Deciphering Optimal Radar Ensemble for Advancing Sleep Posture Prediction through Multiview Convolutional Neural Network (MVCNN) Approach Using Spatial Radio Echo Map (SREM).

Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.

SaccpaNet: A Separable Atrous Convolution-based Cascade Pyramid Attention Network to Estimate Body Landmarks Using Cross-modal Knowledge Transfer for Under-blanket Sleep Posture Classification.

The accuracy of sleep posture assessment in standard polysomnography might be compromised by the unfamiliar sleep lab environment. In this work, we aim to develop a depth camera-based sleep posture monitoring and classification system for home or community usage and tailor a deep learning model that can account for blanket interference. Our model included a joint coordinate estimation network (JCE) and sleep posture classification network (SPC). SaccpaNet (Separable Atrous Convolution-based Cascade Pyramid Attention Network) was developed using a combination of pyramidal structure of residual separable atrous convolution unit to reduce computational cost and enlarge receptive field. The Saccpa attention unit served as the core of JCE and SPC, while different backbones for SPC were also evaluated. The model was cross-modally pretrained by RGB images from the COCO whole body dataset and then trained/tested using dept image data collected from 150 participants performing seven sleep postures across four blanket conditions. Besides, we applied a data augmentation technique that used intra-class mix-up to synthesize blanket conditions; and an overlaid flip-cut to synthesize partially covered blanket conditions for a robustness that we referred to as the Post-hoc Data Augmentation Robustness Test (PhD-ART). Our model achieved an average precision of estimated joint coordinate (in terms of PCK@0.1) of 0.652 and demonstrated adequate robustness. The overall classification accuracy of sleep postures (F1-score) was 0.885 and 0.940, for 7- and 6-class classification, respectively. Our system was resistant to the interference of blanket, with a spread difference of 2.5%.

Piezo1 stretch-activated channel activity differs between murine bone marrow-derived and cardiac tissue-resident macrophages.

Macrophages (MΦ) play pivotal roles in tissue homeostasis and repair. Their mechanical environment has been identified as a key modulator of various cell functions, and MΦ mechanosensitivity is likely to be critical - in particular in a rhythmically contracting organ such as the heart. Cultured MΦ, differentiated in vitro from bone marrow (MΦBM), form a popular research model. This study explores the activity of mechanosensitive ion channels (MSC) in murine MΦBM and compares it to MSC activity in MΦ enzymatically isolated from cardiac tissue (tissue-resident MΦ; MΦTR). We show that MΦBM and MΦTR have stretch-induced currents, indicating the presence of functional MSC in their plasma membrane. The current profiles in MΦBM and in MΦTR show characteristics of cation non-selective MSC such as Piezo1 or transient receptor potential channels. While Piezo1 ion channel activity is detectable in the plasma membrane of MΦBM using the patch-clamp technique, or by measuring cytosolic calcium concentration upon perfusion with the Piezo1 channel agonist Yoda1, no Piezo1 channel activity was observed in MΦTR. The selective transient receptor potential vanilloid 4 (TRPV4) channel agonist GSK1016790A induces calcium entry in MΦTR and in MΦBM. In MΦ isolated from left-ventricular scar tissue 28 days after cryoablation, stretch-induced current characteristics are not significantly different compared to non-injured control tissue, even though scarred ventricular tissue is expected to be mechanically remodelled and to contain an altered composition of pre-existing cardiac and circulation-recruited MΦ. Our data suggest that the in vitro differentiation protocols used to obtain MΦBM generate cells that differ from MΦ recruited from the circulation during tissue repair in vivo. Further investigations are needed to explore MSC identity in lineage-traced MΦ in scar tissue, and to compare mechanosensitivity of circulating monocytes with that of MΦBM. KEY POINTS: Bone marrow-derived (MΦBM) and tissue resident (MΦTR) macrophages have stretch-induced currents, indicating expression of functional mechanosensitive channels (MSC) in their plasma membrane. Stretch-activated current profiles show characteristics of cation non-selective MSC; and mRNA coding for MSC, including Piezo1 and TRPV4, is expressed in murine MΦBM and in MΦTR. Calcium entry upon pharmacological activation of TRPV4 confirms functionality of the channel in MΦTR and in MΦBM. Piezo1 ion channel activity is detected in the plasma membrane of MΦBM but not in MΦTR, suggesting that MΦBM may not be a good model to study the mechanotransduction of MΦTR. Stretch-induced currents, Piezo1 mRNA expression and response to pharmacological activation are not significantly changed in cardiac MΦ 28 days after cryoinjury compared to sham operated mice.

Neuropsychiatric complications of coronavirus disease 2019: Mount Sinai Health System cohort study.

OBJECTIVE: To describe the frequency of neuropsychiatric complications among hospitalized patients with coronavirus disease 2019 (COVID-19) and their association with pre-existing comorbidities and clinical outcomes. METHODS: We retrospectively identified all patients hospitalized with COVID-19 within a large multicenter New York City health system between March 15, 2020 and May 17, 2021 and randomly selected a representative cohort for detailed chart review. Clinical data, including the occurrence of neuropsychiatric complications (categorized as either altered mental status [AMS] or other neuropsychiatric complications) and in-hospital mortality, were extracted using an electronic medical record database and individual chart review. Associations between neuropsychiatric complications, comorbidities, laboratory findings, and in-hospital mortality were assessed using multivariate logistic regression. RESULTS: Our study cohort consisted of 974 patients, the majority were admitted during the first wave of the pandemic. Patients were treated with anticoagulation (88.4%), glucocorticoids (24.8%), and remdesivir (10.5%); 18.6% experienced severe COVID-19 pneumonia (evidenced by ventilator requirement). Neuropsychiatric complications occurred in 58.8% of patients; 39.8% experienced AMS; and 19.0% experienced at least one other complication (seizures in 1.4%, ischemic stroke in 1.6%, hemorrhagic stroke in 1.0%) or symptom (headache in 11.4%, anxiety in 6.8%, ataxia in 6.3%). Higher odds of mortality, which occurred in 22.0%, were associated with AMS, ventilator support, increasing age, and higher serum inflammatory marker levels. Anticoagulant therapy was associated with lower odds of mortality and AMS. CONCLUSION: Neuropsychiatric complications of COVID-19, especially AMS, were common, varied, and associated with in-hospital mortality in a diverse multicenter cohort at an epicenter of the COVID-19 pandemic.

Elevated phase amplitude coupling as a depression biomarker in epilepsy.

Depression is prevalent in epilepsy patients and their intracranial brain activity recordings can be used to determine the types of brain activity that are associated with comorbid depression. We performed case-control comparison of spectral power and phase amplitude coupling (PAC) in 34 invasively monitored drug resistant epilepsy patients' brain recordings. The values of spectral power and PAC for one-minute segments out of every hour in a patient's study were correlated with pre-operative assessment of depressive symptoms by Beck Depression Inventory-II (BDI). We identified an elevated PAC signal (theta-alpha-beta phase (5-25 Hz)/gamma frequency (80-100 Hz) band) that is present in high BDI scores but not low BDI scores adult epilepsy patients in brain regions implicated in primary depression, including anterior cingulate cortex, amygdala and orbitofrontal cortex. Our results showed the application of PAC as a network-specific, electrophysiologic biomarker candidate for comorbid depression and its potential as treatment target for neuromodulation.