Alleviating protein-condensation-associated damage at the endoplasmic reticulum enhances plant disease tolerance.

Disease tolerance is an essential defense strategy against pathogens, alleviating tissue damage regardless of pathogen multiplication. However, its genetic and molecular basis remains largely unknown. Here, we discovered that protein condensation at the endoplasmic reticulum (ER) regulates disease tolerance in Arabidopsis against Pseudomonas syringae. During infection, Hematopoietic protein-1 (HEM1) and Bax-inhibitor 1 (BI-1) coalesce into ER-associated condensates facilitated by their phase-separation behaviors. While BI-1 aids in clearing these condensates via autophagy, it also sequesters lipid-metabolic enzymes within condensates, likely disturbing lipid homeostasis. Consequently, mutations in hem1, which hinder condensate formation, or in bi-1, which prevent enzyme entrapment, enhance tissue-damage resilience, and preserve overall plant health during infection. These findings suggest that the ER is a crucial hub for maintaining cellular homeostasis and establishing disease tolerance. They also highlight the potential of engineering disease tolerance as a defense strategy to complement established resistance mechanisms in combating plant diseases.

Research on low-power driving fatigue monitoring method based on spiking neural network.

Fatigue driving is one of the leading causes of traffic accidents, and the rapid and accurate detection of driver fatigue is of paramount importance for enhancing road safety. However, the application of deep learning models in fatigue driving detection has long been constrained by high computational costs and power consumption. To address this issue, this study proposes an approach that combines Self-Organizing Map (SOM) and Spiking Neural Networks (SNN) to develop a low-power model capable of accurately recognizing the driver's mental state. Initially, spatial features are extracted from electroencephalogram (EEG) signals using the SOM network. Subsequently, the extracted weight vectors are encoded and fed into the SNN for fatigue driving classification. The research results demonstrate that the proposed method effectively considers the spatiotemporal characteristics of EEG signals, achieving efficient fatigue detection. Simultaneously, this approach successfully reduces the model's power consumption. When compared to traditional artificial neural networks, our method reduces energy consumption by approximately 12.21-42.59%.

Proteasome inhibition induces apoptosis through simultaneous inactivation of MCL-1/BCL-XL by NOXA independent of CHOP and JNK pathways.

Proteasome inhibitors have been employed in the treatment of relapsed multiple myeloma and mantle cell lymphoma. The observed toxicity caused by proteasome inhibitors is a universal phenotype in numerous cancer cells with different sensitivity. In this study, we investigate the conserved mechanisms underlying the toxicity of the proteasome inhibitor bortezomib using gene editing approaches. Our findings utilizing different caspase knocking out cells reveal that bortezomib induces classic intrinsic apoptosis by activating caspase-9 and caspase-3/7, leading to pore-forming protein GSDME cleavage and subsequent lytic cell death or called secondary necrosis, a phenotype also observed in many apoptosis triggers like TNFα plus CHX, DTT and tunicamycin treatment in HeLa cells. Furthermore, through knocking out of nearly all BH3-only proteins including BIM, BAD, BID, BMF and PUMA, we demonstrate that NOXA is the sole BH3-only protein responsible for bortezomib-induced apoptosis. Of note, NOXA is well known for selectively binding to MCL-1 and A1, but our studies utilizing different BH3 mimetics as well as immunoprecipitation assays indicate that, except for the constitutive interaction of NOXA with MCL-1, the accumulation of NOXA after bortezomib treatment allows it to interact with BCL-XL, then simultaneous relieving suppression on apoptosis by both anti-apoptotic proteins BCL-XL and MCL-1. In addition, though bortezomib-induced significant ER stress and JNK activation were observed in the study, further genetic depletion experiments prove that bortezomib-induced apoptosis occurs independently of ER stress-related apoptosis factor CHOP and JNK. In summary, these results provide a solid conclusion about the critical role of NOXA in inactivation of BCL-XL except MCL-1 in bortezomib-induced apoptosis.

Comparison of Prognosis for Lung Transplantation between Older and Younger Donors: A Systematic Review and Meta-Analysis Based on Cohort Studies.

PURPOSE: This meta-analysis aimed to compare the prognosis of lung transplantation recipients based on donor age. METHODS: A detailed search was performed in PubMed, Embase, Web of Science, and the Cochrane Library for cohort studies on lung transplantation. The prognosis of lung transplant recipients was investigated based on the donor age, with the primary outcomes being 1-year overall survival (OS), 3-year OS, 5-year OS, and 5-year chronic lung allograft dysfunction (CLAD)-free survival. RESULTS: This meta-analysis included 10 cohort studies. Among the short-term outcomes, the older donor group demonstrated no significant difference from the young donor group in primary graft dysfunction within 72 hours, use of extracorporeal membrane oxygenation, length of ventilator use, and intensive care unit hours. However, a longer hospital stay was associated with the older donor group. In terms of long-term outcomes, no difference was found between the two groups in 1-year OS, 3-year OS, and 5-year OS. Notably, patients with older donors exhibited a superior 5-year CLAD-free survival. CONCLUSIONS: The results of this meta-analysis indicate that older donors are not inferior to younger donors in terms of long-term and short-term recipient outcomes. Lung transplantation using older donors is a potential therapeutic option after rigorous evaluation.

Metabolomic Analysis of Follicular Fluid in Normal-Weight Patients with Polycystic Ovary Syndrome.

BACKGROUND: This study aimed to examine the differential variations in the metabolic composition of follicular fluid (FF) among normal-weight patients with polycystic ovary syndrome (PCOS) and controls and to identify potential biomarkers that may offer insights into the early identification and management of these patients. METHODS: We collected FF samples from 45 normal-weight women with PCOS and 36 normal-weight controls without PCOS who were undergoing in vitro fertilization-embryo transfer. An untargeted metabolomic study of collected FF from infertile women was performed using high-performance liquid chromatography-tandem spectrometry (LC-MS). The tendency of the two groups to separate was demonstrated through multivariate analysis. Univariate analysis and variable importance in projection were used to screen out differential metabolites. Metabolic pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a diagnostic model was established using the random forest algorithm. RESULTS: The metabolomics analysis revealed an increase in the expression of 23 metabolites and a decrease in that of 10 metabolites in the FF of normal-weight women with PCOS. According to the KEGG pathway analysis, these differential metabolites primarily participated in the metabolism of glycerophospholipids and the biosynthesis of steroid hormones. Based on the biomarker combination of the top 10 metabolites, the area under the curve value was 0.805. The concentrations of prostaglandin E2 in the FF of individuals with PCOS exhibited an inverse association with the proportion of high-quality embryos (p < 0.05). CONCLUSIONS: Our research identified a distinct metabolic profile of the FF from normal-weight women with PCOS. The results offer a broader comprehension of the pathogenesis and advancement of PCOS, and the detected differential metabolites could be potential biomarkers and targets for the treatment of PCOS.

Hepatic miR-363 promotes nonalcoholic fatty liver disease by suppressing INSIG1.

Nonalcoholic fatty liver disease (NAFLD) constitutes one of major worldwide health problem which typically progressively results in nonalcoholic steatohepatitis (NASH) and eventually cirrhosis and liver cancer. Liver-specific deletion of INSIG1 promotes SREBP1 nuclear translocation to activate downstream lipogenic genes expression, leading to lipid accumulation. However, the underlying pathogenesis of NAFLD, and particularly involved in miRNA participation are still to be thoroughly explored. Here, we found that miR-363-3p was significantly overexpressed in high-fat, high-cholesterol (HFHC) diet mice liver tissue and fatty acid-induced steatosis cells. miR-363-3p directly targets INSIG1 to inhibit its expression, thereby facilitating the cleavage of SREBP and nuclear translocation to activate subsequent transcription of lipogenic genes in vitro and in vivo. In addition, we identified apigenin, a natural flavonoid compound, inhibited miR-363-3p expression to up-regulate INSIG1 and suppress nuclear translocation of SREBP1, thereby down-regulated lipogenic genes expression in steatosis cells and HFHC diet mice liver tissues. Taken together, our results demonstrated that miR-363-3p as a key regulator of hepatic lipid homeostasis targeted INSIG1, and apigenin alleviated NAFLD through the miR-363-3p/INSIG1/SREBP1 pathway. This indicates that reduction of miR-363-3p levels as a possible treatment of hepatic steatosis and provides a potential new therapeutic strategy for targeting miRNA to ameliorate NAFLD.

Detection of virtual reality motion sickness based on EEG using asymmetry of entropy and cross-frequency coupling.

The existence of Virtual Reality Motion Sickness (VRMS) is a key factor restricting the further development of the VR industry, and the premise to solve this problem is to be able to accurately and effectively detect its occurrence. In view of the current lack of high-accuracy and effective detection methods, this paper proposes a VRMS detection method based on entropy asymmetry and cross-frequency coupling value asymmetry of EEG. First of all, the EEG of the four selected pairs of electrodes on the bilateral brain are subjected to Multivariate Variational Mode Decomposition (MVMD) respectively, and three types of entropy values on the low-frequency and high-frequency components are calculated, namely approximate entropy, fuzzy entropy and permutation entropy, as well as three types of phase-amplitude coupling features between the low-frequency and high-frequency components, namely the mean value, standard deviation and correlation coefficient; Secondly, the difference of the entropies and the cross-frequency coupling features between the left electrodes and the right electrodes are calculated; Finally, the final feature set are selected via t-test and fed into the SVM for classification, thus realizing the automatic detection of VRMS. The results show that the three classification indexes under this method, i.e., accuracy, sensitivity and specificity, reach 99.5 %, 99.3 % and 99.7 %, respectively, and the value of the area under the ROC curve reached 1, which proves that this method can be an effective indicator for detecting the occurrence of VRMS.

Rapid Mass Spectrometry-Based Multiattribute Method for Glycation Analysis with Integrated Afucosylation Detection Capability.

The multiattribute method (MAM) has emerged as a powerful tool for simultaneously screening multiple product quality attributes of therapeutic antibodies. One such potential critical quality attribute (CQA) is glycation, a common modification that can impact the heterogeneity, functional activity, and immunogenicity of therapeutic antibodies. However, current methods for monitoring glycation levels in MAM are rare and not sufficiently rapid and accurate. In this study, an improved mass spectrometry (MS)-based MAM was developed to simultaneously monitor glycation and other quality attributes including afucosylation. The method was evaluated using two therapeutic antibodies with different glycosylation site numbers. Treatment with IdeS, Endo F2, and dithiothreitol generated three distinct subunits, and the glycation results obtained were similar to those treated with PNGase F, which is routinely used to release glycans; the sample processing time was greatly reduced while providing additional quality attribute information. The MS-based MAM was also employed to assess the glycation progression following forced glycation in various buffer solutions. A significant increase in oxidation was observed when forced glycation was conducted in an ammonium bicarbonate buffer solution, and a total of 23 potential glycation sites and 4 significantly oxidized sites were identified. Notably, we found that ammonium bicarbonate was found to specifically stimulate oxidation, while glycation had a synergistic effect on oxidation. These findings establish this study as a novel methodology for achieving a technologically advanced platform and concept that enhances the efficacy of product development and quality control, characterized by its broad-spectrum, rapid, and accurate nature.

Vagus nerve stimulation (VNS) inhibits cardiac mast cells activation and improves myocardial atrophy after ischemic stroke.

BACKGROUND: Ischemic stroke is one of the leading causes of chronic disability worldwide, and stroke-induced heart damage can lead to death. According to research, patients with a variety of brain disease have good clinical results after vagus nerve stimulation (VNS). After ischemic stroke, mast cells (MCs) degranulate and release a large number of mediators, which may cause systemic inflammation. Chymase secreted by MCs can increase the levels of pathological angiotensin II (AngⅡ), which plays a crucial role in the deterioration of heart disease. Our goal was to develop a minimally invasive, targeted, and convenient VNS approach to assess the impact of VNS and to clarify the relationship between VNS and MCs in the prognosis of patients with myocardial atrophy after acute ischemic stroke. METHODS: In this study, we verified the role of VNS in the treatment of myocardial atrophy after stroke and its molecular mechanism using a rat model of middle cerebral artery occlusion (MCAO/r). Behavioral studies were assessed using neurobehavioral deficit scores. Enzyme-linked immunosorbent assays, immunofluorescence staining, Western blotting and qRT-PCR were used to analyze the expression levels of myocardial atrophy, MC and inflammatory markers in rat hearts. RESULTS: VNS improved myocardial atrophy in MCAO/r rats, inhibited MC activation, reduced the expression of chymase and AngⅡ, and inhibited the expression of proinflammatory factors. The chymase activator C48/80 reversed these effects of VNS. Chymase activation inhibited the effect of VNS on myocardial atrophy in MCAO/r rats, increased AngⅡ expression and aggravated inflammation and autophagy. The myocardial atrophy of MCAO/r rats was improved after chymase inhibition, and AngⅡ expression, inflammation and autophagy were reduced. Our results suggest that VNS may reduce the expression of chymase and AngⅡ by inhibiting MC activation, thereby improving myocardial atrophy and reducing inflammation and autophagy in MCAO/r rats. Inhibition of MC activation may be an effective strategy for treating myocardial atrophy after stroke. CONCLUSIONS: VNS inhibits MC activation and reduces the expression of chymase and AngII, thereby alleviating myocardial atrophy, inflammation and autophagy after stroke.

Development of a reliable cell-based reporter gene assay to measure the bioactivity of anti-HER2 therapeutic antibodies.

Human epidermal growth factor receptor 2 (HER2) is a key player in the pathogenesis and progression of breast cancer and is currently a primary target for breast cancer immunotherapy. Bioactivity determination is necessary to guarantee the safety and efficacy of therapeutic antibodies targeting HER2. Nevertheless, currently available bioassays for measuring the bioactivity of anti-HER2 mAbs are either not representative or have high variability. Here, we established a reliable reporter gene assay (RGA) based on T47D-SRE-Luc cell line that expresses endogenous HER2 and luciferase controlled by serum response element (SRE) to measure the bioactivity of anti-HER2 antibodies. Neuregulin-1 (NRG-1) can lead to the heterodimerization of HER2 on the cell membrane and induce the expression of downstream SRE-controlled luciferase, while pertuzumab can dose-dependently reverse the reaction, resulting in a good dose-response curve reflecting the activity of the antibody. After optimizing the relevant assay parameters, the established RGA was fully validated based on ICH-Q2 (R1), which demonstrated that the method had excellent specificity, accuracy, precision, linearity, and stability. In summary, this robust and innovative bioactivity determination assay can be applied in the development and screening, release control, biosimilar assessment and stability studies of anti-HER2 mAbs.

SOAT1 regulates cholesterol metabolism to induce EMT in hepatocellular carcinoma.

Cholesterol metabolism reprogramming is one of the significant characteristics of hepatocellular carcinoma (HCC). Cholesterol increases the risk of epithelial-mesenchymal transition (EMT) in cancer. Sterol O-acyltransferases 1 (SOAT1) maintains the cholesterol homeostasis. However, the exact mechanistic contribution of SOAT1 to EMT in HCC remains unclear. Here we demonstrated that SOAT1 positively related to poor prognosis of HCC, EMT markers and promoted cell migration and invasion in vitro, which was mediated by the increased cholesterol in plasmalemma and cholesterol esters accumulation. Furthermore, we reported that SOAT1 disrupted cholesterol metabolism homeostasis to accelerate tumorigenesis and development in HCC xenograft and NAFLD-HCC. Also, we detected that nootkatone, a sesquiterpene ketone, inhibited EMT by targeting SOAT1 in vitro and in vivo. Collectively, our finding indicated that SOAT1 promotes EMT and contributes to hepatocarcinogenesis by increasing cholesterol esterification, which is suppressed efficiently by nootkatone. This study demonstrated that SOAT1 is a potential biomarker and therapeutic target in NAFLD-HCC and SOAT1-targeting inhibitors are expected to be the potential new therapeutic treatment for HCC.

EEG classification model for virtual reality motion sickness based on multi-scale CNN feature correlation.

BACKGROUND: Virtual reality motion sickness (VRMS) is a key issue hindering the development of virtual reality technology, and accurate detection of its occurrence is the first prerequisite for solving the issue. OBJECTIVE: In this paper, a convolutional neural network (CNN) EEG detection model based on multi-scale feature correlation is proposed for detecting VRMS. METHODS: The model uses multi-scale 1D convolutional layers to extract multi-scale temporal features from the multi-lead EEG data, and then calculates the feature correlations of the extracted multi-scale features among all the leads to form the feature adjacent matrixes, which converts the time-domain features to correlation-based brain network features, thus strengthen the feature representation. Finally, the correlation features of each layer are fused. The fused features are then fed into the channel attention module to filter the channels and classify them using a fully connected network. Finally, we recruit subjects to experience 6 different modes of virtual roller coaster scenes, and collect resting EEG data before and after the task to verify the model. RESULTS: The results show that the accuracy, precision, recall and F1-score of this model for the recognition of VRMS are 98.66 %, 98.65 %, 98.68 %, and 98.66 %, respectively. The proposed model outperforms the current classic and advanced EEG recognition models. SIGNIFICANCE: It shows that this model can be used for the recognition of VRMS based on the resting state EEG.

Improving product quality and productivity of an antibody-based biotherapeutic using inverted frustoconical shaking bioreactors.

The Chinese hamster ovarian (CHO) cells serve as a common choice in biopharmaceutical production, traditionally cultivated in stirred tank bioreactors (STRs). Nevertheless, the pursuit of improved protein quality and production output for commercial purposes demand exploration into new bioreactor types. In this context, inverted frustoconical shaking bioreactors (IFSB) present unique physical properties distinct from STRs. This study aims to compare the production processes of an antibody-based biotherapeutic in both bioreactor types, to enhance production flexibility. The findings indicate that, when compared to STRs, IFSB demonstrates the capability to produce an antibody-based biotherapeutic with either comparable or enhanced bioprocess performance and product quality. IFSB reduces shear damage to cells, enhances viable cell density (VCD), and improves cell state at a 5-L scale. Consequently, this leads to increased protein expression (3.70 g/L vs 2.56 g/L) and improved protein quality, as evidenced by a reduction in acidic variants from 27.0% to 21.5%. Scaling up the culture utilizing the Froude constant and superficial gas velocity ensures stable operation, effective mixing, and gas transfer. The IFSB maintains a high VCD and cell viability at both 50-L and 500-L scales. Product expression levels range from 3.0 to 3.6 g/L, accompanied by an improved acidic variants attribute of 20.6%-22.7%. The IFSB exhibits superior productivity and product quality, underscoring its potential for incorporation into the manufacturing process for antibody-based biotherapeutics. These results establish the foundation for IFSB to become a viable option in producing antibody-based biotherapeutics for clinical and manufacturing applications.

Stable-Isotope N-Me Aziridination Enables Accurate Quantitative C═C Isomeric Lipidomics.

Accurate lipid quantification is essential to revealing their roles in physiological and pathological processes. However, difficulties in the structural resolution of lipid isomers hinder their further accurate quantification. To address this challenge, we developed a novel stable-isotope N-Me aziridination strategy that enables simultaneous qualification and quantification of unsaturated lipid isomers. The one-step introduction of the 1-methylaziridine structure not only serves as an activating group for the C═C bond to facilitate positional identification but also as an isotopic inserter to achieve accurate relative quantification. The high performance of this reaction for the identification of unsaturated lipids was verified by large-scale resolution of the C═C positions of 468 lipids in serum. More importantly, by using this bifunctional duplex labeling method, various unsaturated lipids such as fatty acids, phospholipids, glycerides, and cholesterol ester were accurately and individually quantified at the C═C bond isomeric level during the mouse brain ischemia. This study provides a new approach to quantitative structural lipidomics.

Highly drug/target-tolerant neutralizing antibody (NAb) assay development through target-based drug depletion and drug-based NAb extraction for an anti-EGFR therapeutic monoclonal antibody.

The reduction of immunogenicity is fundamental for the development of biobetter Erbitux, given that the development of an immune response reduces treatment efficacy and may lead to potential side effects. One of the requirements for the clinical research of a Erbitux biobetter candidate (CMAB009) is to develop a neutralizing antibody (NAb) assay, and sufficient drug and target tolerance for the assay is necessary. Here, we describe the development of a competitive ligand binding (CLB) assay for CMAB009 with high drug and target tolerance through target-based drug depletion and drug-based NAb extraction, the integrated experimental strategy was implemented to simultaneously mitigate drug interference and enhance target tolerance. Following troubleshooting and optimization, the NAb assay was validated for clinical sample analysis with the sensitivity of 92 ng/mL, drug tolerance of 70 µg/mL and target tolerance of 798 ng/mL. The innovative drug depletion and NAb extraction achieved though the combination of drug and target beads would enable the development of reliable NAb assays for many other therapeutics that overcome drug and its target interference for more precise and sensitive NAb assessment.

Efficient treatment of colon cancer with codelivery of TRAIL and imatinib by liposomes.

To solve the problem of resistance of tumor cells to TRAIL and the inevitable side effects of imatinib during treatment, we successfully prepared a kind of multifunctional liposome that encapsulated imatinib in its internal water phase and inserted TRAIL on its membrane in this study, which named ITLPs. The liposomes appeared uniform spherical and the particle size was approximately 150 nm. ITLPs showed high accumulation in TRAIL-resistance cells and HT-29 tumor-bearing mice model. In vitro cytotoxicity assay results showed that the killing activity of HT-29 cells treated with ITLPs increased by 50% and confirmed that this killing activity was mediated by the apoptosis pathway. Through mechanism studies, it was found that ITLPs arrested up to 32.3% of cells in phase M to exert anti-tumor effects. In vivo anti-tumor study showed that ITLPs achieved 61.8% tumor suppression and little toxicity in the HT-29 tumor-bearing mice model. Overall results demonstrated that codelivery of imatinib and TRAIL via liposomes may be a prospective method in the treatment of the TRAIL-resistance tumor.

Effect of cesarean section on the risk of autism spectrum disorders/attention deficit hyperactivity disorder in offspring: a meta-analysis.

PURPOSE: This study was conducted to investigate the relationship between cesarean section (CS) offspring and autism spectrum disorders (ASD)/attention deficit hyperactivity disorder (ADHD). METHODS: Searching of the databases (PubMed, Web of Science, Embase, and Cochrane Library) for studies on the relationship between mode of delivery and ASD/ADHD until August 2022. The primary outcome was the incidence of ASD/ADHD in the offspring. RESULTS: This meta-analysis included 35 studies (12 cohort studies and 23 case-control studies). Statistical results showed a higher risk of ASD (odds ratio (OR) = 1.25, P < 0.001) and ADHD (OR = 1.11, P < 0.001) in CS offspring compared to the VD group. Partial subgroup analysis showed no difference in ASD risk between CS and VD offspring in sibling-matched groups (OR = 0.98, P = 0.625). The risk of ASD was higher in females (OR = 1.66, P = 0.003) than in males (OR = 1.17, P = 0.004) in the CS offspring compared with the VD group. There was no difference in the risk of ASD between CS under regional anesthesia group and VD group (OR = 1.07, P = 0.173). However, the risk of ASD was higher in the CS offspring under general anesthesia than in the VD offspring (OR = 1.62, P < 0.001). CS offspring developed autism (OR = 1.38, P = 0.011) and pervasive developmental disorder-not otherwise specified (OR = 1.46, P = 0.004) had a higher risk than VD offspring, but there was no difference in Asperger syndrome (OR = 1.19, P = 0.115). Offspring born via CS had a higher incidence of ADHD in different subgroup analyses (sibling-matched, type of CS, and study design). CONCLUSIONS: In this meta-analysis, CS was a risk factor for ASD/ADHD in offspring compared with VD.

pH Sensitive Quercetin Nanoparticles Ameliorate DSS-Induced Colitis in Mice by Colon-Specific Delivery.

SCOPE: Ulcerative colitis (UC) is a classic inflammatory bowel disease (IBD) that represents a serious threat to human health. As a natural flavonoid with multiple biological activities, quercetin (QCT) suffers from low bioavailability through limitations in chemical stability. Here, the study investigates the regulatory effects of quercetin nanoparticles (QCT NPs) on dextran sulfate sodium (DSS) induced colitis mice. METHODS AND RESULTS: Chitosan is modified to obtain N-succinyl chitosan (NSC) with superior water solubility. Nanoparticles composed of sodium alginate (SA) and NSC can encapsulate QCT after cross-linking, forming QCT NPs. In vitro drug release assays demonstrate the pH sensitivity of QCT NPs. Compared with free quercetin, QCT NPs have better therapeutic efficacy in modulating gut microbiota and its metabolites short chain fatty acid (SCFAs) to relieve DSS-induced colitis in mice, thereby alleviating colon inflammatory infiltration, increasing goblet cells density and mucus protein, ameliorating TNF-α, IL-1ß, IL-6, IL-10, and Myeloperoxidase (MPO) levels, and recovering intestinal barrier integrity. CONCLUSION: pH sensitive QCT nanoparticles can reduce inflammatory reaction, improve gut microbiota, and repair intestinal barrier by targeting colon, thus improving DSS induced colitis in mice, providing reference for the treatment of colitis.

Real driving environment EEG-based detection of driving fatigue using the wavelet scattering network.

BACKGROUND: Driving fatigue is one of the main factors leading to traffic accidents. So, it is necessary to detect driver fatigue accurately and quickly. NEW METHOD: To precisely detect driving fatigue in a real driving environment, this paper adopts a classification method for driving fatigue based on the wavelet scattering network (WSN). Firstly, electroencephalogram (EEG) signals of 12 subjects in the real driving environment are collected and categorized into two states: fatigue and awake. Secondly, the WSN algorithm extracts wavelet scattering coefficients of EEG signals, and these coefficients are used as input in support vector machine (SVM) as feature vectors for classification. RESULTS: The results showed that the average classification accuracy of 12 subjects reached 99.33%; the average precision rate reached 99.28%; the average recall rate reached 98.27%; the average F1 score reached 98.74%; and the average classification accuracy of the public data set SEED-VIG reached 99.39%. The average precision, recall rate and F1 score reached 99.27%, 98.41% and 98.83% respectively. COMPARISON WITH EXISTING METHODS: In addition, the WSN algorithm is compared with traditional convolutional neural network (CNN), Sparse-deep belief networks (SDBN), Spatio-temporal convolutional neural networks (STCNN), Long short-term memory (LSTM), and other methods, and it is found that WSN has higher classification accuracy. CONCLUSION: Furthermore, this method has good versatility, providing excellent recognition effect on small sample data sets, and fast running time, making it convenient for real-time online monitoring of driver fatigue. Therefore, the WSN algorithm is promising in efficiently detecting driving fatigue state of drivers in real environments, contributing to improved traffic safety.

Motor intent recognition of multi-feature fusion EEG signals by UMAP algorithm.

The key to the analysis of electroencephalogram (EEG) signals lies in the extraction of effective features from the raw EEG signals, which can then be utilized to augment the classification accuracy of motor imagery (MI) applications in brain-computer interface (BCI). It can be argued that the utilization of features from multiple domains can be a more effective approach to feature extraction for MI pattern classification, as it can provide a more comprehensive set of information that the traditional single feature extraction method may not be able to capture. In this paper, a multi-feature fusion algorithm based on uniform manifold approximate and projection (UMAP) is proposed for motor imagery EEG signals. The brain functional network and common spatial pattern (CSP) are initially extracted as features. Subsequently, UMAP is utilized to fuse the extracted multi-domain features to generate low-dimensional features with improved discriminative capability. Finally, the k-nearest neighbor (KNN) classifier is applied in a lower dimensional space. The proposed method is evaluated using left-right hand EEG signals, and achieved the average accuracy of over 92%. The results indicate that, compared with single-domain-based feature extraction methods, multi-feature fusion EEG signal classification based on the UMAP algorithm yields superior classification and visualization performance. Feature extraction and fusion based on UMAP algorithm of left-right hand motor imagery.