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Sleep posture and movements offer insights into neurophysiological health and correlate with overall well-being and quality of life. Clinical practices utilise polysomnography for sleep assessment, which is intrusive, performed in unfamiliar environments, and requires trained personnel. While sensor technologies such as actigraphy are less invasive alternatives, concerns about their reliability and precision in clinical practice persist. Moreover, the field lacks a universally accepted algorithm, with methods ranging from raw signal thresholding to data-intensive classification models that may be unfamiliar to medical staff. This paper proposes a comprehensive framework for objectively detecting sleep posture changes and temporally segmenting postural inactivity using clinically relevant joint kinematics, measured by a custom-made wearable sensor. The framework was evaluated on wrist kinematic data from five healthy participants during simulated sleep. Intuitive three-dimensional visualisations of kinematic time series were achieved through dimension reduction-based preprocessing, providing an out-of-the-box framework explainability that may be useful for clinical monitoring and diagnosis. The proposed framework achieved up to 99.2% F1-score and 0.96 Pearson's correlation coefficient for posture detection and inactivity segmentation respectively. This work paves the way for reliable home-based sleep movement analysis, serving patient-centred longitudinal care. [ABSTRACT FROM AUTHOR]

Sleep posture is linked to several health conditions such as nocturnal cramps and more serious musculoskeletal issues. However, in-clinic sleep assessments are often limited to vital signs (e.g. brain waves). Wearable sensors with embedded inertial measurement units have been used for sleep posture classification; nonetheless, previous works consider only few (commonly four) postures, which are inadequate for advanced clinical assessments. Moreover, posture learning algorithms typically require longitudinal data collection to function reliably, and often operate on raw inertial sensor readings unfamiliar to clinicians. This paper proposes a new framework for sleep posture classification based on a minimal set of joint angle measurements. The proposed framework is validated on a rich set of twelve postures in two experimental pipelines: computer animation to obtain synthetic postural data, and human participant pilot study using custom-made miniature wearable sensors. Through fusing raw geo-inertial sensor measurements to compute a filtered estimate of relative segment orientations across the wrist and ankle joints, the body posture was characterised in a way comprehensible to medical experts. The proposed sleep posture learning framework offers plug-and-play posture classification by capitalising on a novel kinematic data augmentation method that requires only one training example per posture. Additionally, a new metric together with data visualisations were employed to extract meaningful insights from the postures dataset, demonstrate the added value of the data augmentation method, and explain the classification performance. The proposed framework attained promising overall accuracy as high as 100% on synthetic data and 92.7% on real data, on par with state of the art data-hungry algorithms available in the literature. • Used computer graphics tools to simulate a virtual sleep experiment. • Classification of sleep postures both through in silico and in vivo experiments. • Learning of twelve human sleep postures with one observation per posture. • Inertial sensor fusion enabled posture classification using quadruple joint angles. • Metrics and visualisations enabled quantitative and qualitative posture analysis. [ABSTRACT FROM AUTHOR]

Background: BTC is an aggressive disease exacerbated by inflammation and immune suppression. Expansion of immunosuppressive cells occurs in biliary tract cancer (BTC), yet the role of BTC-derived cytokines in this process is unclear.Methods: Activated signalling pathways and cytokine production were evaluated in a panel of human BTC cell lines. Human peripheral blood mononuclear cells (PBMCs) were cultured with BTC supernatants, with and without cytokine neutralising antibodies, and analysed by flow cytometry or immunoblot. A human BTC tissue microarray (TMA, n = 69) was stained for IL-6, GM-CSF, and CD33+S100a9+ cells and correlated with clinical outcomes.Results: Immunomodulatory factors (IL-6, GM-CSF, MCP-1) were present in BTC supernatants. BTC supernatants expanded CD33dimCD11b+HLA-DRlow/- myeloid-derived suppressor cells (MDSCs) from human PBMCs. Neutralisation of IL-6 and GM-CSF in BTC supernatants inhibited activation of STAT3/5, respectively, in PBMCs, with heterogeneous effects on MDSC expansion in vitro. Staining of a BTC TMA revealed a positive correlation between IL-6 and GM-CSF, with each cytokine and more CD33+S100a9+ cells. Increased CD33+S100a9+ staining positively correlated with higher tumour grade, differentiation and the presence of satellite lesions.Conclusion: BTC-derived factors promote suppressive myeloid cell expansion, and higher numbers of CD33+S100a9+ cells in resectable BTC tumours correlates with more aggressive disease. [ABSTRACT FROM AUTHOR]

Bioactive phytochemicals from natural products, such as black raspberries (BRB; Rubus occidentalis), have direct anticancer properties on malignant cells in culture and in xenograft models. BRB components inhibit cancer progression in more complex rodent carcinogenesis models. Although mechanistic targets for BRB phytochemicals in cancer cells are beginning to emerge, the potential role in modulating host immune processes impacting cancer have not been systematically examined. We hypothesized that BRB contain compounds capable of eliciting potent immunomodulatory properties that impact cellular mediators relevant to chronic inflammation and tumor progression. We studied both an ethanol extract from black raspberries (BRB-E) containing a diverse mixture of phytochemicals and two abundant phytochemical metabolites of BRB produced upon ingestion (Cyanidin-3-Rutinoside, C3R; Quercitin-3-Rutinoside, Q3R). BRB-E inhibited proliferation, and viability of CD3/CD28 activated human CD4 and CD8 T lymphocytes. BRB-E also limited in vitro expansion of myeloid-derived suppressor cells (MDSC) and their suppressive capacity. Pre-treatment of immune cells with BRB-E attenuated IL-6-mediated phosphorylation of signal transducer and activator of transcription-3 (STAT3) and IL-2-induced STAT5 phosphorylation. In contrast, pre-treatment of immune cells with the C3R and Q3R metabolites inhibited MDSC expansion, IL-6-mediated STAT3 signaling, but not IL-2-induced STAT5 phosphorylation and were less potent inhibitors of T cell viability. Together these data indicate that BRB extracts and their physiologically relevant metabolites contain phytochemicals that affect immune processes relevant to carcinogenesis and immunotherapy. Furthermore, specific BRB components and their metabolites may be a source of lead compounds for drug development that exhibits targeted immunological outcomes or inhibition of specific STAT-regulated signaling pathways. [ABSTRACT FROM AUTHOR]