Yitong Zhang

Purpose: This study unveils the complex interplay among obstructive sleep apnea (OSA), thyroid function, and morphological changes. Methods: Data from 1,102 patients were collected retrospectively from the Department of Otorhinolaryngology—Head and Neck Surgery of the Second Affiliated Hospital of Xi’an Jiaotong University from 2012 to 2023. The patients were divided into severe and non-severe OSA groups according to their polysomnography results. The data were analyzed by sex and age stratification. Results: Serum free triiodothyronine (FT3), total triiodothyronine (TT3), and reverse triiodothyronine (RT3) were higher in severe OSA group in the total population (p < 0.05). Similar trends were observed in male but not in female. FT3 and TT3 are higher in the severe group in the nonelderly population (age < 60) (p < 0.05), and RT3 is higher in the severe group in the elderly population (age ≥ 60) (p < 0.05). In addition, we first reveal that RT3 is associated with the diameter of the left inferior thyroid artery (L-ITA) (r=0.394, p < 0.05) and lowest transcutaneous oxygen saturation at night (lowest SpO₂) (r=− 0.269, p < 0.05). The severe OSA group showed larger thyroid volume and isthmus length, as well as the thicker ITA diameter and lower left thyroid lobe resistance index (RI) (all p < 0.05). Conclusion: Our study demonstrates a significant association between thyroid function/morphology and OSA, with distinct sex- and age-related differences. Reduced RI in severe OSA suggests its clinical utility in assessing vascular health. Increased thyroid volume and isthmus length in severe OSA may reflect ITA-related changes. These findings support our prior observations of rising thyroid hormone levels with OSA progression and highlight the need for sex- and age-stratified analyses. Integrated evaluation of thyroid function and morphology is essential for understanding OSA-thyroid pathophysiology.

As the two most typical classic network models, Convolutional Neural Networks (CNN) and Transformer have been widely applied in Obstructive sleep apnea (OSA) detection in recent years. However, due to the inherent limitations of the receptive field in traditional CNN models (the receptive field is positively correlated with the fixed convolution kernel size, and the ability to extract global feature information is limited), further improvement in their performance is constrained. While, for Transformer, due to the computational complexity of the self attention mechanism in the Transformer model increases exponentially with the length of the context, it will hold a very high computational overhead, and which would hinder the deployment of transformer on devices with limited computing resources. To address these problems, this paper proposes an adaptive bidirectional selective state space model based method for OSA detection, termed as OSAMamba. The main novelty of the proposed method lies in the following two aspects: the development of the lightweight multi-scale efficient aggregation (LMSEA) module, and the propose of the adaptive bidirectional selective state space model (ABSM). To achieve the purpose of expanding the model receptive field and capturing the effective temporal features with a very low number of parameters, the LMSEA module adopts a combination of partial convolution (PConv)-based multiscale strategy and convolutional block attention module (CBAM). The purpose of the ABSM module is to reduce the computational cost of the model and improve the model deployability by using a frequency domain enhancement strategy to fuse the effective time domain features extracted by adaptive bi-directional Mamba (ABi-Mamba) with linear complexity with the frequency domain features extracted by the Frequency Domain Enhancement Module (FEM). Extensive experiments on the Apnea-ECG dataset show that of all compared methods, the proposed method obtains the best accuracy of 91.91% in the per-segment detection, and which surpassing the state-of-the-art (SOTA) TFFormer by 0.31%. It also achieves a remarkable accuracy of 100% with the lowest mean absolute error (MAE) of 2.43 in per-record detection.

Purpose: To develop a deep learning (DL) model for obstructive sleep apnea (OSA) detection and severity assessment and provide a new approach for convenient, economical, and accurate disease detection. Methods: Considering medical reliability and acquisition simplicity, we used electrocardiogram (ECG) and oxygen saturation (SpO₂) signals to develop a multimodal signal fusion multiscale Transformer model for OSA detection and severity assessment. The proposed model comprises signal preprocessing, feature extraction, cross-modal interaction, and classification modules. A total of 510 patients who underwent polysomnography were included in the hospital dataset. The model was tested on hospital and public datasets. The hospital dataset was utilized to demonstrate the applicability and generalizability of the model. Two public datasets, Apnea-ECG dataset (consisting of 8 recordings) and UCD dataset (consisting of 21 recordings), were used to compare the results with those of previous studies. Results: In the hospital dataset, the accuracy (Acc) values of per-segment and per-recording detection were 91.38 and 96.08%, respectively. The Acc values for mild, moderate, and severe OSA were 90.20, 88.24, and 92.16%, respectively. The Bland‒Altman plots revealed the consistency of the true apnea–hypopnea index (AHI) and the predicted AHI. In the public datasets, the per-segment detection Acc values of the Apnea-ECG and UCD datasets were 95.04 and 90.56%, respectively. Conclusion: The experiments on hospital and public datasets have demonstrated that the proposed model is more advanced, accurate, and applicable in OSA detection and severity assessment than previous models.

Purpose: Aimed to analyze the developmental characteristics of craniofacial structures and soft tissues in children with obstructive sleep apnea (OSA) and to establish and evaluate prediction model. Methods: It's a retrospective study comprising 747 children aged 2-12 years (337 patients and 410 controls) visited the Department of Otolaryngology-Head and Neck Surgery, the Second Affiliated Hospital of Xi'an Jiaotong University (July 2017 to March 2024). Lateral head radiographs were obtained to compare the cephalometric measurements. The clinical prediction model was constructed using LASSO regression analysis. We analyzed 300 children from the Xi'an Children's Hospital for external validation. Results: Children with OSA had a higher body mass, a higher tonsil grade, larger AN ratio (ratio of the adenoids to the skeletal upper airway width), larger radius of the tonsils, a smaller angle between the skull base and maxilla (SNA) and smaller angle between the skull base and mandible (SNB), a larger distance from the hyoid to the mandibular plane (H-MP) and smaller distance between the third cervical vertebra and hyoid (H-C), a larger thickness of the soft palate (SPT) and smaller inclination angle of the soft palate than those of the controls (all p < 0.05). A prediction model was constructed for 2-12 years group (AUC of 0.812 [95% CI: 0.781-0.842]). Age-specific prediction models were developed for preschool children (AUC of 0.769 [95% CI: 0.725-0.814]), for school-aged children (AUC of 0.854 [95% CI: 0.812-0.895]). Conclusion: Our study findings support the important role of craniofacial structures such as the hyoid, maxilla, mandible, and soft palate in pediatric OSA. Age-stratified predictive models for pediatric OSA indicated varying parameters across different age groups which underscore the necessity for stratifying by age in future research. The prediction model designed will greatly assist health care practitioners with rapidly identifying.

Although Transformer-based methods have achieved significant success in Obstructive sleep apnea (OSA) detection, they suffer from higher computational costs, lower feature capturing ability in frequency domain, and class imbalance at the data level. To address these problems, this paper proposes a data rebalancing loss constrained light transformer for OSA detection, called DRLFormer. The core of the proposed method lies in the following two aspects: the development of the Additive Temporal-Frequency Fusion Attention (ATFA) module, and the propose of Data Re-balancing Loss (DRLoss) function. The purpose of the ATFA module is to fuse the frequency domain features extracted by the frequency domain enhancement module (FEM) with the important time domain features extracted by the learnable time domain attention (LTA) in a very low computational cost by novel using the summation strategy. The purpose of DRLoss is to solve the problem of imbalanced samples within batches which usually ignored by existing common used CrossEntropy loss (CELoss) . the novelty of the designing of DRLoss lies in by designing weight memory units (WMU), and allocatting different weights for correctly and incorrectly predicted samples to achieve prediction level balance through classification penalty item (CPI). Extensive experiments are conducted on the Apnea-ECG dataset, and compared with the state-of-the-art method. Our method achieving an improving by 0.44% in accuracy (from 91.68% improved to 92.12%) with a reduction of parameter size by 74% and FLOPs by 53%. Furthermore, the excellent performance was achieved on UCD, PhysioNet, and clinical XJ300 datasets, thoroughly verifying the effectiveness and clinical application potential of DRLFormer.

Accurate detection of Obstructive Sleep Apnea (OSA) with a single-lead electrocardiogram (ECG) signal is highly desirable for timely treating of OSA patients. However, due to the variance of apneas in appearance and size in ECG signals, it is still a very challenging task to obtain an accurate OSA apnea detection. To address this problem, this paper presents a time frequency information fusion based CNN-Transformer model (TFFormer) for OSA detection with Single-lead ECG. In which, a module consisting of a deep residual shrinkage module, a multi-scale convolutional attention module (MSCA), and a multi-layer convolution module is developed for time-frequency feature extraction. The purpose of this operation is to extract rich features from a short length of ECG signal sequences with a low computation cost. For time-frequency information fusion, to reduce its computation cost, a gated self-attention based adaptive pruning time-frequency information fusion module is developed to prune the redundant tokens. With the attention based adaptive pruning time-frequency information fusion module(APTFFA), the TFFormer is constructed for data parallel processing and long-distance modeling. Compared with the best model in the comparative method, the accuracy of the proposed method was improved by 0.18% in the segmented case, and the mean absolute error was reduced by 0.25 per-recorded case, which demonstrates that the TFFormer model has better OSA detection performance and could provide a convenient and accurate solution for clinical OSA detection.

Ye Gao, Lei Xin, Yi-Tong Zhang, Xiao-Rong Guo, Qian-Qian Meng, Zhao-Shen Li, and Zhuan Liao. Gut and Liver 1999;0:. https://doi.org/10.5009/gnl19345

Aberrant methylation is one of the most frequent epigenetic alterations that regulate the expression levels of genes, including long non‑coding RNAs (lncRNAs), in tumors. However, to the best of our knowledge, the expression and function of hepatic nuclear factor 1α antisense RNA 1 (HNF1A‑AS1) and its methylation condition have not yet been reported in the development and progression of laryngeal squamous cell carcinoma (LSCC). In the present study, the expression and methylation of HNF1A‑AS1 were first examined by reverse transcription‑quantitative PCR, bisulfite genomic sequencing and methylation‑specific polymerase chain reaction in samples from patients with LSCC, which were based on the in silico analysis using The Cancer Genome Atlas data, and were then further verified in LSCC cell lines with and without 5‑Aza‑2'‑deoxycytidine (5‑Aza‑dC) treatment. Subsequently, proliferation, cell cycle distribution, migration and invasion of LSCC cells following either knockdown or overexpression of HNF1A‑AS1 were determined in vitro. Furthermore, the characteristic of HNF1A‑AS1 on epithelial‑mesenchymal transition (EMT) changes was investigated in vitro and in vivo. The associations between the expression levels of HNF1A‑AS1 and tumorigenicity and cervical lymph node metastasis were assessed in a xenograft model in nude mice. In the present study, downregulation and hypermethylation in CpG sites of HNF1A‑AS1 were detected in LSCC tissues as well as metastatic cervical lymph nodes samples when compared with those in the adjacent non‑tumor tissues. Additionally, HNF1A‑AS1 inhibited proliferation, migration and invasion of LSCC cells in vitro by regulating the process of EMT. Furthermore, HNF1A‑AS1 inhibited tumor growth and metastasis by regulating EMT in vivo. Additionally, the migration and invasion abilities, and the expression levels of HNF1A‑AS1 and EMT markers in LSCC cells were significantly reversed by treatment with 5‑Aza‑dC. In summary, HNF1A‑AS1 was downregulated by hypermethylation in LSCC and laryngeal cancer cells. These findings suggested that HNF1A‑AS1 could serve as a tumor suppressor lncRNA in LSCC by regulating the EMT process, leading to the discovery of novel therapeutic targets and strategies for the treatment of patients with LSCC.