Amperometry approach curve profiling to understand the regulatory mechanisms governing the concentration of intestinal extracellular serotonin.

Enterochromaffin (EC) cells located within the intestinal mucosal epithelium release serotonin (5-HT) to regulate motility tones, barrier function and the immune system. Electroanalytical methodologies have been able to monitor steady state basal extracellular 5-HT levels but are unable to provide insight into how these levels are influenced by key regulatory processes such as release and uptake. We established a new measurement approach, amperometry approach curve profiling, which monitors the extracellular 5-HT level at different electrode-tissue (E-T) distances. Analysis of the current profile can provide information on contributions of regulatory components on the observed extracellular 5-HT level. Measurements were conducted from ex vivo murine ileum and colon using a boron-doped diamond (BDD) microelectrode. Amperometry approach curve profiling coupled with classical pharmacology demonstrated that extracellular 5-HT levels were significantly lower in the colon when compared to the ileum. This difference was due to a greater degree of activity of the 5-HT transporter (SERT) and a reduced amount of 5-HT released from colonic EC cells. The presence of an inhibitory 5-HT4 autoreceptor was observed in the colon, where a 40% increase in extracellular 5-HT was the half maximal inhibitory concentration for activation of the autoreceptor. This novel electroanalytical approach allows estimates of release and re-uptake and their contribution to 5-HT extracellular concentration from intestinal tissue be obtained from a single series of measurements.

Train vs. Play: Evaluating the Effects of Gamified and Non-Gamified Wheelchair Skills Training Using Virtual Reality.

This study compares the influence of a gamified and a non-gamified virtual reality (VR) environment on wheelchair skills training. In specific, the study explores the integration of gamification elements and their influence on wheelchair driving performance in VR-based training. Twenty-two non-disabled participants volunteered for the study, of whom eleven undertook the gamified VR training, and eleven engaged in the non-gamified VR training. To measure the efficacy of the VR-based wheelchair skills training, we captured the heart rate (HR), number of joystick movements, completion time, and number of collisions. In addition, an adapted version of the Wheelchair Skills Training Program Questionnaire (WSTP-Q), the Igroup Presence Questionnaire (IPQ), and the Simulator Sickness Questionnaire (SSQ) questionnaires were administered after the VR training. The results showed no differences in wheelchair driving performance, the level of involvement, or the ratings of presence between the two environments. In contrast, the perceived cybersickness was statistically higher for the group of participants who trained in the non-gamified VR environment. Remarkably, heightened cybersickness symptoms aligned with increased HR, suggesting physiological connections. As such, while direct gamification effects on the efficacy of VR-based wheelchair skills training were not statistically significant, its potential to amplify user engagement and reduce cybersickness is evident.

Effect of Gait Speed on Trajectory Prediction Using Deep Learning Models for Exoskeleton Applications.

Gait speed is an important biomechanical determinant of gait patterns, with joint kinematics being influenced by it. This study aims to explore the effectiveness of fully connected neural networks (FCNNs), with a potential application for exoskeleton control, in predicting gait trajectories at varying speeds (specifically, hip, knee, and ankle angles in the sagittal plane for both limbs). This study is based on a dataset from 22 healthy adults walking at 28 different speeds ranging from 0.5 to 1.85 m/s. Four FCNNs (a generalised-speed model, a low-speed model, a high-speed model, and a low-high-speed model) are evaluated to assess their predictive performance on gait speeds included in the training speed range and on speeds that have been excluded from it. The evaluation involves short-term (one-step-ahead) predictions and long-term (200-time-step) recursive predictions. The results show that the performance of the low- and high-speed models, measured using the mean absolute error (MAE), decreased by approximately 43.7% to 90.7% when tested on the excluded speeds. Meanwhile, when tested on the excluded medium speeds, the performance of the low-high-speed model improved by 2.8% for short-term predictions and 9.8% for long-term predictions. These findings suggest that FCNNs are capable of interpolating to speeds within the maximum and minimum training speed ranges, even if not explicitly trained on those speeds. However, their predictive performance decreases for gaits at speeds beyond or below the maximum and minimum training speed ranges.

Risk of cardioembolic ischemic events and relation to atrial fibrillation/flutter in patients with arrhythmogenic cardiomyopathy during a long-term follow-up.

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is an inherited heart disease characterized by fibro-fatty replacement of myocardium. Limited data is available concerning cardioembolic stroke. This study sought to determine the occurrence of cardioembolic ischemic events (CIEs) in ACM patients and to identify clinical and imaging predictors of CIEs. METHODS: Every consecutive ACM patient was enrolled. ECG, Holter monitoring or implantable cardiac devices were used to detect atrial arrhythmias (AAs). CIEs were defined according to TOAST classification. RESULTS: In our cohort of 111 patients, CIEs were observed in eleven (10%) over a 12.9-year median follow-up, with an incidence of 7.9 event/1000 patient-year (HR 4.12 compared to general population). Mean age at the event was 42 ± 9 years. Female sex (p = 0.03), T-wave inversion (p = 0.03), RVOT dilatation (p = 0.006) and lower LVEF (p = 0.006) were associated with CIEs. Among patients with AAs (23/111, 20.7%), 5 (21.7%) experienced CIEs. CHA2DS2-VASc did not prove useful to define patients at higher risk of CIEs (p = 0.098). 60% of stroke suffering patients had a pre-event score between 0 and 1 (if female). CONCLUSIONS: In ACM patients, CIEs are much more common than in general population and present a high burden at younger age. AAs relate to less than half of these events. In AAs patients, CHA2DS2-VASc is not useful to stratify those requiring oral anticoagulation. As a hypothesis-generating study, our research proposes the role of atrial myopathy, irrespective of AAs, as a pivotal factor in thrombogenesis risk, pointing out a definite unmet need in ACM therapeutic algorithm.

Echocardiographic Parameters to Predict Malignant Events in Arrhythmic Mitral Valve Prolapse Population.

Bileaflet Mitral Valve Prolapse (bMVP) has been linked to major arrhythmic events and sudden cardiac death (SCD). Consistent predictors in this field are still lacking. Echocardiography is the best tool for the analysis of the prolapse and its impact on the ventricular mechanics. The aim of this study was to find new echocardiographic predictors of malignant events within an arrhythmic MVP population. We evaluated 22 patients with arrhythmic bMVP with a transthoracic echocardiogram focused on mitral valve anatomy and ventricular contraction. Six of them had major arrhythmic events that required ICD implantation (ICD-MVP group), while sixteen presented with a high arrhythmic burden without major events (A-MVP group). The best predictors of malignant events were the Anterior Mitral Leaflet (AML) greater length and greater Mechanical Dispersion (MD) of basal and mid-ventricular segments, while other significant predictors were the larger mitral valve annulus (MVA) indexed area, lower MVA anteroposterior diameter/AML length ratio, higher inferolateral basal segment S3 velocity.

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated orthotic devices (e.g., Powered Ankle Foot Orthosis-PAFO). Several studies have forecasted healthy gait trajectories, but, to the best of our knowledge, none have forecasted gait trajectories of children with pathological gait yet. These exhibit higher inter- and intra-subject variability compared to typically developing gait of healthy subjects. Pathological trajectories represent the typical gait patterns that rehabilitative exoskeletons and actuated orthoses would target. In this study, we implemented two deep learning models, a Long-Term Short Memory (LSTM) and a Convolutional Neural Network (CNN), to forecast hip, knee, and ankle trajectories in terms of corresponding Euler angles in the pitch, roll, and yaw form for children with neurological disorders, up to 200 ms in the future. The deep learning models implemented in our study are trained on data (available online) from children with neurological disorders collected by Gillette Children's Speciality Healthcare over the years 1994-2017. The children's ages range from 4 to 19 years old and the majority of them had cerebral palsy (73%), while the rest were a combination of neurological, developmental, orthopaedic, and genetic disorders (27%). Data were recorded with a motion capture system (VICON) with a sampling frequency of 120 Hz while walking for 15 m. We investigated a total of 35 combinations of input and output time-frames, with window sizes for input vectors ranging from 50-1000 ms, and output vectors from 8.33-200 ms. Results show that LSTMs outperform CNNs, and the gap in performance becomes greater the larger the input and output window sizes are. The maximum difference between the Mean Absolute Errors (MAEs) of the CNN and LSTM networks was 0.91 degrees. Results also show that the input size has no significant influence on mean prediction errors when the output window is 50 ms or smaller. For output window sizes greater than 50 ms, the larger the input window, the lower the error. Overall, we obtained MAEs ranging from 0.095-2.531 degrees for the LSTM network, and from 0.129-2.840 degrees for the CNN. This study establishes the feasibility of forecasting pathological gait trajectories of children which could be integrated with exoskeleton control systems and experimentally explores the characteristics of such intelligent systems under varying input and output window time-frames.

A crowdsourcing semi-automatic image segmentation platform for cell biology.

State-of-the-art computer-vision algorithms rely on big and accurately annotated data, which are expensive, laborious and time-consuming to generate. This task is even more challenging when it comes to microbiological images, because they require specialized expertise for accurate annotation. Previous studies show that crowdsourcing and assistive-annotation tools are two potential solutions to address this challenge. In this work, we have developed a web-based platform to enable crowdsourcing annotation of image data; the platform is powered by a semi-automated assistive tool to support non-expert annotators to improve the annotation efficiency. The behavior of annotators with and without the assistive tool is analyzed, using biological images of different complexity. More specifically, non-experts have been asked to use the platform to annotate microbiological images of gut parasites, which are compared with annotations by experts. A quantitative evaluation is carried out on the results, confirming that the assistive tools can noticeably decrease the non-expert annotation's cost (time, click, interaction, etc.) while preserving or even improving the annotation's quality. The annotation quality of non-experts has been investigated using IoU (intersection over union), precision and recall; based on this analysis we propose some ideas on how to better design similar crowdsourcing and assistive platforms.

Towards image-based cancer cell lines authentication using deep neural networks.

Although short tandem repeat (STR) analysis is available as a reliable method for the determination of the genetic origin of cell lines, the occurrence of misauthenticated cell lines remains an important issue. Reasons include the cost, effort and time associated with STR analysis. Moreover, there are currently no methods for the discrimination between isogenic cell lines (cell lines of the same genetic origin, e.g. different cell lines derived from the same organism, clonal sublines, sublines adapted to grow under certain conditions). Hence, additional complementary, ideally low-cost and low-effort methods are required that enable (1) the monitoring of cell line identity as part of the daily laboratory routine and 2) the authentication of isogenic cell lines. In this research, we automate the process of cell line identification by image-based analysis using deep convolutional neural networks. Two different convolutional neural networks models (MobileNet and InceptionResNet V2) were trained to automatically identify four parental cancer cell line (COLO 704, EFO-21, EFO-27 and UKF-NB-3) and their sublines adapted to the anti-cancer drugs cisplatin (COLO-704rCDDP1000, EFO-21rCDDP2000, EFO-27rCDDP2000) or oxaliplatin (UKF-NB-3rOXALI2000), hence resulting in an eight-class problem. Our best performing model, InceptionResNet V2, achieved an average of 0.91 F1-score on tenfold cross validation with an average area under the curve (AUC) of 0.95, for the 8-class problem. Our best model also achieved an average F1-score of 0.94 and 0.96 on the authentication through a classification process of the four parental cell lines and the respective drug-adapted cells, respectively, on a four-class problem separately. These findings provide the basis for further development of the application of deep learning for the automation of cell line authentication into a readily available easy-to-use methodology that enables routine monitoring of the identity of cell lines including isogenic cell lines. It should be noted that, this is just a proof of principal that, images can also be used as a method for authentication of cancer cell lines and not a replacement for the STR method.

Computer-generated ovaries to assist follicle counting experiments.

Precise estimation of the number of follicles in ovaries is of key importance in the field of reproductive biology, both from a developmental point of view, where follicle numbers are determined at specific time points, as well as from a therapeutic perspective, determining the adverse effects of environmental toxins and cancer chemotherapeutics on the reproductive system. The two main factors affecting follicle number estimates are the sampling method and the variation in follicle numbers within animals of the same strain, due to biological variability. This study aims at assessing the effect of these two factors, when estimating ovarian follicle numbers of neonatal mice. We developed computer algorithms, which generate models of neonatal mouse ovaries (simulated ovaries), with characteristics derived from experimental measurements already available in the published literature. The simulated ovaries are used to reproduce in-silico counting experiments based on unbiased stereological techniques; the proposed approach provides the necessary number of ovaries and sampling frequency to be used in the experiments given a specific biological variability and a desirable degree of accuracy. The simulated ovary is a novel, versatile tool which can be used in the planning phase of experiments to estimate the expected number of animals and workload, ensuring appropriate statistical power of the resulting measurements. Moreover, the idea of the simulated ovary can be applied to other organs made up of large numbers of individual functional units.

Understanding changes in uptake and release of serotonin from gastrointestinal tissue using a novel electroanalytical approach.

Serotonin (5-HT) is well known to be a key neurotransmitter within the gastrointestinal (GI) tract, where it is responsible for influencing motility. Obtaining dynamic information about the neurotransmission process (specifically the release and reuptake of 5-HT) requires the development of new approaches to measure the extracellular 5-HT concentration profile. In this work constant-potential amperometry has been utilised at +650 mV vs. Ag|AgCl to measure in vitro the overflow of 5-HT. Steady-state levels of 5-HT have been observed, due to continuous mechanical stimulation of the tissue from the experimental protocol. Measurements are conducted at varying tissue-electrode distances in the range of 5 to 1100 microm. The difference in the current from the bulk media and that from each tissue-electrode distance is obtained, and the natural log of this current is plotted versus the tissue-electrode distance. The linear fit to the log of the current is derived, and its intercept, I(0), with the vertical axis and its slope are calculated. The reciprocal of the slope, indicated as slope(-1), is used as a marker of reuptake. The ratio between intercept, I(0), and the reciprocal of the slope, I(0)/slope(-1), is a measure of the flux at the tissue surface and it can be used as a marker for the 5-HT release rate. Current measurements for ileum and colon tissue indicated a significantly higher reuptake rate in the colon, showed by a lower slope(-1). In addition, the ratio, I(0)/slope(-1), indicated that the colon has a higher 5-HT flux compared to the ileum. Following the application of the serotonin selective reuptake inhibitor (SSRI), fluoxetine, both tissues showed a higher value of slope(-1), as the reuptake process is blocked preventing clearance of 5-HT. No differences were observed in the ratio, I(0)/slope(-1), in the ileum, but a decrease was observed in the colon. These results indicate that ileum and colon are characterised by different reuptake and release processes. The new approach we propose provides pivotal information on the variations in the signalling mechanism, where steady state levels are observed and can be a vital tool to study differences between normal and diseased tissue and also the efficacy of pharmacological agents.

Theoretical modelling to understand the neurotransmission mechanism in the gastrointestinal tract.

In this paper we apply a novel experimental and theoretical method to study the neurotransmitter signalling process. This method allows the understanding of changes in uptake between different tissue types from the gastrointestinal tract. The reaction-diffusion model we used has shown that by changing the uptake rate, the slope of the response changed when the levels of 5-HT released were held constant. Experimental data from the ileum and colon obtained at one distance from the tissue shows no significant difference in the release profile, however measurements at multiple sites show different current slopes for the two tissues. The response based upon the theoretical data indicates that colon has a higher uptake rate than the ileum. These results show that the combination of a theoretical and experimental approach to study biological processes can provide a mean of gaining further inside into the mechanisms involved and can have important clinical applications.

Thermal fluctuations of red blood cell membrane via a constant-area particle-dynamics model.

We describe a model of the mechanical properties of the cell plasma membrane using a finite-temperature particle-dynamics simulation of the whole cell, in which a two-dimensional network of virtual particles embedded in a three-dimensional closed surface represents the membrane. The particles interact via harmonic potential and dihedral angle potential and are subject to a constant area constraint. The evolution of the positions of the particles yields the equilibrium state of the membrane and allows determination of the membrane thermal fluctuations and the elastic moduli. We show that time-averaging of the cell-model configurations allows quantitative comparison with experimental data on membrane fluctuations and elastic moduli of the red blood cell.

Quantumlike short-time behavior of a classical crystal.

We have performed a molecular-dynamics simulation of a face-centered-cubic Lennard-Jones crystal, and studied its relaxation toward equilibrium and its microcanonical equilibrium dynamics through the computation of the normal modes. At low temperature, the weak interaction among normal modes yields a very slow relaxation of the fluctuation of the kinetic energy; this requires a new formulation of the measure of the microcanonical specific heat at constant volume. This specific heat turns out to depend on the time of observation; for times of the order of 20 ps, its values are much nearer to the quantum ones than to the value 3R predicted by the classical Dulong and Petit law. For longer observation times, the classical specific heat progressively approaches 3R over most of the temperature range of the solid crystal, with the exception of the lowest temperature range, where it still drops to values close to zero. The time dependence of the specific heat of the crystal is similar to the behavior found in a supercooled liquid near the glass transition.