Background: Sleep staging is a fundamental component in the diagnosis of sleep disorders and the management of sleep health. Traditionally, this analysis is conducted in clinical settings and involves a time-consuming scoring procedure. Recent data-driven algorithms for sleep staging, using the photoplethysmogram (PPG) time series, have shown high performance on local test sets but lower performance on external datasets due to data drift. Methods: This study aimed to develop a generalizable deep learning model for the task of four class (wake, light, deep, and rapid eye movement (REM)) sleep staging from raw PPG physiological time-series. Six sleep datasets, totaling 2,574 patients recordings, were used. In order to create a more generalizable representation, we developed and evaluated a deep learning model called SleepPPG-Net2, which employs a multi-source domain training approach.SleepPPG-Net2 was benchmarked against two state-of-the-art models. Results: SleepPPG-Net2 showed consistently higher performance over benchmark approaches, with generalization performance (Cohen's kappa) improving by up to 19%. Performance disparities were observed in relation to age, sex, and sleep apnea severity. Conclusion: SleepPPG-Net2 sets a new standard for staging sleep from raw PPG time-series.

The blood protein Von Willebrand factor (VWF) is critical in facilitating arterial thrombosis. At pathologically high shear rates, the protein unfolds and binds to the arterial wall, enabling the rapid deposition of platelets from the blood. We present a novel continuum model for VWF dynamics in flow based on a modified viscoelastic fluid model that incorporates a single constitutive relation to describe the propensity of VWF to unfold as a function of the scalar shear rate. Using experimental data of VWF unfolding in pure shear flow, we fix the parameters for VWF’s unfolding propensity and the maximum VWF length, so that the protein is half unfolded at a shear rate of approximately \(5000\,\text {s}^{-1}\). We then use the theoretical model to predict VWF’s behaviour in two complex flows where experimental data are challenging to obtain: pure elongational flow and stenotic arterial flow. In pure elongational flow, our model predicts that VWF is 50% unfolded at approximately \(2000\,\text {s}^{-1}\), matching the established hypothesis that VWF unfolds at lower shear rates in elongational flow than in shear flow. We demonstrate the sensitivity of this elongational flow prediction to the value of maximum VWF length used in the model, which varies significantly across experimental studies, predicting that VWF can unfold between \(2000\text { and }3200\,\text {s}^{-1}\) depending on the selected value. Finally, we examine VWF dynamics in a range of idealised arterial stenoses, predicting the relative extension of VWF in elongational flow structures in the centre of the artery compared to high shear regions near the arterial walls.

Objective. Photoplethysmography is a non-invasive optical technique that measures changes in blood volume within tissues. It is commonly and being increasingly used for a variety of research and clinical applications to assess vascular dynamics and physiological parameters. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and limited open tools exist for continuous photoplethysmogram (PPG) analysis. Consequently, the primary objective of this research was to identify, standardize, implement and validate key digital PPG biomarkers. Approach. This work describes the creation of a standard Python toolbox, denoted pyPPG, for long-term continuous PPG time-series analysis and demonstrates the detection and computation of a high number of fiducial points and digital biomarkers using a standard fingerbased transmission pulse oximeter. Main results. The improved PPG peak detector had an F1-score of 88.19% for the state-of-the-art benchmark when evaluated on 2054 adult polysomnography recordings totaling over 91 million reference beats. The algorithm outperformed the open-source original Matlab implementation by ∼5% when benchmarked on a subset of 100 randomly selected MESA recordings. More than 3000 fiducial points were manually annotated by two annotators in order to validate the fiducial points detector. The detector consistently demonstrated high performance, with a mean absolute error of less than 10 ms for all fiducial points. Significance. Based on these fiducial points, pyPPG engineered a set of 74 PPG biomarkers. Studying PPG time-series variability using pyPPG can enhance our understanding of the manifestations and etiology of diseases. This toolbox can also be used for biomarker engineering in training data-driven models. pyPPG is available on https://physiozoo.com/.

Photoplethysmography is an optical sensing technique widely used for health and fitness monitoring in clinical and consumer devices (Charlton et al 2022b), such as smartwatches and pulse oximeters. Photoplethysmography was developed in the 1930s (Allen 2007), and its potential value for assessing cardiovascular health was recognised in the 1940s (Dillon and Hertzman 1941). It was not until the 1970s that photoplethysmography became widely used as the sensing technology in pulse oximeters (Aoyagi 2003). Photoplethysmography-based wearable devices entered the consumer market in the 2010s (Charlton and Marozas 2022), and are now used by millions of people for unobtrusive health monitoring (Natarajan et al 2020).

Effective surgical planning for breast cancer hinges on accurately predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Diffusion-weighted MRI (DWI) and machine learning offer a non-invasive approach for early pCR assessment. However, most machine-learning models require manual tumor segmentation, a cumbersome and error-prone task. We propose a deep learning model employing "Size-Adaptive Lesion Weighting" for automatic DWI tumor segmentation to enhance pCR prediction accuracy. Despite histopathological changes during NAC complicating DWI image segmentation, our model demonstrates robust performance. Utilizing the BMMR2 challenge dataset, it matches human experts in pCR prediction pre-NAC with an area under the curve (AUC) of 0.76 vs. 0.796, and surpasses standard automated methods mid-NAC, with an AUC of 0.729 vs. 0.654 and 0.576. Our approach represents a significant advancement in automating breast cancer treatment planning, enabling more reliable pCR predictions without manual segmentation.

Nanoparticles (NP) play a crucial role in nanomedicine, serving as carriers for localized therapeutics to allow for precise drug delivery to specific disease sites and conditions. When injected systemically, NP can directly interact with various blood cell types, most critically with circulating platelets. Hence, the potential activation/inhibition of platelets following NP exposure must be evaluated a priori due to possible debilitating outcomes. In recent years, various studies have helped resolve the physicochemical parameters that influence platelet-NP interactions, and either emphasize nanoparticles' therapeutic role such as to augment hemostasis or to inhibit thrombus formation, or conversely map their potential undesired side effects upon injection. In the present review, we discuss some of the main effects of several key NP types including polymeric, ceramic, silica, dendrimers and metallic NPs on platelets, with a focus on the physicochemical parameters that can dictate these effects and modulate the therapeutic potential of the NP. Despite the scientific and clinical significance of understanding Platelet-NP interactions, there is a significant knowledge gap in the field and a critical need for further investigation. Moreover, improved guidelines and research methodologies need to be developed and implemented. Our outlook includes the use of biomimetic in vitro models to investigate these complex interactions under both healthy physiological and disease conditions.

Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head motion during fMRI scans, occurring between shots of slice acquisition, can result in distortion, biased analyses, and increased costs due to the need for scan repetitions. Therefore, retrospective slice-level motion correction through slice-to-volume registration (SVR) is crucial. Previous studies have utilized deep learning (DL) based models to address the SVR task; however, they overlooked the uncertainty stemming from the input stack of slices and did not assign weighting or scoring to each slice. In this work, we introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume, incorporating a self-attention mechanism to enhance robustness against input data variations and uncertainties. It utilizes independent slice and volume encoders and a self-attention module to assign pixel-wise scores for each slice. We conducted evaluation experiments on 200 images involving synthetic rigid motion generated from 27 subjects belonging to the test set, from the publicly available Healthy Brain Network (HBN) dataset. Our experimental results demonstrate that our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods (Euclidean distance of $0.93$ [mm] vs. $1.86$ [mm]). Furthermore, our approach exhibits significantly faster registration speed compared to conventional iterative methods ($0.096$ sec. vs. $1.17$ sec.). Our end-to-end SVR model facilitates real-time head motion tracking during fMRI acquisition, ensuring reliability and robustness against uncertainties in inputs. source code, which includes the training and evaluations, will be available soon.

In recent years, the structural analysis of tissue elements has gained significant importance in biomedical research. Advancements in imaging technologies have created a pressing need to quantify tissue and cell properties accurately. This paper introduces a MATLAB-based analytical tool designed to measure a spectrum of properties from 2D images of tissues and cells. Our software efficiently computes parameters such as eccentricity, orientation, density, co-localization, size, and perimeter. The algorithm9s precision in evaluating these characteristics has broad implications for enhancing the understanding of various biological processes and diseases. The code9s flexibility allows for application across different tissue types and experimental conditions, providing researchers with a robust method for quantitative analysis. This advancement in computational image analysis represents a pivotal step towards more detailed and objective assessment in tissue engineering and cellular biology.

Ventricular tachycardia (VT) is a dangerous cardiac arrhythmia that can lead to sudden cardiac death. Early detection and management of VT is thus of high clinical importance. We hypothesize that it is possible to identify patients with VT during sinus rhythm leveraging continuous 24h Holter electrocardiogram and artificial intelligence.

We analyzed a retrospective Holter dataset from the Rambam Health Care Campus, Haifa, Israel that included 1,773 Holter recordings from 1,570 non-VT patients and 52 recordings from 49 VT patients. Morphological and heart rate variability features were engineered from the raw electrocardiogram signal and fed, together with demographical features, to a data-driven model for the task of classifying a patient as VT or non-VT.

The model obtained an area under the receiving operative curve of 0.76±0.07. Feature importance suggested that the proportion of premature ventricular beats and beat-to-beat interval variability were discriminative of VT while demographic features were not.

This original study demonstrates the feasibility of VT identification from sinus rhythm in Holter.

In vitro models of the pancreas can aid in developing therapies for pancreatic diseases. Nonetheless, most pancreatic tissue engineering is limited to insulin-secreting β-cells or pancreatic adenocarcinoma models. Combining all essential tissue components, including exocrine, endocrine, and blood vasculature, is crucial to recapitulate native tissue organization. In this study, extrusion-based 3D bioprinting to create pancreatic tissue constructs containing both endocrine and exocrine compartments is exploited. Mouse pluripotent stem cell-derived pancreatic progenitors, pancreatic endothelial cells, and mesenchymal stem cells are bioprinted. During postprinting cultivation, the cells differentiated into exocrine and endocrine lineages, resulting in vascularized pancreatic tissue-like constructs with multiple cell types. However, the bioprinted constructs contracted significantly postprinting, hindering control of cell positioning and shape preservation. Therefore, 2 strategies to reduce the contraction and deformation of the bioprinted constructs are developed. These bioprinting techniques and biomaterial combinations allow us to investigate the influence of construct design and cellular composition on pancreatic cell fate. The results reveal that increased construct stiffness and endothelial component presence significantly promoted endocrine while suppressing exocrine differentiation. Overall, a novel strategy for pancreatic tissue engineering that advances and holds promise for pancreas disease and development modeling, as well as pharmaceutical testing is demonstrated.