Rutaecarpine alleviates inflammation and fibrosis by targeting CK2α in diabetic nephropathy.

Diabetic nephropathy (DN) is one of the serious microvascular complications of diabetes mellitus. During the progression of DN, the proliferation of glomerular mesangial cells (GMCs) leads to the deposition of excessive extracellular matrix (ECM) in the mesangial region, eventually resulting in glomerulosclerosis. Rutaecarpine (Rut), an alkaloid found in the traditional Chinese medicinal herb Fructus Evodiae (Euodia rutaecarpa (Juss.

Revealing brain connectivity: graph embeddings for EEG representation learning and comparative analysis of structural and functional connectivity.

This study employs deep learning techniques to present a compelling approach for modeling brain connectivity in EEG motor imagery classification through graph embedding. The compelling aspect of this study lies in its combination of graph embedding, deep learning, and different brain connectivity types, which not only enhances classification accuracy but also enriches the understanding of brain function. The approach yields high accuracy, providing valuable insights into brain connections and has potential applications in understanding neurological conditions.

Classification of inattentional blindness using brain dynamics of ERPs.

Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources.

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions.

[Characteristics and Ecological Risk Assessment of Pharmaceutical Active Compounds in Guangdong Province].

Pharmaceutically active compounds(PhACs) have become a class of new pollutants in the environment after extensive production and use of PhACs in China. To investigate the pollution characteristics of PhACs in Guangdong Province, raw sewage was collected from 186 sewage treatment plants in 21 cities, including 178 townships and administrative districts in Guangdong Province. The pollution levels of ten typical PhACs in influent water of sewage treatment plants were analyzed using automatic solid phase extraction and high performance liquid chromatography-triple quadrupole mass spectrometry.

Human midbrain dopaminergic progenitors.

Human midbrain dopaminergic progenitors (mDAPs) are one of the most representative cell types in both basic research and clinical applications. However, there are still many challenges for the preparation and quality control of mDAPs, such as the lack of standards. Therefore, the establishment of critical quality attributes and technical specifications for mDAPs is largely needed.

Human neural stem cells.

'Human neural stem cells' jointly drafted and agreed upon by experts from the Chinese Society for Stem Cell Research, is the first guideline for human neural stem cells (hNSCs) in China. This standard specifies the technical requirements, test methods, test regulations, instructions for use, labelling requirements, packaging requirements, storage requirements, transportation requirements and waste disposal requirements for hNSCs, which is applicable to the quality control for hNSCs. It was originally released by the China Society for Cell Biology on 30 August 2022.

A tumor microenvironment-activatable nanoplatform with phycocyanin-assisted in-situ nanoagent generation for synergistic treatment of colorectal cancer.

The in-situ generation of therapeutic agents in targeted lesions is promising for revolutionizing oncotherapy but is limited by the low production efficiency. Given the specific tumor microenvironment (TME) of colorectal cancer (CRC), i.e.

Speech2EEG: Leveraging Pretrained Speech Model for EEG Signal Recognition.

Identifying meaningful brain activities is critical in brain-computer interface (BCI) applications. Recently, an increasing number of neural network approaches have been proposed to recognize EEG signals. However, these approaches depend heavily on using complex network structures to improve the performance of EEG recognition and suffer from the deficit of training data.

Association between Apolipoprotein E genotype and functional outcome in acute ischemic stroke.

This study aims to determine whether APOE alleles would affect the functional outcome in acute ischemic stroke (AIS) and whether the relationship between inflammation and stroke-related disability varies according to APOE genotypes. We retrospectively collected the demographic and clinical data of AIS patients within one week of symptom-onset through medical records review. The primary outcome was dependence or death, defined as modified Rankin scale (mRS) score of 2-6, which was assessed at 3 months.

Improving EEG-Based Driver Distraction Classification Using Brain Connectivity Estimators.

This paper discusses a novel approach to an EEG (electroencephalogram)-based driver distraction classification by using brain connectivity estimators as features. Ten healthy volunteers with more than one year of driving experience and an average age of 24.3 participated in a virtual reality environment with two conditions, a simple math problem-solving task and a lane-keeping task to mimic the distracted driving task and a non-distracted driving task, respectively.

Brain Connectivity Changes During Bimanual and Rotated Motor Imagery.

Motor imagery-based brain-computer interface (MI-BCI) currently represents a new trend in rehabilitation. However, individual differences in the responsive frequency bands and a poor understanding of the communication between the ipsilesional motor areas and other regions limit the use of MI-BCI therapy. Bimanual training has recently attracted attention as it achieves better outcomes as compared to repetitive one-handed training.

Adaptive Initialization Method for K-Means Algorithm.

The K-means algorithm is a widely used clustering algorithm that offers simplicity and efficiency. However, the traditional K-means algorithm uses a random method to determine the initial cluster centers, which make clustering results prone to local optima and then result in worse clustering performance. In this research, we propose an adaptive initialization method for the K-means algorithm (AIMK) which can adapt to the various characteristics in different datasets and obtain better clustering performance with stable results.

Exploring Covert States of Brain Dynamics via Fuzzy Inference Encoding.

Human brain inherently exhibits latent mental processes which are likely to change rapidly over time. A framework that adopts a fuzzy inference system is proposed to model the dynamics of the human brain. The fuzzy inference system is used to encode real-world data to represent the salient features of the EEG signals.

miR-19a/b-3p promotes inflammation during cerebral ischemia/reperfusion injury via SIRT1/FoxO3/SPHK1 pathway.

Background: Stroke affects 3-4% of adults and kills numerous people each year. Recovering blood flow with minimal reperfusion-induced injury is crucial. However, the mechanisms underlying reperfusion-induced injury, particularly inflammation, are not well understood.

Motor-Imagery-Based Brain-Computer Interface Using Signal Derivation and Aggregation Functions.

Brain-computer interface (BCI) technologies are popular methods of communication between the human brain and external devices. One of the most popular approaches to BCI is motor imagery (MI). In BCI applications, the electroencephalography (EEG) is a very popular measurement for brain dynamics because of its noninvasive nature.

The Impact of Vigorous Cycling Exercise on Visual Attention: A Study With the BR8 Wireless Dry EEG System.

Many studies have reported that exercise can influence cognitive performance. But advancing our understanding of the interrelations between psychology and physiology in sports neuroscience requires the study of real-time brain dynamics during exercise in the field. Electroencephalography (EEG) is one of the most powerful brain imaging technologies.

Gender-related difference in altered fractional amplitude of low-frequency fluctuations after electroacupuncture on primary insomnia patients: A resting-state fMRI study.

Background: Primary insomnia (PI) is defined as a sleep disorder with no definite cause or inducement. Electroacupuncture, a treatment of inserting needles into specific points on the body surface and applying electrical stimulation, has been proved effective in treating PI with minimal adverse effects. However, the influence of gender difference on the clinical treatment efficacy of electroacupuncture for PI patients remains unclear.

Statistical Analysis of Brain Connectivity Estimators during Distracted Driving.

This paper presents comparison of brain connectivity estimators of distracted drivers and non-distracted drivers based on statistical analysis. Twelve healthy volunteers with more than one year of driving experience participated in this experiment. Lane-keeping tasks and the Math problem-solving task were introduced in the experiment and EEGs (electroencephalogram) were used to record the brain waves.

A Driving Performance Forecasting System Based on Brain Dynamic State Analysis Using 4-D Convolutional Neural Networks.

Vehicle accidents are the primary cause of fatalities worldwide. Most often, experiencing fatigue on the road leads to operator errors and behavioral lapses. Thus, there is a need to predict the cognitive state of drivers, particularly their fatigue level.

Effectiveness and cerebral responses of multi-points acupuncture for primary insomnia: a preliminary randomized clinical trial and fMRI study.

Background: Primary insomnia (PI) is characterized by difficulties in initiating sleep or maintaining sleep, which lead to many serious diseases. Acupuncture for PI has drawn attention with its effectiveness and safety. However, the operation of choosing acupoints lacks scientific suggestion.

Facile and Reliable Thickness Identification of Atomically Thin Dichalcogenide Semiconductors Using Hyperspectral Microscopy.

Although large-scale synthesis of layered two-dimensional (2D) transition metal dichalcogenides (TMDCs) has been made possible, mechanical exfoliation of layered van der Waals crystal is still indispensable as every new material research starts with exfoliated flakes. However, it is often a tedious task to find the flakes with desired thickness and sizes. We propose a method to determine the thickness of few-layer flakes and facilitate the fast searching of flakes with a specific thickness.

Evaluation of the age-related and gender-related differences in patients with primary insomnia by fractional amplitude of low-frequency fluctuation: A resting-state functional magnetic resonance imaging study.

Insomnia patients with different gender and age usually had different sleep experience. Primary insomnia (PI) has been considered to be a disorder of hyper-arousal in the physiologic, emotional, or cognitive network. Although the hyper-arousal brain regions can be shown by comparing the brain activity of PI patients with normal people at rest, whether the brain activity of PI patients varied according to age and gender and whether age and gender could affect the distribution of hyper-arousal brain regions are still worthy of further exploration.