dataset based on peripheral blood mononuclear cells (PBMCs) were downloaded from the Gene Expression Omnibus (GEO) database. All differential expressed genes (DEGs) were ranked using the Robust Rank Aggregation (RRA) algorithm. A weighted gene co-expression network analysis (WGCNA) was constructed. Subsequently, we used immune infiltration, enrichment analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis for apnea and hypopnea index (AHI) and hypertension and excessive daytime sleepiness (EDS) and constructed diagnostic model using random forest algorithm. RESULTS: In the present study, we identified 318 DEGs in PBMCs involved in pathogenesis or continuous positive airway pressure (CPAP) therapy. Pathway enrichment identified DEGs associated with protein regulation and metabolism. Notably, through intra group analysis, we found that the immune disorder was more significant for OSA in males, non-daytime sleepy, or non-hypertensive OSA. The area under the ROC curve of model for EDS prediction is 0.889 and 0.852 for hypertension. Notably, we found that the diagnostic model had a high linear predictive value for AHI. CONCLUSIONS: Our results indicate that PBMCs are a significant component of alterations in OSA and are expected to explain the mechanism of multisystem diseases caused by OSA. The present study provides new insights for symptom evaluation, classification and treatment of OSA from the molecular level.

Influence of postural changes on nasal resistance in patients with obstructive sleep apnea.

OBJECTIVE: Increased nasal resistance (NR) can augment upper airway collapse in patients with obstructive sleep apnea (OSA). Posture change can lead to altered nasal resistance. Our study aimed to investigate the influence of posture changes on NR in patients with OSA. METHODS: Healthy controls without subjective nasal obstruction (apnea-hypopnea index (AHI) < 5 events/h), patients with OSA and subjective nasal obstruction, and patients with OSA and no subjective nasal obstruction were recruited. NR was measured by active anterior rhinomanometry in sitting, supine, left-lateral, and right-lateral postural positions. Total NR and postural change-related NR increments were calculated and compared among groups. RESULTS: In total, 26 healthy controls and 72 patients with OSA (mean AHI 39.7 ± 24.8 events/h) were recruited. Of patients with OSA, 38/72 (53%) had subjective nasal obstruction. Compared with controls, patients with OSA and no subjective nasal obstruction had lower total NR (inspiration, p = 0.037; expiration, p = 0.020) in the supine postural position. There was no difference in sitting, left-lateral, and right-lateral total NR among groups. Total NR was higher in lateral compared to sitting posture in both patients with OSA and in controls. The NR increment for sitting to supine postural change was significantly lower in patients with OSA (inspiration, p = 0.003; expiration, p = 0.005) compared with controls. The change in NR showed no statistically significant difference among groups in supine-left or supine-right postural change. CONCLUSION: Patients with OSA had lower supine total NR and lower total NR increment in the sitting to supine postural change, which may be related to a different posture-related NR regulatory mechanism. This study provides a new exploratory direction for the compensatory mechanism of the upper airway to collapse during sleep.

Facial Contour Features Measured on CT Reflects Upper Airway Morphology in Patients With OSA.

OBJECTIVE: To determine facial contour features, measured on computed tomography (CT), related to upper airway morphology in patients with obstructive sleep apnea (OSA); certain phenotype of facial abnormalities implying restriction of craniofacial skeleton and adipose tissue nimiety has predicted the value of the severity of OSA. MATERIALS AND METHOD: Sixty-four male patients with OSA [apnea-hypopnea index (AHI) ≥10/h] who had upper airway CT were randomly selected to quantitatively measure indicators of facial contour and upper airway structures. Pearson correlation analyses were performed. Partial correlation procedure was used to examine correlations while controlling body mass index (BMI). RESULTS: Upper airway anatomy can nearly all be reflected in the face, except retroglossal airway. Upper face width can be measured to assess the overall skeletal structures of the airway. Lower face width can be used to represent how much adipose tissue deposited. Hard palate, retropalatal, and hypopharyngeal airways have corresponding face indicators respectively. Midface width is a better predictor of AHI severity and minimum blood oxygen even than neck circumference because it contains the most anatomical information about the airway, including RP airway condition, soft palate length, tongue volume, etc. These correlations persisted even after correction for BMI. CONCLUSIONS: All anatomical features of the upper airway except retroglossal airway can be reflected in the face, and midface width is the best predictor of AHI severity and minimum blood oxygen, even better than neck circumference and BMI.

Concurrent Hearing and Genetic Screening of 180,469 Neonates with Follow-up in Beijing, China.

Concurrent hearing and genetic screening of newborns is expected to play important roles not only in early detection and diagnosis of congenital deafness, which triggers intervention, but also in predicting late-onset and progressive hearing loss and identifying individuals who are at risk of drug-induced HL. Concurrent hearing and genetic screening in the whole newborn population in Beijing was launched in January 2012. This study included 180,469 infants born in Beijing between April 2013 and March 2014, with last follow-up on February 24, 2018. Hearing screening was performed using transiently evoked otoacoustic emission (TEOAE) and automated auditory brainstem response (AABR). For genetic testing, dried blood spots were collected and nine variants in four genes, GJB2, SLC26A4, mtDNA 12S rRNA, and GJB3, were screened using a DNA microarray platform. Of the 180,469 infants, 1,915 (1.061%) were referred bilaterally or unilaterally for hearing screening; 8,136 (4.508%) were positive for genetic screening (heterozygote, homozygote, or compound heterozygote and mtDNA homoplasmy or heteroplasmy), among whom 7,896 (4.375%) passed hearing screening. Forty (0.022%) infants carried two variants in GJB2 or SLC26A4 (homozygote or compound heterozygote) and 10 of those infants passed newborn hearing screening. In total, 409 (0.227%) infants carried the mtDNA 12S rRNA variant (m.1555A>G or m.1494C>T), and 405 of them passed newborn hearing screening. In this cohort study, 25% of infants with pathogenic combinations of GJB2 or SLC26A4 variants and 99% of infants with an m.1555A>G or m.1494C>T variant passed routine newborn hearing screening, indicating that concurrent screening provides a more comprehensive approach for management of congenital deafness and prevention of ototoxicity.

Childhood asthma and type 1 diabetes mellitus: A meta-analysis and bidirectional Mendelian randomization study.

BACKGROUND: Worldwide incidence and prevalence of both asthma and type 1 diabetes mellitus (T1DM) in children have been increasing in past decades. Association between the two diseases has been found in some but not in other studies. OBJECTIVE: We conducted a meta-analysis to verify such an association, and bidirectional Mendelian randomization analysis to examine the potential cause-effect relationships. METHODS: Three databases (PubMed, Embase, and Web of Science) were searched from their inception to February 1, 2021. Pooled hazard ratios (HR) or odds ratios (OR), and 95% confidence intervals, were calculated. Associations between single-nucleotide polymorphisms with childhood asthma and T1DM were selected based on genome-wide association studies. The outcome datasets were obtained from FinnGen study. We used the inverse-variance-weighted (IVW), weighted median and MR-Egger methods to estimate causal effects. To assess robustness and horizontal pleiotropy, MR-Egger regression and MR pleiotropy residual sum and outlier test were conducted. RESULTS: In meta-analysis, childhood asthma was associated with an increased risk of T1DM (HR = 1.30, 95% CI 1.05-1.61, P = .014), whereas T1DM was not associated with the risk of asthma (HR = 0.98, 95% CI 0.64-1.51, P = .941; OR = 0.84, 95% CI 0.65-1.08, P = .168). MR analysis indicated increased genetic risk of T1DM in children with asthma (OR = 1.308; 95% CI 1.030-1.661; P = .028). Analysis using the IVW method indicated no association between T1DM and genetic risk of asthma (OR = 1.027, 95%CI 0.970-1.089, P = .358). CONCLUSION: Both meta-analysis and MR study suggested that childhood asthma was a risk factor for T1DM. No epidemiological or genetic evidence was found for an association of T1DM with asthma incidence. Further studies could be carried out to leverage this newfound insight into better clinical and experimental research in asthma and T1DM.

Detecting obstructive sleep apnea by craniofacial image-based deep learning.

STUDY OBJECTIVES: This study aimed to develop a deep learning-based model to detect obstructive sleep apnea (OSA) using craniofacial photographs. METHODS: Participants referred for polysomnography (PSG) were recruited consecutively and randomly divided into the training, validation, and test groups for model development and evaluation. Craniofacial photographs were taken from five different angles (front, right 90° profile, left 90° profile, right 45° profile, and left 45° profile) and inputted to the convolutional neural networks. The neural networks extracted features from photographs and outputted the probabilities of the presence of the disease. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated using PSG diagnosis as the reference standard. These analyses were repeated using two apnea-hypopnea index thresholds (≥ 5 and ≥ 15events/h). RESULTS: A total of 393 participants were enrolled. Using the operating point with maximum sum of sensitivity and specificity, the model of the photographs exhibited an AUC of 0.916 (95% confidence interval [CI], 0.847-0.960) with a sensitivity of 0.95 and a specificity of 0.80 at an AHI threshold of 5 events/h; an AUC of 0.812 (95% CI, 0.729-0.878) with a sensitivity of 0.91 and a specificity of 0.73 at an AHI threshold of 15 events/h. CONCLUSIONS: The results suggest that combining craniofacial photographs and deep learning techniques can help detect OSA automatically. The model may have potential utility as a tool to assess OSA probability in clinics or screen for OSA in the community.

Automatic classification of the obstruction site in obstructive sleep apnea based on snoring sounds.

OBJECTIVES: Snoring is a common symptom of obstructive sleep apnea (OSA) which is considered to be potential predictors of the obstruction site. Successful treatment of OSA depend on the determination the types of obstruction site. This study aimed to develop a machine learning-based model to detect obstruction site using snoring sound. METHODS: Patients with OSA underwent drug-induced sleep endoscopy (DISE) and the snoring sounds were recorded simultaneously. We extracted acoustic features based on Mel-frequency cepstral coefficients (MFCC). A k-nearest neighbors (KNN) was used for snore classification. RESULTS: Total 42 patients with OSA were enrolled. The accuracy of model was 85.55 %, F1 score was 85.04. With combined age, gender and Body Mass Index (BMI), the accuracy of model was 87.98 %, and F1 score was 87.96. The model exhibited accuracies of 83 %, 93 % and 92 %; an AUC of 85.88, 89.22 and 88.17 in detecting retropalatal, retrolingual and multilevel obstructions. CONCLUSION: Our results suggest that combing snoring sound with age, gender and BMI, the machine learning based model can help automatically assess obstruction site. The model may have potential utility as a clinical tool to help for clinical decision-making.

Evaluation of the clinical efficacy of nasal surgery in the treatment of obstructive sleep apnoea.

STUDY OBJECTIVES: The aim of the study was to evaluate the clinical efficacy of nasal surgery in the treatment of obstructive sleep apnoea (OSA) by comparing the improvement of subjective symptoms and objective metrics before surgery and after 6 months of surgery. METHODS: Patients with the main complaint of nasal congestion combined with habitual snoring who were hospitalized and treated were selected. Patients underwent subjective symptom tests and objective indicator monitoring both before surgery and 6 months after surgery. Comparisons between groups were performed using the independent samples t-test. RESULTS: Subjective scale evaluations demonstrated that nasal congestion, daytime sleepiness, snoring, nose-related symptoms, and sleep symptoms in patients with simple snoring or with OSA were improved after nasal surgery. Additionally, vitality was improved in all groups except for the patients with simple snoring and emotional consequence was improved in patients with simple snoring and mild OSA. Objective evaluations indicated the apnoea-hypopnoea index (AHI), the thickness of the soft palate, and the maximum cross-sectional area of the sagittal plane of the soft palate decreased after surgery in patients with mild OSA. The lowest blood oxygen concentration (LSaO2) and anteroposterior diameter of the soft palate increased after surgery in patients with mild OSA. The arousal index also significantly decreased in patients with mild and moderate OSA. The nasal cavity volumes (NCVs) and the nasal minimal cross-sectional areas (NMCAs) of all groups showed significant differences after surgery. CONCLUSIONS: Nasal surgery can effectively improve nose and sleep symptoms in patients with simple snoring or with OSA. It can significantly reduce the nasal resistance and increase the ventilation volume. STATEMENT OF SIGNIFICANCE: Obstructive sleep apnoea (OSA) is becoming a global health problem. OSA is associated with several coexisting conditions, reduced health-related quality of life, and impaired work productivity. This study performed nasal surgery on OSA patients with the main complaint of nasal congestion combined with snoring and patients with simple snoring to compare the improvement of subjective symptoms and objective metrics before and after surgery. We found that: (1) symptoms such as nasal congestion, daytime sleepiness or snoring were improved after nasal surgery; (2) the apnoea-hypopnoea index (AHI) and arousal index decreased after surgery in patients with OSA; (3) the nasal and oropharyngeal cavity volumes increased after surgery. These findings suggest that patients with OSA or with simple snoring could benefit from nasal surgery.

Aerodynamics Analysis of the Impact of Nasal Surgery on Patients with Obstructive Sleep Apnea and Nasal Obstruction.

OBJECTIVE: The purpose of this study was to evaluate the effects of nasal surgery on airflow characteristics in patients with obstructive sleep apnea (OSA) by comparing the alterations of airflow characteristics within the nasal and palatopharyngeal cavities. METHODS: Thirty patients with OSA and nasal obstruction who underwent nasal surgery were enrolled. A pre- and postoperative 3-dimensional model was constructed, and alterations of airflow characteristics were assessed using the method of computational fluid dynamics. The other subjective and objective clinical indices were also assessed. RESULTS: By comparison with the preoperative value, all postoperative subjective symptoms statistically improved (p < 0.05), while the Apnea-Hypopnea Index (AHI) changed little (p = 0.492); the postoperative airflow velocity and pressure in both nasal and palatopharyngeal cavities, nasal and palatopharyngeal pressure differences, and total upper airway resistance statistically decreased (all p < 0.01). A significant difference was derived for correlation between the alteration of simulation metrics with subjective improvements (p < 0.05), except with the AHI (p > 0.05). CONCLUSION: Nasal surgery can decrease the total resistance of the upper airway and increase the nasal airflow volume and subjective sleep quality in patients with OSA and nasal obstruction. The altered airflow characteristics might contribute to the postoperative reduction of pharyngeal collapse in a subset of OSA patients.

A model for obstructive sleep apnea detection using a multi-layer feed-forward neural network based on electrocardiogram, pulse oxygen saturation, and body mass index.

PURPOSE: To develop and evaluate a model for obstructive sleep apnea (OSA) detection using an artificial neural network (ANN) based on the combined features of body mass index (BMI), electrocardiogram (ECG), and pulse oxygen saturation (SpO2). METHODS: Polysomnography (PSG) data for 148 patients with OSA and 33 unaffected individuals were included. A multi-layer feed-forward neural network (FNN) was used based on the features obtained from ECG, SpO2, and BMI. The receiver operating characteristic (ROC) curve and the metrics of accuracy, sensitivity, and specificity were used to evaluate the performance of the overall classification. Some other machine learning methods including linear discriminant, linear Support Vector Machine (SVM), Complex Tree, RUSBoosted Trees, and Logistic Regression were also used to compare their performance with the FNN. RESULTS: The accuracy, sensitivity, and specificity of the proposed multi-layer FNN were 97.8%, 98.6%, and 93.9%, respectively, and the area under the ROC curve was 97.0%. Compared with the other machine learning methods mentioned above, the FNN achieved the highest performance. CONCLUSIONS: The satisfactory performance of the proposed FNN model for OSA detection indicated that it is reliable to screen potential patients with OSA using the combined channels of ECG and SpO2 and also taking into account BMI. This strategy might be a viable alternative method for OSA diagnosis.

Severity evaluation of obstructive sleep apnea based on speech features.

PURPOSE: There are upper airway abnormalities in patients with obstructive sleep apnea (OSA), and their speech signal characteristics are different from those of unaffected people. In this study, the severity of OSA was evaluated automatically by machine learning technology based on the speech signals of Chinese people. METHODS: In total, 151 adult male Mandarin native speakers who had suspected OSA completed polysomnography to assess the severity of the disease. Chinese vowels and nasal sounds were recorded in sitting and supine positions, and the accuracy of predicting the apnea-hypopnea index (AHI) of the participants using a machine learning method was analyzed based on features extracted from the speech signals. RESULTS: Among the 151 participants, 75 had AHI > 30 events/h, and 76 had AHI ≤ 30 events/h. Various features including linear prediction cepstral coefficients (LPCC) were extracted from the data collected from participants recorded in the sitting and supine positions and by using a linear support vector machine (SVM); we classified the participants with thresholds of AHI = 30 and AHI = 10 events/h. The accuracies of the classifications were both 78.8%, the sensitivities were 77.3% and 79.1%, and the specificities were 80.3% and 78.0%, respectively. CONCLUSION: This study constructed a severity evaluation model of OSA based on speech signal processing and machine learning, which can be used as an effective method to screen patients with OSA. In addition, it was found that Chinese pronunciation can be used as an effective feature to predict OSA.

The values of (1,3)-ß-D-glucan and galactomannan in cases of invasive fungal rhinosinusitis.

OBJECTIVE: The purpose of the present study was to investigate the values of the serum (1,3)-ß-D-glucan test (G test) alone, the galactomannan test (GM test) alone, and their combination in the diagnosis of invasive fungal rhinosinusitis (IFRS). METHODS: The present study retrospectively analysed the clinical data of 98 patients who were preliminarily diagnosed with "space-occupying lesions in nose". Of these 98 patients, 88 received the G test, 55 received the GM test, and 45 received both. A pathology analysis was used as the gold standard to diagnose IFRS. All data were analysed using SPSS 19.0. RESULTS: The sensitivities (Se) of the G and GM tests alone were 60.0% and 28.6%, respectively, whereas the specificities (Sp) were 92.3% and 93.8%, respectively. Moreover, the positive predictive values (PPV) of the G and GM tests alone were 50.0% and 40.0%, respectively, and the negative predictive values (NPV) were 94.7% and 90.0%, respectively. In addition, the diagnostic odds ratios (DOR) were 18.0 and 6.0, respectively, and the Kappa values were 0.48 (P < 0.05) and 0.25 (P > 0.05), respectively. When the G and GM tests were parallel combined, the Se was 66.7%, the Sp was 92.3%, the PPV was 57.1%, the NPV was 94.7%, the DOR was 24.0, and the Kappa value was 0.55 (P < 0.05). The present study was unable to evaluate the serial diagnosis due to the lack of patients testing positive. CONCLUSIONS: The G/GM tests exhibited low Se and PPV when used to diagnose IFRS, while high Sp and NPV. Parallel diagnosis improved the diagnostic Se and DOR values.

Automated multi-model deep neural network for sleep stage scoring with unfiltered clinical data.

PURPOSE: To develop an automated framework for sleep stage scoring from PSG via a deep neural network. METHODS: An automated deep neural network was proposed by using a multi-model integration strategy with multiple signal channels as input. All of the data were collected from one single medical center from July 2017 to April 2019. Model performance was evaluated by overall classification accuracy, precision, recall, weighted F1 score, and Cohen's Kappa. RESULTS: Two hundred ninety-four sleep studies were included in this study; 122 composed the training dataset, 20 composed the validation dataset, and 152 were used in the testing dataset. The network achieved human-level annotation performance with an average accuracy of 0.8181, weighted F1 score of 0.8150, and Cohen's Kappa of 0.7276. Top-2 accuracy (the proportion of test samples for which the true label is among the two most probable labels given by the model) was significantly improved compared to the overall classification accuracy, with the average being 0.9602. The number of arousals affected the model's performance. CONCLUSION: This research provides a robust and reliable model with the inter-rater agreement nearing that of human experts. Determining the most appropriate evaluation parameters for sleep staging is a direction for future research.

The effects of nasal decongestion on obstructive sleep apnoea.

BACKGROUND: Many studies have indicated associations between impaired nasal breathing and sleep disorders. However, the precise nature of the relationship between nasal patency and sleep remains unclear. PURPOSE: We analysed the effects of nasal patency on sleep architecture and breath in nasal obstruction-predominant obstructive sleep apnoea (NO-OSA) patients by applying nasal decongestant. MATERIAL AND METHODS: A randomized, placebo-controlled double-blind crossover study was performed in OSA patients with chronic nasal obstruction and without obvious pharyngeal narrowing. All OSA patients (confirmed by polysomnography) were recruited and completed 2 overnight studies (randomly applying oxymetazoline or placebo). Data collected after oxymetazoline or placebo treatments were compared. The ClinicalTrials.gov identifier is NCT03506178. RESULTS: Compared with placebo, oxymetazoline resulted in significant increase in rapid eye movement sleep (p = 0.027) and reduction of stage 1 sleep (p = 0.004), as well as arousal index (p = 0.002). Moreover, great improvements in apnoea/hypopnea index (AHI) were observed (p < 0.001); AHI in the supine position was significantly reduced (p = 0.001). Oxygen saturation during sleep was increased significantly [mean oxygen saturation (p = 0.005) and lowest oxygen saturation (p = 0.024)]. Oxygen desaturation index was significantly reduced (p < 0.001). CONCLUSIONS: Improving nasal patency by decongestant could improve sleep quality, AHI, and oxygen saturation level during sleep.

The predictive value of polysomnography combined with quality of life for treatment decision of children with habitual snoring related to adenotonsillar hypertrophy.

PURPOSE: Both surgical treatment and non-surgical treatment are suggested by clinicians for children with habitual snoring related to adenotonsillar hypertrophy; However, how should the decision be made remains unclear. The objective of this study was to investigate potential predictors for the treatment decision, i.e., surgical treatment vs wait and see in children with habitual snoring related to adenoidal and/or tonsillar hypertrophy. METHODS: Children with complaints of snoring and/or apnea associated with adenotonsillar hypertrophy who received polysomnography (PSG) monitoring at our Hospital were recruited. After at least 6 months, the subjects were followed up and grouped according to whether or not they had received adenoidectomy and/or tonsillectomy (AT) execution. The heights, weights, as well as the quality of life (assessed using the obstructive sleep apnea-18 (OSA-18) quality of life questionnaire) and baseline PSG of the subjects were recorded and compared. Two logistic regressions were performed to reveal the factors influencing decision-making on conducting AT. RESULTS: A total of 509 children were finally included (345 males and 164 females). Among these children, 287 eventually received AT. Significant differences in age, scores for item 1 and 5 of the OSA-18, apnea-hypopnea index, obstructive apnea index, obstructive apnea-hypopnea index (OAHI), and Lowest arterial oxygen saturation (P < 0.05) were observed between groups. By multivariate logistic regression, the factors that influenced the surgical decision were identified as follows: age < 7 years (P = 0.008: odds ratio [OR] = 1.667, 95% confidence interval [CI] 1.140-2.438), score for item 5 of OSA-18 > 4 points (P = 0.042: OR = 1.489, 95% CI 1.014-2.212) and OAHI > 1/h (P = 0.044: OR = 1.579, 95% CI 1.013-2.463). CONCLUSION: School-age children aged < 7 years, with OAHI > 1/h and mouth breathing scored > 4 points were more likely to receive AT during the disease process and thus require increased attention.

Stagewise treatment of anterior commissure laryngeal web and recurrent laryngeal papillomatosis under endoscope.

OBJECTIVE: To evaluate the stagewise treatment of anterior commissure laryngeal web caused by recurrent laryngeal papillomatosis. METHODS: One patient with anterior commissure laryngeal web caused by recurrent laryngeal papillomatosis underwent laryngomicrosurgery three times. At the same time of using CO2 laser to remove papilloma, we performed vocal cord mucosal flap repair and suture. RESULTS: After 1 year following up, laryngeal papilloma did not recur and the voice quality of the patient significantly improved with no wheezing sound. CONCLUSION: This method can resolve the problem of recurrence and adhesion in laryngeal papilloma.

The up-regulation expression of APRIL is a marker of glottic malignant disease.

A proliferation-inducing ligand (APRIL) is a member of the tumor necrosis factor (TNF) family. Recent studies have implied that APRIL is closely related to solid tumors and hematological tumors, indicating that APRIL could be a potential marker to diagnose glottic malignant disease. The purpose of this study was to investigate the difference of the APRIL mRNA and protein expression in glottic malignant disease, corresponding adjacent non-neoplastic tissues and glottic benign lesion, and detect the influence of different clinical parameter in glottic carcinoma. The APRIL mRNA expression in the glottic carcinoma, corresponding adjacent non-neoplastic tissues and glottic polypus tissue samples from patients was detected by qRT-PCR. Moreover, we studied the APRIL protein expression in pathological sections of other patients with glottic carcinoma or glottic polypus using immunohistochemistry. All the patients with different clinical parameter underwent surgery. Using qRT-PCR, we revealed an up-regulation of APRIL mRNA expression in glottic carcinoma as compared to glottic polypus and corresponding adjacent non-neoplastic tissues, but no significant difference with T stages, histopathological differentiation grade or lymph node metastasis in glottic carcinoma. The result of the immunohistochemistry was the same, with no influence of different clinical parameter in glottic carcinoma. These results strongly suggest that APRIL could be a potential diagnosed marker to distinguish glottic malignant disease from glottic benign lesion, and it may play an important role in the development of glottic malignant disease.

Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals.

BACKGROUND: Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complications. AIMS/OBJECTIVE: Our aim is to build an automatic deep learning model for OSA event detection using combined signals from the electrocardiogram (ECG) and thoracic movement signals. MATERIALS AND METHODS: We retrospectively obtained 420 cases of PSG data and extracted the signals of ECG, as well as the thoracic movement signal. A deep learning algorithm named ResNeSt34 was used to construct the model using ECG with or without thoracic movement signal. The model performance was assessed by parameters such as accuracy, precision, recall, F1-score, receiver operating characteristic (ROC), and area under the ROC curve (AUC). RESULTS: The model using combined signals of ECG and thoracic movement signal performed much better than the model using ECG alone. The former had accuracy, precision, recall, F1-score, and AUC values of 89.0%, 88.8%, 89.0%, 88.2%, and 92.9%, respectively, while the latter had values of 84.1%, 83.1%, 84.1%, 83.3%, and 82.8%, respectively. CONCLUSIONS AND SIGNIFICANCE: The automatic OSA event detection model using combined signals of ECG and thoracic movement signal with the ResNeSt34 algorithm is reliable and can be used for OSA screening.