Exosomes derived from baicalin‑pretreated mesenchymal stem cells mitigate atherosclerosis by regulating the SIRT1/NF‑κB signaling pathway.

Atherosclerosis (AS) is a disease with high global incidence and mortality rates. Currently, the treatment of AS in clinical practice carries a high risk of adverse effects and toxic side effects. The pretreatment of mesenchymal stem cells (MSCs) with drugs may enhance the bioactivity of MSC‑derived exosomes (MSC‑exos), which could be a promising candidate for inhibiting the progression of AS.

Realize Generative Yet Complete Latent Representation for Incomplete Multi-View Learning.

In multi-view environment, it would yield missing observations due to the limitation of the observation process. The most current representation learning methods struggle to explore complete information by lacking either cross-generative via simply filling in missing view data, or solidative via inferring a consistent representation among the existing views. To address this problem, we propose a deep generative model to learn a complete generative latent representation, namely Complete Multi-view Variational Auto-Encoders (CMVAE), which models the generation of the multiple views from a complete latent variable represented by a mixture of Gaussian distributions.

Toward Assessment of Sound Localization in Disorders of Consciousness Using a Hybrid Audiovisual Brain-Computer Interface.

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R.

Enhancing Performance of a Hybrid EEG-fNIRS System Using Channel Selection and Early Temporal Features.

Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties.

Multiparameter Electromyography Analysis of the Masticatory Muscle Activities in Patients with Brainstem Stroke at Different Head Positions.

The performance of the masticatory muscle is frequently affected and presents high heterogeneity poststroke. Surface electromyography (EMG) is widely used to quantify muscle movement patterns. However, only a few studies applied EMG analysis on the research of masticatory muscle activities poststroke, and most of which used single parameter-root mean squares (RMS).