A Fusion Algorithm Based on a Constant Velocity Model for Improving the Measurement of Saccade Parameters with Electrooculography.

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human-computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods.

C. difficile intoxicates neurons and pericytes to drive neurogenic inflammation.

Clostridioides difficile infection (CDI) is a major cause of healthcare-associated gastrointestinal infections1,2. The exaggerated colonic inflammation caused by C. difficile toxins such as toxin B (TcdB) damages tissues and promotes C. difficile colonization3-6, but how TcdB causes inflammation is unclear. Here we report that TcdB induces neurogenic inflammation by targeting gut-innervating afferent neurons and pericytes through receptors, including the Frizzled receptors (FZD1, FZD2 and FZD7) in neurons and chondroitin sulfate proteoglycan 4 (CSPG4) in pericytes. TcdB stimulates the secretion of the neuropeptides substance P (SP) and calcitonin gene-related peptide (CGRP) from neurons and pro-inflammatory cytokines from pericytes. Targeted delivery of the TcdB enzymatic domain, through fusion with a detoxified diphtheria toxin, into peptidergic sensory neurons that express exogeneous diphtheria toxin receptor (an approach we term toxogenetics) is sufficient to induce neurogenic inflammation and recapitulates major colonic histopathology associated with CDI. Conversely, mice lacking SP, CGRP or the SP receptor (neurokinin 1 receptor) show reduced pathology in both models of caecal TcdB injection and CDI. Blocking SP or CGRP signalling reduces tissue damage and C. difficile burden in mice infected with a standard C. difficile strain or with hypervirulent strains expressing the TcdB2 variant. Thus, targeting neurogenic inflammation provides a host-oriented therapeutic approach for treating CDI.

mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning.

A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.