Xiaoyong Ren

Age‐related hearing loss and balance decline are prevalent features of organismal aging, yet how the cochlea and vestibular organs converge on shared cellular liabilities remains insufficiently resolved. In particular, whether mitochondrial ultrastructural injury and mitochondrial quality‐control programs co‐vary with synaptic vulnerability and sensory functional decline across these systems within an age‐resolved framework has not been clearly delineated. Here, we compared cochlear and vestibular aging in SAMP8 mice of different ages using integrated functional assays, region‐resolved quantification of hair cells and CtBP2/GluA2 synapses, cochlear NF200+ fiber area fraction, transmission electron microscopy, and targeted qPCR of mitophagy/autophagy–lysosome genes. The results show that ABR thresholds rose progressively across 5.6–32 kHz. VsEP exhibited age‐dependent threshold shifts and prolonged P‐wave latency. Relative to the magnitude of synaptic and functional changes, cochlear hair‐cell numbers were broadly preserved, although regional OHC loss was observed in middle‐to‐basal turns, whereas vestibular macular hair‐cell density declined with age. Ultrastructurally, the proportion of pathological mitochondria increased with age, featuring electron‐lucent matrix, disrupted cristae organization, and rounded/swollen profiles. What's more, guided by an adult‐versus‐aged transcriptomic screen nominating the Ca ²⁺ extrusion gene Atp2b4, we derived z‐scored molecular indices, including a flux–burden signature (z(p62)–z(Lc3b)) and a TFEB–lysosome module. Descriptive coupling across age‐group means indicated that mitochondrial pathology burden aligned closely with high‐frequency ABR loss and basal synaptic uncoupling, and tracked the flux–burden signature more consistently than the TFEB–lysosome module. Together, these findings support age‐aligned associations among mitochondrial ultrastructural injury, molecular remodeling, synaptic vulnerability, and progressive sensory decline across cochlear and vestibular systems.

Allergic rhinitis (AR) is a common, persistent nasal disorder that poses significant public health challenges worldwide. Current treatment options frequently fail to achieve adequate symptom control in a substantial subset of patients. Over the past two decades, biologic therapies that target type 2 inflammatory pathways have been used to treat patients experiencing poorly controlled symptoms, despite standard‐of‐care (SoC) treatment. Although biological treatment options for AR remain limited worldwide, the recent approval of novel agents, such as stapokibart for seasonal allergic rhinitis (SAR), has accelerated clinical research and development in this field. Evidence for biologic therapy in the management of perennial allergic rhinitis (PAR) is currently sparse. To standardise the use of biologics in AR management and promote their evidence‐based application, a multidisciplinary expert panel was convened. This position paper evaluates current evidence regarding the efficacy and safety of biologic agents for AR, incorporating data from both international and regional clinical studies. Here, we provide recommendations on appropriate indications for biologic therapy and emphasise its role in patients with uncontrolled SAR, supporting clinical decision‐making and facilitating the integration of biologics into routine practice.

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.

The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this article, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome.

As a fundamental physiological process, sleep plays a vital role in human health. High-quality sleep requires a reasonable distribution of sleep duration over different sleep stages. Recently, contactless solutions have been used for in-home sleep stage monitoring via wireless signals as it enables monitoring daily sleep in a non-intrusive manner. However, various factors, such as the subject's physiological characteristics during sleep, the subject's health status, and even the sleep environment, pose challenges to wireless signal analysis. In this paper, we propose Hypnos, a contactless sleep monitoring system that identifies different sleep stages using an ultra-wideband (UWB) device. Hypnos enables automated bed localization and extracts signals containing coarse-grained body movements and fine-grained chest movements due to breathing and heartbeat from the subject, which acts as the preparation step for sleep staging. The key to our system is a seq2seq deep learning model, which adopts an attention-based sequence encoder to learn the patterns and transitions within and between sleep epochs and combines with contrastive learning to improve the generalizability of the encoder. Particularly, we incorporate sleep apnea detection as an auxiliary task into the model to reduce the interference of sleep apnea with sleep staging. Moreover, we design a two-step training for better adaptation of subjects with different severities of sleep disorders. We conduct extensive experiments on 100 subjects, including healthy individuals and patients with sleep disorders, and the experimental results show that Hypnos achieves excellent performance in multi-stage sleep classification (including 5-stage sleep classification), and outperforms other baseline methods.

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.

To explore the value of the synthetic MRI (SyMRI), combined with amide proton transfer-weighted (APTw) MRI for quantitative and morphologic assessment of sinonasal lesions, which could provide relative scale for the quantitative assessment of tissue properties. A total of 80 patients (31 malignant and 49 benign) with sinonasal lesions, who underwent the SyMRI and APTw examination, were retrospectively analyzed. Quantitative parameters (T1, T2, proton density (PD)) and APT % were obtained through outlining the region of interest (ROI) and comparing the two groups utilizing independent Student t test or a Wilcoxon test. Receiver operating characteristic curve (ROC), Delong test, and logistic regression analysis were performed to assess the diagnostic efficiency of one-parameter and multiparametric models. SyMRI-derived mean T1, T2, and PD were significantly higher and APT % was relatively lower in benign compared to malignant sinonasal lesions (p < 0.05). The ROC analysis showed that the AUCs of the SyMRI-derived quantitative (T1, T2, PD) values and APT % ranged from 0.677 to 0.781 for differential diagnosis between benign and malignant sinonasal lesions. The T2 values showed the best diagnostic performance among all single parameters for differentiating these two masses. The AUCs of combined SyMRI-derived multiple parameters with APT % (AUC = 0.866) were the highest than that of any single parameter, which was significantly improved (p < 0.05). The combination of SyMRI and APTw imaging has the potential to reflect intrinsic tissue characteristics useful for differentiating benign from malignant sinonasal lesions. Combining synthetic MRI with amide proton transfer-weighted imaging could function as a quantitative and contrast-free approach, significantly enhancing the differentiation of benign and malignant sinonasal lesions and overcoming the limitations associated with the superficial nature of endoscopic nasal sampling. • Synthetic MRI and amide proton transfer-weighted MRI could differentiate benign from malignant sinonasal lesions based on quantitative parameters. • The diagnostic efficiency could be significantly improved through synthetic MRI + amide proton transfer-weighted imaging. • The combination of synthetic MRI and amide proton transfer-weighted MRI is a noninvasive method to evaluate sinonasal lesions.