Proposed Mechanisms for the Relationship between Periodontal Diseases and the Severity of Covid-19: Common Pathogens, Inflammatory Mediators, and Risk Factors.

Periodontal disease (PD) is a chronic inflammatory disease with some cytokine involvement, associated with several risk factors such as diabetes, obesity, etc., Corona Virus Disease 2019 (COVID-19), a new viral infection, also appears to be related to cytokine storm and similar risk factors. In this review, we intend to evaluate the possible relationship between PD and COVID-19.

Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces.

Introduction: Event-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.

Decoding Bilateral Hindlimb Kinematics From Cat Spinal Signals Using Three-Dimensional Convolutional Neural Network.

To date, decoding limb kinematic information mostly relies on neural signals recorded from the peripheral nerve, dorsal root ganglia (DRG), ventral roots, spinal cord gray matter, and the sensorimotor cortex. In the current study, we demonstrated that the neural signals recorded from the lateral and dorsal columns within the spinal cord have the potential to decode hindlimb kinematics during locomotion. Experiments were conducted using intact cats.

Robust multi-input multi-output adaptive fuzzy terminal sliding mode control of deep brain stimulation in Parkinson's disease: a simulation study.

Deep brain stimulation (DBS) has become an effective therapeutic solution for Parkinson's disease (PD). Adaptive closed-loop DBS can be used to minimize stimulation-induced side effects by automatically determining the stimulation parameters based on the PD dynamics. In this paper, by modeling the interaction between the neurons in populations of the thalamic, the network-level modulation of thalamic is represented in a standard canonical form as a multi-input multi-output (MIMO) nonlinear first-order system with uncertainty and external disturbances.

Decoding hindlimb kinematics from descending and ascending neural signals during cat locomotion.

. The main objective of this research is to record both sensory and motor information from the ascending and descending tracts within the spinal cord to decode the hindlimb kinematics during walking on a treadmill..

A probabilistic recurrent neural network for decoding hind limb kinematics from multi-segment recordings of the dorsal horn neurons.

Objective: Providing accurate and robust estimates of limb kinematics from recorded neural activities is prominent in closed-loop control of functional electrical stimulation (FES). A major issue in providing accurate decoding the limb kinematics is the decoding model. The primary goal of this study is to develop a decoding approach to model the dynamic interactions of neural systems for accurate decoding.

Decoding hind limb kinematics from neuronal activity of the dorsal horn neurons using multiple level learning algorithm.

Decoding continuous hind limb joint angles from sensory recordings of neural system provides a feedback for closed-loop control of hind limb movement using functional electrical stimulation. So far, many attempts have been done to extract sensory information from dorsal root ganglia and sensory nerves. In this work, we examine decoding joint angles trajectories from the single-electrode extracellular recording of dorsal horn gray matter of the spinal cord during passive limb movement in anesthetized cats.