Infection by Zika virus triggers epileptic seizures in newborn mice and leads to viral persistence in the brain and behavioral deficits in adulthood.

Extra robotic arms (XRAs) are gaining interest in neuroscience and robotics, offering potential tools for daily activities. However, this compelling opportunity poses new challenges for sensorimotor control strategies and human-machine interfaces (HMIs). A key unsolved challenge is allowing users to proficiently control XRAs without hindering their existing functions. To address this, we propose a pipeline to identify suitable HMIs given a defined task to accomplish with the XRA. Following such a scheme, we assessed a multimodal motor HMI based on gaze detection and diaphragmatic respiration in a purposely designed modular neurorobotic platform integrating virtual reality and a bilateral upper limb exoskeleton. Our results show that the proposed HMI does not interfere with speaking or visual exploration and that it can be used to control an extra virtual arm independently from the biological ones or in coordination with them. Participants showed significant improvements in performance with daily training and retention of learning, with no further improvements when artificial haptic feedback was provided. As a final proof of concept, naïve and experienced participants used a simplified version of the HMI to control a wearable XRA. Our analysis indicates how the presented HMI can be effectively used to control XRAs. The observation that experienced users achieved a success rate 22.2% higher than that of naïve users, combined with the result that naïve users showed average success rates of 74% when they first engaged with the system, endorses the viability of both the virtual reality–based testing and training and the proposed pipeline.

Patients with Parkinson’s disease (PD) manifest nonmotor and motor symptoms. Autonomic cardiovascular dysregulation is a common nonmotor manifestation associated with increased morbimortality. Conventional clinical treatment alleviates motor signs but does not change disease progression and fails in handling nonmotor features. Nutrition is a key modifiable determinant of chronic disease. This study aimed to assess the effects of propolis on cardiological features, heart rate (HR) and heart rate variability (HRV) and on nigrostriatal dopaminergic damage, detected by tyrosine hydroxylase (TH) immunoreactivity, in the 6-hydroxydopamine (6-OHDA) rat model of PD. Male Wistar rats were injected bilaterally with 6-OHDA or saline into the striatum and were treated with propolis or water for 40 days. Autonomic function was assessed by time domain parameters (standard deviation of all normal-to-normal intervals (SDNN) and square root of the mean of the squared differences between adjacent normal RR intervals (RMSSD)) of HRV calculated from electrocardiogram recordings. Reductions in HR (p = 1.47 × 10⁻¹⁹), SDNN (p = 3.42 × 10⁻¹⁰) and RMSSD (p = 8.2 × 10⁻⁶) detected in parkinsonian rats were reverted by propolis. Propolis attenuated neuronal loss in the substantia nigra (p = 5.66 × 10⁻¹⁵) and reduced striatal fiber degeneration (p = 7.4 × 10⁻⁵) in 6-OHDA-injured rats, which also showed significant weight gain (p = 1.07 × 10⁻⁵) in comparison to 6-OHDA-lesioned counterparts. Propolis confers cardioprotection and neuroprotection in the 6-OHDA rat model of PD.

Modulation of brain activity is one of the main mechanisms capable of demonstrating the synchronization dynamics of neural oscillations. In epilepsy, modulation is a key concept since seizures essentially result from neural hypersynchronization and hyperexcitability. In this study, we have introduced a time-dependent index based on the Kullback-Leibler divergence to quantify the effects of phase and frequency modulations of neural oscillations in neonatal mice exhibiting epileptiform activity induced by Zika virus (ZIKV) infection. Through this index, we demonstrate that fast oscillations (gamma and beta 2) are the more susceptible modulated rhythms in terms of phase, during seizures, whereas slow waves (delta and theta) mainly undergo changes in frequency. The index also allowed detection of specific patterns associated with the interdependent modulation of phase and frequency in neural activity. Furthermore, by comparing ZIKV modulations with the general computational model Epileptors, we verify different signatures related to the brain rhythms modulation in phase and frequency. These findings instigate new studies on the effects of ZIKV infection on neuronal networks from electrophysiological activities, and how different mechanisms can trigger epilepsy.

Echocardiographic exams allow the observation and extraction of measures related to cardiac structures. In the longitudinal parasternal view, these measures include the left ventricle end-diastolic and end-systolic diameters, end-diastolic interventricular septum thickness (IVSd), and end-diastolic left ventricle posterior wall thickness (LVPWd). Among these measures, the IVSd is important for diagnosing pathologies like hypertrophic cardiomyopathy, aneurysms, abnormal movement and structural faults. This work presents a hybrid neural network system to segment interventricular septum in echocardiographic images of parasternal longitudinal view. The hybrid system developed here consist of a Self-Organizing Map and a Multilayer Perceptron (MLP) neural network. The approach has two phases: clustering and classification. First, the Self-Organizing Map clusters image patches that are previously labeled as Septum and Non-septum. Later, an MLP is trained with information generated by the map. The MLP is then employed to classify patches of a new image resulting in a mask that indicates the probable septum regions. To validate the results, we did a semi-automatic extraction of septum thickness. The average error between the septum thicknesses obtained by the algorithm and the one manually traced was 0.5477mm ± 0.5277mm. Future recommendations are presented to improve the hybrid system performance to get more accurate results.