Miracle-dz/UniFAR-FTUs huggingface dataset:
Interoceptive sensitivity modulates heart rate and insula activity when listening to music

Interoception plays an important role in emotion processing, but the relationship between the physiological responses associated with emotional experience and interoception is unclear. In this study, we measured interoceptive sensitivity using the heartbeat discrimination task and investigated the effects of individual differences in interoceptive sensitivity on changes in heart rate and insula activity in response to music-induced emotions. We found that the heart rate increased when listening to the music pieces rated as emotionally high-touching in the high interoceptive sensitivity group only. Compared to the emotionally low-touching music, listening to the emotionally high-touching music was associated with higher insula activity. Furthermore, relative to individuals with low interoceptive sensitivity, the region of interest analysis of the insula subregions for individuals with high interoceptive sensitivity revealed significant activity in the bilateral dorsal granular insula, the right ventral dysgranular insula, and the right granular and dorsal dysgranular insula while listening to the high-touching music pieces. Our results suggest that individuals with high interoceptive sensitivity use their physical condition to assess their emotional level when listening to music. Furthermore, the insula activity may reflect the use of interoception to estimate emotions.
Efemp1 modulates elastic fiber formation and mechanics of the extrahepatic bile duct

EGF-Containing Fibulin Extracellular Matrix Protein 1 (EFEMP1, also called fibulin 3) is an extracellular matrix protein linked in a genome-wide association study to biliary atresia, a fibro-inflammatory disease of the neonatal extrahepatic bile duct. EFEMP1 is expressed in most tissues and Efemp1 null mice have decreased elastic fibers in visceral fascia; however, in contrast to other short fibulins (fibulins 4 and 5), EFEMP1 does not have a role in the development of large elastic fibers, and its overall function remains unclear. We demonstrated that EFEMP1 is expressed in the submucosa of both neonatal and adult mouse and human extrahepatic bile ducts and that, in adult Efemp1+/- mice, elastin organization into fibers is decreased. We used pressure myography, a technique developed to study the mechanics of the vasculature, to show that Efemp1+/- extrahepatic bile ducts are more compliant to luminal pressure, leading to increased circumferential stretch. We conclude that EFEMP1 has an important role in the formation of elastic fibers and mechanical properties of the extrahepatic bile duct. These data suggest that altered expression of EFEMP1 in the extrahepatic bile duct leads to an abnormal response to mechanical stress such as obstruction, potentially explaining the role of EFEMP1 in biliary atresia.
Graphsite: Ligand-binding site classification using Deep Graph Neural Network

Binding sites are concave surfaces on proteins that bind to small molecules called ligands. Types of molecules that bind to the protein determine its biological function. Meanwhile, the binding process between small molecules and the protein is also crucial to various biological functionalities. Therefore, identifying and classifying such binding sites would enormously contribute to biomedical applications such as drug repurposing. Deep learning is a modern artificial intelligence technology. It utilizes deep neural networks to handle complex tasks such as image classification and language translation. Previous work has proven the capability of deep learning models handle binding sites wherein the binding sites are represented as pixels or voxels. Graph neural networks (GNNs) are deep learning models that operate on graphs. GNNs are promising for handling binding sites related tasks - provided there is an adequate graph representation to model the binding sties. In this communication, we describe a GNN-based computational method, GraphSite, that utilizes a novel graph representation of ligand-binding sites. A state-of-the-art GNN model is trained to capture the intrinsic characteristics of these binding sites and classify them. Our model generalizes well to unseen data and achieves test accuracy of 81.28% on classifying 14 binding site classes.
Inhibiting Host Protein Deposition on Urinary Catheters Reduces Urinary Tract Infections

Microbial adhesion to medical devices is common for hospital-acquired infections, particularly for urinary catheters. If not properly treated these infections cause complications and exacerbate antimicrobial resistance. Catheter use elicits bladder inflammation, releasing host serum-proteins, including fibrinogen, into the bladder, which deposit on the urinary catheter. Enterococcus faecalis uses fibrinogen as a scaffold to bind and persist in the bladder despite antibiotic treatments. Inhibition of fibrinogen-pathogen interaction significantly reduces infection. Here, we show deposited fibrinogen is advantageous for uropathogens, suggesting that targeting catheter protein deposition may reduce colonization creating an effective intervention. Hostprotein deposition was reduced, using liquid-infused catheters, resulting in decreased colonization on catheters, in bladders, and dissemination in vivo. Furthermore, proteomics revealed a significant decrease in deposition of host-secreted proteins on liquid-infused catheter surfaces. Our findings suggest targeting microbial binding scaffolds may be an effective antibiotic-sparing intervention for use against catheter-associated urinary tract infections and other medical device infections.
Development of a mobile, cost-effective and easy to use inertial motion capture system for monitoring in rehabilitation applications

Abstract Many people are familiar with the feeling of instability, pain, or subsidence in the knee joint after a knee injury. There are many different methods for examining the knee, such as the drawer test or the Lachman test [1], before and after surgery. While these tests can be used in short term and provide useful results, motion capture systems can be used as an alternative measurement method, almost as a substitute in longer term. These include marker-based or mechanica l systems, which achieve good measurement results but are expensive and inflexible. For this reason, this paper presents a mobile, easy-to-use motion and easy expandable capture system using a low-cost IMU-based development system. The modular design of the system allows it to be adapted to each body region with simple adjustments. However, the present work focuses on applications for capturing human motion sequences and deriving three joint angles of the lower extremities to detect malposition.
Partial 3D-reconstruction of the colon from monoscopic colonoscopy videos using shape-from-motion and deep learning

Abstract For the image-based documentation of a colonoscopy procedure, a 3D-reconstuction of the hollow colon structure from endoscopic video streams is desirable. To obtain this reconstruction, 3D information about the colon has to be extracted from monocular colonoscopy image sequences. This information can be provided by estimating depth through shape-from-motion approaches, using the image information from two successive image frames and the exact knowledge of their disparity. Nevertheless, during a standard colonoscopy the spatial offset between successive frames is continuously changing. Thus, in this work deep convolutional neural networks (DCNNs) are applied in order to obtain piecewise depth maps and point clouds of the colon. These pieces can then be fused for a partial 3D reconstruction.
Pulse Arrival Time - A Sensitive Vital Parameter for the Detection of Mental Stress

Abstract Mental stress triggers positive inotropic and chronotropic effects as well as peripheral vasoconstriction. This alters the pulse arrival time (PAT), the duration between electrical excitation of the ventricles and arrival of the pulse wave in the periphery. We conducted a study to examine PAT during five rest blocks and under mental stress utilizing the Mannheim Multicomponent Stress Test. Electrocardiograms as well as finger and earlobe photoplethysmograms were recorded. PAT was calculated for over 135,000 heartbeats from 42 healthy volunteers as the time duration between the R peak in the electrocardiogram and the following pulse onset in the respective photoplethysmogram. To identify the effect of mental stress, block-wise PAT means were statistically analyzed with repeated measures ANOVA. The analyses showed significant differences between the block means for both PAT measures (p < 0.001). Post-hoc tests revealed significantly reduced PAT during the stress block compared to all rest blocks for both PAT measures (p < 0.001). We found no significant differences between the rest blocks. Our results support that PAT is a sensitive vital parameter for the detection of mental stress in healthy volunteers. This holds true for both measurement positions, the finger and the earlobe.
Sensitivity and Generalization of a Neural Network for Estimating Left Atrial Fibrotic Volume Fractions from the 12-lead ECG

Abstract Features extracted from P waves of the 12-lead electrocardiogram (ECG) have proven valuable for noninvasively estimating the left atrial fibrotic volume fraction associated with the arrhythmogenesis of atrial fibrillation. However, feature extraction in the clinical context is prone to errors and oftentimes yields unreliable results in the presence of noise. This leads to inaccurate input values provided to machine learning algorithms tailored at estimating the amount of atrial fibrosis with clinical ECGs. Another important aspect for clinical translation is the network’s generalization ability regarding new ECGs. To quantify a network’s sensitivity to inaccurately extracted P wave features, we added Gaussian noise to the features extracted from 540,000 simulated ECGs consisting of P wave duration, dispersion, terminal force in lead V1, peak-to-peak amplitudes, and additionally thoracic and atrial volumes. For assessing generalization, we evaluated the network performance for train-validation-test splits divided such that ECGs simulated with the same atria or torso geometry only belonged to either the training and validation or the test set. The root mean squared error (RMSE) of the network increased the most in case of noisy torso volumes and P wave durations. Large generalization errors with a RMSE difference between training and test set of more than 2% fibrotic volume fraction only occurred if very high or low atria and torso volumes were left out during training. Our results suggest that P wave duration and thoracic volume are features that have to be measured accurately if employed for estimating atrial fibrosis with a neural network. Furthermore, our method is capable of generalizing well to ECGs simulated with anatomical models excluded during training and thus meets an important requirement for clinical translation.
Comparison of Characteristics of Poly(Nisopropylacrylamide) in Bulk Hydrogel and Ball-milled Microgel Forms

Abstract Poly(N-isopropylacrylamide) hydrogels are a popular temperature sensitive biomaterial. Bulk hydrogels can be quickly and easily processed into microgels using a ball mill. However, there is no information on whether the mechanical milling process affects critical PNIPAM characteristics. In this work, we compare swelling and thermo-responsive properties for a series of PNIPAM gels in bulk and microgel forms.
An Ultra-Fast Digitally Reconstructed Radiograph (DRR) Implementation of the Siddon-Jacobs Algorithm using Parallel Computing: Runtime Improvement of an Intensity-Based 2D/3D Registration

Abstract A key component of an intensity-based 2D/3D registration is the digitally reconstructed radiograph (DRR) module, which creates 2D projections from pre-operative 3D data, e.g., CT and MRI scans. On average, an intensity-based 2D/3D registration requires ten iterations and the rendering of twelve DRR images per iteration. In a typical DRR implementation, the rendering time is about two seconds, and the registration runtime is four minutes. We present an implementation of the Siddon-Jacobs algorithm that uses a novel pixel-step approach to determine the pixel location of the rendering plane. In addition, we calculate the intensity of each pixel in the rendering plane using a parallel computing approach. The DRR rendering time is reduced to 10ms on average so that the registration runtime can be achieved in an average of 4.8 seconds.
Analysis of expansion within a pressure inflated section of an urethral stricture model

Abstract The Urethra is a long tubular structure in the genitourinary tract and serves important functions. Researchers have experimented with some approaches to model the urethra and to analyse its biomechanical properties. However, experiments to model the in-vivo behaviour of urethra with strictures is not thoroughly explored. To analyse the in-vivo urethral properties and specifically for supporting treatment of strictures, a new inflatable sensor-actuator system is being developed. The capabilities of this sensor shall be evaluated in simulations which require appropriate modelling of the human male urethra with strictures. This forms a part of the identification procedure for a variety of urethra conditions and geometries, which in turn forms a basis for inverse modelling. As an initial simplified approach, an axisymmetric Finite Element model was generated that resembled the urethra incorporating a stricture region. An ideal actuator with sensor elements exerting a pressure on inner wall of this urethra was simulated. Three circumference measurement zones within the sensor height (top surface, centre and bottom surface) were implemented. The resulting pressure-extension (circumferential) responses were determined at these measurement zones. The sensor was placed at different lengths within this urethral tube and inflated and the pressure-extension responses were noted. It was found that depending on the position of the sensor-actuator, the extension of tissue can vary. The possible factors for this variation were the finite length of the actuator as well as the influence of tissue properties around the measurement zones. This is important information for the interpretation of sensor data to be gained by the current development. It was possible to generate datasets based on an ideal sensor model, that proved helpful in the evaluation of biomechanical tissue properties in healthy and stricture conditions. This indicates simulations are a versatile and prospective way to test new sensors prior to real experiments.
Effect of microscale protrusions on local fluid flow and mass transport in the presence of forced convection

Three-dimensional creeping flow around single, axisymmetric protrusions is studied numerically using the boundary-integral technique. Emphasis is placed upon cylindrical protrusions on plane walls for various height-to-radius (h-to-a) aspect ratios, but cones and sections of spheres protruding from plane walls are also briefly examined. The presented items include shear-stress distributions, shear-stress contours, extents of the fluid-flow disturbance, total forces and torques on the cylinders, streamlines, and skin-friction lines. Also included is a discussion of flow topology around axisymmetric geometries. No flow reversal is observed for cylindrical protrusions with aspect ratios greater than 2.4 to 2.6. At higher aspect ratios, the fluid tends to be swept around cylindrical protrusions with little vertical motion. At lower aspect ratios, the strength of the recirculation increases, and the recirculation region becomes wider in the transverse direction and narrower in the flow direction. Also, the recirculation pattern begins to resemble the closed streamline patterns in two-dimensional flow over square ridges. However, unlike two-dimensional flow, closed streamline patterns are not observed. For arbitrary axisymmetric geometries, the extent of the fluid-flow disturbance can be estimated with the total force that is exerted on the protrusion. When the same force is exerted on protrusions with different aspect ratios, the protrusion with the higher aspect ratio tends to have a greater disturbance in the flow direction and a smaller disturbance in the transverse direction. The total force exerted on cylindrical protrusions with rounded corners is only slightly lower than the total force exerted on cylindrical protrusions with sharp corners.
Comparison of Deep Learning Algorithms for Semantic Segmentation of Ultrasound Thyroid Nodules

Abstract Ultrasound (US) imaging is used as a preliminary diagnostic tool for the detection, risk-stratification and classification of thyroid nodules. In order to perform the risk stratification of nodules in US images physicians first need to effectively detect the nodules. This process is affected due to the presence of inter-observer and intra-observer variability and subjectivity. Computer Aided Diagnostic tools prove to be a step in the right direction towards reducing the issue of subjectivity and observer variability. Several segmentation techniques have been proposed, from these Deep Learning techniques have yielded promising results. This work presents a comparison between four state of the art (SOTA) Deep Learning segmentation algorithms (UNet, SUMNet, ResUNet and Attention UNet). Each network was trained on the same dataset and the results are compared using performance metrics such as accuracy, dice coefficient and Intersection over Union (IoU) to determine the most effective in terms of thyroid nodule segmentation in US images. It was found that ResUNet performed the best with an accuracy, dice coefficient and IoU of 89.2%, 0.857, 0.767. The aim is to use the trained algorithm in the development of a Computer Aided Diagnostic system for the detection, riskstratification and classification of thyroid nodules using US images to reduce subjectivity and observer variability
Contributions towards Data driven Deep Learning methods to predict Steady State Fluid Flow in mechanical Heart Valves

Abstract It is commonly accepted that hemodynamic situation is related with cardiovascular diseases as well as clinical post-procedural outcome. In particular, aortic valve stenosis and insufficiency are associated with high shear flow and increased pressure loss. Furthermore, regurgitation, high shear stress and regions of stagnant blood flow are presumed to have an impact on clinical result. Therefore, flow field assessment to characterize the hemodynamic situation is necessary for device evaluation and further design optimization. In-vitro as well as in-silico fluid mechanics methods can be used to investigate the flow through prostheses. In-silico solutions are based on mathematical equitation’s which need to be solved numerically (Computational Fluid Dynamics - CFD). Fundamentally, the flow is physically described by Navier-Stokes. CFD often requires high computational cost resulting in long computation time. Techniques based on deep-learning are under research to overcome this problem. In this study, we applied a deep-learning strategy to estimate fluid flows during peak systolic steady-state blood flows through mechanical aortic valves with varying opening angles in randomly generated aortic root geometries. We used a data driven approach by running 3,500 two dimensional simulations (CFD). The simulation data serves as training data in a supervised deep learning framework based on convolutional neural networks analogous to the U-net architecture. We were able to successfully train the neural network using the supervised data driven approach. The results showing that it is feasible to use a neural network to estimate physiological flow fields in the vicinity of prosthetic heart valves (Validation error below 0.06), by only giving geometry data (Image) into the Network. The neural network generates flow field prediction in real time, which is more than 2500 times faster compared to CFD simulation. Accordingly, there is tremendous potential in the use of AIbased approaches predicting blood flows through heart valves on the basis of geometry data, especially in applications where fast fluid mechanic predictions are desired.
Classification of Bundle Branch Blocks with QRS Templates Extracted from 12-lead ECGs

Abstract Being non-invasive, cheap and widely available, the 12-lead electrocardiogram (ECG) is a standard method to assess cardiac function. Still, its reliable interpretation requires specialized knowledge and experience, rendering a second opinion valuable. We evaluated the performance of machine learning based classification of 11,705 healthy and bundle branch block 12-lead ECGs from 3 open databases. For each lead of the ECG signal, a representative QRS-complex template was extracted automatically. Principal component analysis (PCA) was applied to the concatenated, normalized and rescaled QRS signals to reduce their dimensionality. Multilayer perceptron and support-vector machine classifiers were trained using the principal components of weighted and non-weighted QRS template signals as input data. Classifiers achieved F1 scores between 0.92 and 0.96 on the test set for different input configurations. Anomaly based weighting slightly improved the performance of the classifiers. Neither class-wise PCA for feature extraction nor adding information on sex, gender and electrical heart axis to the input data yielded considerable improvement of the F1 scores. The achieved classification accuracy is similar to deep learning classifier performances and should generalize robustly to other ECG datasets. Our results suggest that this simple and well interpretable approach based on morphological signal characteristics is suitable for automatically and non-invasively identifying bundle branch block pathologies in clinical or smart electronics contexts.
Evaluation and Implementation of Biocompatible Methods for the Cross-linking of Plasma Proteins

Abstract Autologous plasma proteins can be used to fabricate patient specific cardiovascular implants but need to be cross-linked to increase their mechanical strength and reduce water solubility. Glutaraldehyde is the state-of-the-art solution but its reaction products have been shown to be cytotoxic and pro-inflammatory. In this work, it has been shown, that cross-linking of plasma proteins with biocompatible alternatives to glutaraldehyde is possible. This was achieved by identifying four candidate substances (thrombin, transglutaminase, genipin, EDC) from current literature and investigating their ability to cross-link porcine plasma proteins in vitro. The degree of crosslinking was examined using calorimetric (DSC) and spectroscopic (FTIR, Raman) methods, mapping the influence of cross-linking on the denaturation temperature and primary amino-group content of the proteins. It could be shown that thrombin, genipin and EDC are able to cross-link plasma proteins to a satisfactory degree and thus represent useful alternatives to glutaraldehyde. Transglutaminase, on the other hand, could not sufficiently cross-link the plasma proteins and was therefore ruled out as an alternative.
Vital Signs and Sensors for Post-Exertional Malaise Prevention

Post-exertional malaise (PEM) is the core symptom of Chronic Fatigue Syndrome / Myalgic Encephalomyelitis, a disease that can break out after surviving a SARS-CoV-2 infection. Multiple types of triggers lead to an extremal deterioration of the patient's overall health condition. In this paper, vital signs are compared to what extent they are sensitive to the triggers and thus can be used to prevent PEM. In addition, sensors and measurement methods are proposed, based on criteria for use in patients' daily lives. Even simple pedometers can contribute to PEM prevention. However, chest straps and wrist-worn sensors are more promising. The essential relevant vital parameters heart rate, blood pressure and respiratory rate are measured in this way. © 2021 The Author(s), published by De Gruyter.