Obstructive sleep apnea (OSA) is characterised by cyclical respiratory events followed by hyperpneic breaths that frequently coincide with cortical arousals. Whilst this post-event hyperventilation partly reflects accumulated respiratory stimuli, arousal itself appears to contribute independently through both its presence and its intensity. This study aimed to quantify the relative contributions of arousal presence and arousal intensity to post-event ventilation, independent of chemoreflex-driven responses. Two retrospective polysomnography data sets were analysed: a community-based cohort (Multi-Ethnic Study of Atherosclerosis, MESA; N = 1781 included) and a Physiological Study data set (N = 67). For every obstructive respiratory event, arousal presence (0/1), arousal intensity (range 0 to 9) and post-event ventilation were derived for each event, and mixed-effects linear models were used to examine their associations at both the inter-event and inter-participant levels. At the inter-event level, arousal presence at event termination increased ventilation by 17-30%Eupnea (increase above eupneic baseline) in both data sets, with each step increment in arousal intensity contributing an additional 1-4%Eupnea. At the inter-participant level, each step increment in overnight mean arousal intensity was associated with a 2-4%Eupnea increase in the ventilatory response to arousal in both data sets. These findings demonstrate that arousal presence plays an important role in post-event hyperventilation in OSA, with arousal intensity exerting a modest effect per increment but a substantial cumulative effect across the full 0-9 scale. Consistent across both data sets, the results suggest a mechanistic pathway to post-event hyperventilation distinct from chemoreflex stimulation.

Sleep bruxism (SB) has been reportedly associated with temporomandibular disorder (TMD); however, solid evidence is lacking. Previous studies have primarily used traditional metrics, such as the masticatory muscle activity (MMA) index and bruxism time index (BTI) to investigate the link between SB and TMD. However, we aimed to examine how the electromyography (EMG) frequency spectrum is associated with TMD in SB participants. We hypothesised that the EMG signal frequencies during MMA events would be lower in SB participants with TMD pain compared to those without TMD pain. In this exploratory study, we retrospectively analysed home polysomnography data from 44 participants who indicated possible SB. The median signal frequencies and absolute power were calculated using the Fast Fourier Transform of the EMG signals during MMA events. Moreover, the MMA index and BTI were calculated, and all parameters were compared between SB participants with and without TMD pain. The results showed that the absolute power and median frequencies were significantly lower in SB participants with TMD pain compared to those without TMD pain (p < 0.05), whereas the MMA index and BTI did not differ between the groups (p > 0.05). These findings suggest that masticatory muscles are getting fatigued in TMD participants with SB and therefore, EMG frequency-based analysis may provide a promising direction for future assessment of TMD consequences of SB. However, these preliminary results should be validated in future studies involving a larger and more heterogeneous pool of participants.

Obstructive sleep apnea (OSA) is a common nocturnal breathing disorder characterized by recurrent upper airway obstruction during sleep, leading to sleep fragmentation and intermittent hypoxemia. The apnea-hypopnea index (AHI) is the most widely used clinical metric to assess OSA severity; however, the AHI fails to capture the full physiological burden caused by recurrent oxygen desaturations. Consequently, quantifying hypoxemia, the cumulative burden of oxygen desaturations, has become crucial for assessing the physiological impact of OSA more comprehensively. Furthermore, studies have illustrated that hypoxemia is associated with cardiovascular morbidity, metabolic dysfunction, and neurocognitive impairment in OSA patients. This narrative review aims to explore various methods used to quantify nocturnal hypoxemia from sleep recordings and discuss the different approaches, and their association with OSA-related comorbidities. The discussed metrics include traditional measures and newer parameters that provide a more detailed quantification of oxygen desaturation, including separate characterization of desaturation and re-saturation dynamics as well as integrated measures of the overall hypoxic load. Additionally, this review discusses how these more detailed metrics have provided further insight into the associations between OSA-related hypoxemia and clinically relevant outcomes, including cardiometabolic comorbidities and impaired daytime functioning. This narrative review emphasizes the need for diagnostic modifications and advocates for the integration of metrics quantifying hypoxemia into routine clinical practice to enable early recognition of the health risks associated with OSA.

STUDY OBJECTIVES:To examine the prognostic value of the apnea-hypopnea index (AHI), desaturation severity parameters, and cardiac troponins alone and combined for major cardiovascular events (MACEs). METHODS:MACE data were retrieved in 2021 from the Norwegian Patient Registry for 518 participants in the Akershus Sleep APnea (ASAP) cohort. Baseline polysomnography and fasting blood samples were collected between June 2006 and January 2008. Desaturation duration (DesDur) and severity (DesSev) were calculated using Automatic Blood Oxygen Saturation Analysis software. Cox regression models estimated hazard ratios (HRs) for MACE. Predictive properties of combining troponins and obstructive sleep apnea severity were calculated by comparing established clinical thresholds for cardiac troponin I (cTnI) and T (cTnT) with AHI clinical thresholds of ≥15 and ≥30, respectively. RESULTS:High AHI, DesDur, DesSev, cTnI, and cTnT were associated with increased MACE risk. However, only cTnI independently predicted MACE after adjustment (HR: 1.74, 95% CI: 1.32-2.29). The HR for MACE was 2.68 (95% CI: 1.03-6.97) in patients with both high cTnI and AHI ≥30 events/h. CONCLUSION:In this 15-year follow-up, cTnI was independently associated with risk of MACE, whereas the AHI, desaturation parameters, and cTnT were not independent predictors. cTnI, especially when combined with AHI, was a stronger MACE predictor than cTnT. Provided our findings are validated in clinical obstructive sleep apnea populations, the measurement of cTnI may be considered for cardiovascular risk stratification.

Hydrogels are key components of tissue-engineered constructs. Optimizing tissue engineering (TE) requires understanding how hydrogel mechanical properties evolve during culture; however, current assessments are limited to discrete time points due to the destructive nature of conventional methods. This study provides a detailed characterization of the daily evolution of the mechanical properties of gelatin-methacrylate (GelMA) hydrogels using a non-destructive approach integrating near-infrared spectroscopy (NIRS) and machine learning (ML). GelMA samples were incubated for 28 days with daily in situ spectral acquisition, while destructive mechanical testing yielded their equilibrium (Eeq) and dynamic (Ed) moduli at five time points. Labeled spectra were used to develop ML models to predict hydrogel mechanics, which were then applied to unlabeled spectra from an independent set. The spectroscopy-based models outperformed conventional time-based models, achieving 37.6% and 22.1% lower mean absolute error for Eeq and Ed, respectively. Median predictions for the independent evaluation set showed strong correlation with measured moduli (R² = 0.91 for Eeq and 0.93 for Ed). Monitoring revealed an increase in mechanical properties up to day 14, followed by a decline. By enabling longitudinal monitoring, NIRS can provide detailed insights into the evolution of hydrogel mechanics, supporting improved interpretation and design of hydrogel-based TE studies.

STUDY OBJECTIVES:To predict the improvement in sleepiness following continuous positive airway pressure (CPAP) treatment for obstructive sleep apnea (OSA) using baseline characteristics. METHODS:Data from five polysomnography cohorts (HomePAP, BestAIR, ABC, STAGES, and MESA; total n=2332) were analyzed. Associations between polysomnographic metrics and Epworth Sleepiness Scale (ESS) were assessed using multivariable linear regression. Cross-validated LASSO regression was used to select predictor variables. Prediction of ESS improvement was evaluated in CPAP-treated participants (n=213) using linear regression models trained on 139 participants and tested in a holdout group (n=74). RESULTS:Cross-sectional analysis revealed significant associations between baseline ESS and ventilatory burden (0.56 points/SD; 95% CI 0.34-0.78), hypoxic burden (0.51; 0.28-0.74), the apnea-hypopnea index (AHI, 0.45; 0.23-0.68), time below 90% oxygen saturation (0.36; 0.15-0.57), and flow limitation severity (0.31; 0.10-0.53). Baseline ESS and baseline ventilatory burden were the most frequently selected predictors of ESS improvement. Using these two variables in a regression model, predicted and actual ESS improvement were correlated in the holdout group (adjusted R2=0.313). The model classified CPAP responders (≥2 points ESS improvement) more accurately than current clinical guidelines (78.1% [3.5% SD] versus 68.4% [3.7%]). Substituting the AHI for ventilatory burden slightly reduced accuracy (77.3% [3.4%]). Charts were developed to assist in predicting ESS improvement from baseline metrics. CONCLUSIONS:Models incorporating baseline ESS and either ventilatory burden or AHI predict ESS improvement and may help identify patients with OSA likely to benefit from CPAP. The ventilatory burden model demonstrated modestly better performance, while the AHI-based model offers greater clinical applicability.

Osteoarthritis (OA) is a debilitating joint disease in which early microstructural and compositional changes in articular cartilage (AC) are challenging to detect with current diagnostic tools. We hypothesized that multispectral imaging (MSI) could capture optical signatures that predict key AC properties. To test this, we developed and evaluated an MSI-based approach for estimating tissue thickness, proteoglycan (PG) content, collagen fiber orientation, and cell morphological properties (area and circularity). Reflectance images of bovine patellar AC were acquired using a custom-built MSI system operating at six wavelengths (550-970 nm), and machine learning models were trained to predict the targeted markers. The models yielded reliable estimates for AC thickness, whereas for PG content, collagen fiber orientation, and cell circularity, they achieved moderate accuracy. This work suggests that MSI holds promise as a label-free tool for characterizing AC and detecting early degenerative changes.

This study investigates the capacity of near-infrared spectroscopy (NIRS) for classifying hyaluronan (HA) levels in equine synovial fluid. NIRS, combined with machine learning, discriminated between low and high HA levels with an accuracy of 81%, highlighting its potential as a promising rapid osteoarthritis diagnostic tool.

Finite element analysis (FEA) is the leading numerical technique for studying joint biomechanics related to the onset and progression of osteoarthritis. However, subject-specific FEA of joint mechanics is a time- and compute-intensive process limiting its clinical applicability. We introduce and evaluate a novel hybrid modelling framework combining discrete element analysis (DEA) and FEA for computationally efficient evaluation of cartilage mechanics in the hip joint. In our approach, the hip joint contact mechanics are first estimated using DEA and subsequently used as input for matching FEA models, substantially reducing model complexity. The cartilage mechanical responses obtained using the hybrid DEA-FEA method were evaluated for subject-specific hip joint geometries from five asymptomatic individuals under loading conditions typical to normal walking gait and compared to conventional FEA in terms of peak intra-tissue mechanical stresses and model run-times. The hybrid DEA-FEA method had a median run-time of 3.6 min per subject (64-core processor, 512 GB RAM) and produced minimum principal (compressive) stress estimates comparable to stresses obtained using conventional FEA models with a median run-time of 96.2 min. On average, the peak compressive stresses obtained using the hybrid DEA-FEA approach were 0.06 MPa (95 % confidence interval: -0.86-0.99) lower than the stresses estimated with conventional FEA. Despite up to 1.4 MPa differences at individual gait time-points, the results indicate that the proposed hybrid DEA-FEA method enables estimation of hip cartilage mechanics in a fraction of time compared to conventional FEA, facilitating implementation in large cohort studies and clinical applications.

Osteoarthritis is well established to be a whole joint disease, and previous studies have found degenerative changes in periarticular tendons around osteoarthritic joints. Thus, we aimed to assess whether potential periarticular adaptations (patellar tendon) are related to intraarticular changes (tibial cartilage) at material-level. As they are both connected to the tibia, we explored the relationships between patellar tendon and tibial cartilage viscoelastic properties obtained from eight osteoarthritic cadaver knees. Six patellar tendon regions and six to eight tibial cartilage samples per knee underwent tensile and indentation sinusoidal measurements, respectively, at 0.1, 0.5, and 1 Hz. Osteoarthritis Research Society International (OARSI) grades were obtained from tibial cartilage histological sections and tested for associations with patellar tendon properties. All knees were deemed osteoarthritic according to tibial cartilage OARSI grades. Comparing the two tissues, we found strong inverse correlations in the phase difference between stress and strain (r: -0.887 to -0.934). Patellar tendon phase difference was also strongly, inversely correlated with average tibial cartilage OARSI grade (r: -0.889 to -0.890). Patellar tendon dynamic modulus was moderately correlated with tibial cartilage dynamic modulus (r: -0.561 to -0.575) and OARSI grade (r: 0.631-0.646). For the first time, we show that the viscoelastic properties of the human patellar tendon are strongly, inversely related to tibial cartilage viscoelastic properties and OARSI grades in osteoarthritic knees, suggesting these two tissues may undergo diverging adaptations with osteoarthritis. These results provide a foundation for more detailed future investigations on patellar tendon-cartilage interactions in knee osteoarthritis.

Photon-counting detectors (PCDs) are cutting-edge technology that enable spectral computed tomography (CT) imaging with a single scan. Spectral imaging is particularly effective in contrast-enhanced CT (CECT) imaging, especially when multiple contrast agents are utilized, as materials are distinguishable based on their unique X-ray absorption. One application of CECT is joint imaging, where it assesses the structure and composition of articular cartilage soft tissue. This evaluates articular cartilage and reveals compositional changes associated with early-stage osteoarthritis (OA) using a photon-counting detector CT (PCD-CT) technique combined with a dual-contrast agent method. A dual-contrast agent combination was used, consisting of proteoglycan-binding cationic tantalum oxide nanoparticles, developed in our lab, and a commercial non-ionic iodinated iodixanol agent. Ex vivo equine stifle joint cartilage samples (N = 30) were immersed in the contrast agent bath for 96 hours and imaged at multiple timepoints for analysis of proteoglycan, collagen, and water contents as well as collagen orientation, histological scoring, and biomechanical parameters. By analyzing contrast agent concentrations, the technique provided a simultaneous assessment of the solid constituents and function of cartilage. Contrast agent diffusion depended on contrast agent composition and was significantly different between healthy and early-stage OA groups within 12 hours. The present study shows the promising utility of the dual-contrast PCD-CT technique for articular cartilage assessment and early-stage OA detection.

The prevalence of positional obstructive sleep apnea (POSA) in community populations warrants further investigation. Further, more research is needed into the clinical characteristics of its subtypes such as supine predominant OSA (spOSA) and supine isolated OSA (siOSA). A cross-sectional analysis was performed on 1,870 Sleep Heart Health Study participants. OSA was defined by an apnea-hypopnea index (AHI) of ≥ 5 events/h of sleep. Participants with OSA were classified as POSA if the supine AHI was ≥ 2 times the nonsupine AHI. Participants with OSA who did not meet this threshold were classified as participants with nonpositional OSA. Demographics, polysomnographic data, comorbidities, and medications were all considered. The POSA subtypes spOSA and siOSA were also investigated. Participants with POSA were slightly older, less obese, and had higher systolic blood pressure than participants with nonpositional OSA, in addition to being more prevalent (62

Quantitative T2 mapping is an important MRI method for assessing degenerative changes in articular cartilage. Recently, in a measurement setup with automated sample re-orientation, it was demonstrated that T2 can be split into its orientation-independent components. This quantitative MRI study aims to assess the diagnostic significance of the automated approach with ex vivo human cartilage. T2 maps of 30 human osteochondral samples harvested from 5 cadaveric individuals were acquired at 9.4T in 13 orientations, allowing calculation of the T2 components. Additionally, T1, adiabatic T1ρ, and continuous wave T1ρ with two spin-lock frequencies were scanned in a single orientation. For reference, the collagen network anisotropy, proteoglycan content and biomechanical indentation properties were measured. The relationships between quantitative MRI and reference parameters were studied using Mann-Whitney U-test and Spearman’s rank correlation. All parameters were compared between healthy and degenerated groups based on OARSI grading. The anisotropic relaxation rate component of T2 (R2a), and all T1 and T1ρ parameters differed (p < 0.05) between the groups in superficial cartilage. R2a correlated moderately with PLM anisotropy (r = 0.44) and optical density (r = − 0.37) in the deep zone. Isotropic T2 component (R2i) correlated with instantaneous modulus (r = 0.48), and R2a with phase shift between stress and strain during indentation testing (r = − 0.44). T1 and T1ρ parameters correlated with both, instantaneous and dynamic modulus in several zones of cartilage. The elevation of T2 in degenerated cartilage is primarily driven by the R2a component, whereas the R2i component showed no significant difference between healthy and degenerated human articular cartilage.

Introduction:Segmenting sleep into fixed 30-second epochs remains central to current sleep scoring practice, yet it imposes rigid boundaries that may not accurately reflect the true temporal sleep dynamics. We aimed to develop a deep learning-based, high-temporal-resolution sleep-wake classifier leveraging temporally continuous manual reference scoring without fixed epoch boundaries and transfer learning techniques to facilitate progress toward a more physiologically consistent sleep assessment. Methods:Three independent datasets were utilized, of which two included sleep-wake scoring manually conducted in a temporally continuous manner. A U-Net based model was initially trained on a large dataset scored using 30-second epochs, with post hoc scoring modifications (n=2034). It was then fine-tuned via transfer learning using a subset of one of the datasets with temporally continuous scoring (n=39) and validated on both its holdout portion (n=40) and the other independent temporally continuous scoring dataset (n=20). Wakefulness and arousals were consolidated, acknowledging their shared physiological characteristics. Prediction confidence estimates were also generated. Results:The model achieved overall concordance of 88.96% (κ=0.78) and 88.23% (κ=0.76) in the holdout and second independent evaluation dataset, respectively, with temporally continuous scoring. Correlation between 1-second automatic predictions and temporally continuous manual scoring was r=0.93 (p<0.001) for total sleep time and r=0.67 (p<0.001) for sleep-to-wake transition index. Conclusions:These findings support the utility of our model in addressing key limitations of 30-second epoch-based scoring and progressing toward more physiologically consistent sleep-wake assessment by providing a practical basis for subsequent analyses. Misclassifications generally showed lower confidences, indicating additional value for targeted review.

Arousal burden (AB) is defined as the cumulative duration of arousals during sleep divided by the total sleep time. However, in-depth analysis of AB related to sleep characteristics is lacking. Based on previous studies addressing the arousal index, we hypothesized that the AB would peak in the supine sleeping position and during non-rapid eye movement stage 1 and show high variability between scorers. Nine expert scorers analyzed polysomnography recordings of 50 participants, the majority with an increased risk for obstructive sleep apnea. AB was calculated in different sleeping positions and sleep stages. A generalized estimating equation was used to test the association between AB and sleeping positions, sleep stages, and scorers. The correlation between AB and arousal index was tested with Spearman’s rank-order correlation. AB significantly differed between sleeping positions (P < .001). The median AB in the supine sleeping position was 47–62

The functional regeneration of human articular cartilage is hampered by a lack of biomaterials and tissue engineering strategies that adequately capture the physiological depth-dependent compression properties of native tissue. Here, we demonstrate that photocrosslinkable gelatin-hyaluronic acid hydrogels reinforced with multiphasic polycaprolactone microfibre scaffolds form biomimetic soft network composites that serve as in vitro models mimicking the compressive and depth-dependent deformation characteristics of human cartilage. Mono- and multi-phasic gradient scaffolds with fibre spacings of 200, 400, and 800 μm were manufactured using melt electrowriting and embedded in the photocrosslinkable hydrogel system. Mechanical testing combined with finite element analysis revealed how defined microfibre architecture is altered and influences compressive moduli and interstitial fluid load support to mimic the loading response of native articular cartilage in our in vitro model. Digital image and volume correlations demonstrated depth-dependent strain fields in the fibre-reinforced constructs in response to compression, demonstrating biomimetic depth-dependent behaviour. Lastly, we demonstrate that these fibre-reinforced hydrogels support high cell viability, chondrogenic redifferentiation and hyaline-like tissue formation by expanded human articular chondrocytes in vitro. Together, this study demonstrates that, in vitro, these hydrogels reinforced with gradient scaffolds successfully recapitulate key biomechanical traits of native articular cartilage, toward the development of improved models for functional cartilage tissue engineering.

This study investigates the relationship between the optical properties of articular cartilage with its biomechanical parameters. The absorption and reduced scattering coefficients of articular cartilage, and average maximum penetration depth, average maximum lateral spread, and average path length of photons were estimated by optical measurements and Monte Carlo simulation. The equilibrium and instantaneous moduli, and initial fibrillar network, strain-dependent fibrillar network, and nonfibrillar network moduli, and initial and deformation-dependent permeability of the tissue were estimated by multistep stress-relaxation measurement and fibril-reinforced poroelastic modeling. The relationship between the optical properties and biomechanical parameters was assessed using predictive regression modeling. A strong relationship was found between reduced scattering coefficients, averaged maximum penetration depth, averaged maximum lateral spread, and average path length of photons with equilibrium, instantaneous, and initial fibrillar network moduli. We attribute this relationship to the collagen fibers as the main contributor to the scattering and biomechanical properties of the tissue.