Obstructive sleep apnea (OSA) is a prevalent but often underdiagnosed sleep disorder linked to several health risks. While polysomnography (PSG) remains the clinical gold standard for OSA diagnosis, it is costly, timeconsuming, and uncomfortable for patients. Automated OSA detection systems based on machine learning offer a promising alternative, but most existing approaches rely on invasive physiological signals (e.g., electrocardiogram) or require specialized equipment, limiting their practicality. This study aims to develop a non-invasive, cost-effective deep learning framework for OSA detection using sleep-related respiratory sound signals. We introduce a novel latent representation learning method to extract meaningful latent features from sleep sound signals using a supervised signal-guided encoder-decoder block. A hierarchical cross-attention fusion block is then proposed to integrate the learned representations with handcrafted acoustic features and demographic information. Finally, a TabNet classifier is used to perform apnea-hypopnea event classification, leveraging an adaptive feature selection and transformation mechanism. Extensive experiments on clinical sleep laboratory data demonstrate that our model significantly outperforms existing baselines in both segment-based apnea event detection and OSA severity classification. This work provides a non-invasive, cost-effective solution for OSA detection, enabling large-scale screening and facilitating early diagnosis and intervention in clinical settings. The complete implementation of our method is publicly available at: https://github.com/pw220/osa-audio.
The increase in smart device use, including smartphones and tablets, among young children has raised concerns about its impact on health, particularly on body mass index (BMI). However, the bidirectional associations between smart device use and BMI in preschoolers remain unclear. This study examined the longitudinal associations, considering the moderating effects of mother-child interactions and child sex. Data were obtained from the Longitudinal Examination Across Prenatal and Postpartum Health in Taiwan, a cohort study conducted in Taipei, Taiwan. In total, 590 preschoolers were assessed at ages 3, 4, and 5 years. Smart device use, BMI z-scores, and mother-child interaction quality were evaluated using validated parent-reported questionnaires. The random-intercept cross-lagged panel model was used to investigate bidirectional associations, adjusting for stable confounders. Multiple-group models examined the moderating effects of mother-child interactions and child sex. Model estimates were reported as standardized coefficients. Higher BMI z-scores at age 4 years were linked to increased device use at age 5 years (β = 0.36; 95
BACKGROUND:Understanding the mechanisms underlying drug resistance in head and neck squamous cell carcinoma (HNSCC) is critical for the development of effective therapeutic strategies. M2-type tumor-associated macrophages (M2-TAMs), activated by colony-stimulating factor 1 (CSF1), play a pivotal role in promoting chemoresistance and metastasis through immunosuppressive signaling and tumor-immune crosstalk. However, the precise mechanisms of CSF1-driven tumor support and macrophage activation remain incompletely understood. METHODS:We employed humanized patient-derived xenograft (PDX) models of drug-resistant HNSCC to examine the functional roles of CSF1 and its downstream effectors. Drug-tolerant persister (DTP) cells derived from these models were subjected to transcriptomic profiling. In vitro, both direct and indirect co-culture systems were used to assess the impact of CSF1 modulation on tumor cell viability and M2 macrophage activity. The therapeutic potential of the CSF1R inhibitor pexidartinib (PLX3397), alone and in combination with cisplatin, was evaluated in vivo. RESULTS:Our findings revealed that CSF1 silencing in M2 macrophages reduced the viability of cisplatin-resistant SCC9-P and HSC3-P cells in an indirect co-culture system, indicating a paracrine survival signal. In contrast, CSF1 overexpression enhanced tumor cell proliferation. In direct co-culture, CSF1 silencing inhibited M2 macrophage activation, whereas CSF1 overexpression promoted M2 proliferation and immunosuppressive activity. In vivo, pexidartinib effectively disrupted CSF1-mediated resistance and reduced the expression of key tumor-promoting factors, including DKK1, IL10, CXCL12, and AKT1. Clinically, high DKK1 and CSF1 expression correlated with cisplatin resistance and poor prognosis. CONCLUSION:This study underscores the dual role of CSF1 in regulating both tumor survival and M2 macrophage activation in HNSCC. Targeting the DKK1/CSF1 axis may represent a promising strategy to overcome chemoresistance by disrupting tumor-macrophage crosstalk and reprogramming the immunosuppressive microenvironment.
IntroductionDengue is a global health threat, with severe cases causing significant complications. Cytokines have been proposed as potential indicators of disease severity; however, the short plasma half-life and pre-analytical instability of free-form cytokines limit their clinical applicability. Exosome-encapsulated cytokines are protected by a lipid bilayer and may provide a more stable and integrated representation of host immune responses.MethodsIn this cross-sectional study, we analyzed single time-point plasma samples collected from patients during clinical evaluation between July and December 2023, with most samples obtained during the acute phase (within 7 days post-symptom onset). Plasma exosomes were isolated from patients with mild dengue, dengue with warning signs (DFWS), severe dengue (SD), other febrile illnesses, and healthy controls (HCs). Exosome-associated cytokines were quantified using a multiplex panel assessing 15 cytokines.ResultsAcross the five study groups, significant differences in plasma exosome cytokine levels were observed for interleukin (IL)-1β, IL-6, IL-10, IL-12, interferon-γ (IFN-γ), and tumor necrosis factor (TNF)-α (all p < 0.05). Post-hoc analyses further demonstrated that IL-6, IL-10, and TNF-α levels were significantly higher in both the DFWS and SD groups compared with HCs (all p < 0.01). In a subsequent analysis comparing mild dengue with the combined DFWS/SD group, significantly higher exosomal levels of IL-1β, IL-5, IL-10, IL-12, IL-13, and TNF-α were observed in the DFWS/SD group (all p < 0.05). Receiver operating characteristic (ROC) analysis showed that IL-1β, IL-10, and TNF-α moderately discriminated mild from severe cases, with area under the curve (AUC) values of approximately 0.7. An “all-positive” panel (TNF-α, IL-10, and IL-1β) achieved 84% sensitivity and 67.5% specificity for identifying DFWS/SD.ConclusionThese findings suggest that combined exosomal cytokine profiling may aid in disease severity stratification. However, given the cross-sectional design and moderate discriminatory performance, larger prospective studies are needed to validate its clinical applicability.
