Quercetin regulates dendritic cell activation by targeting STAT4 in the treatment of experimental autoimmune encephalomyelitis.

Multiple sclerosis (MS) is a class of autoimmune diseases mainly caused by the immune system attacking the myelin sheath of the axons in the nervous system. Although the pathogenesis of MS is complex, studies have shown that dendritic cells (DCs) play a vital role in the pathogenesis of MS. Quercetin (QU) has a unique advantage in clinical application, especially for treating autoimmune diseases. However, the mechanism of QU in the treatment of experimental autoimmune encephalomyelitis (EAE) remains unclear. In this study, we explore the potential role of QU in EAE. Finally, we find that QU has anti-inflammatory activities and neural protective effects in EAE. The experimental results suggest that the cellular basis for QU's function is to inhibit the activation of DCs while modulating the Th17 cell differentiation in the co-culture system. Further, QU may target STAT4 to inhibit its activation in DCs. This work will be of great significance for the future development and utilization of QU.

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

OBJECTIVES: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. METHODS: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. RESULTS: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. CONCLUSIONS: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Enantioselective formal total synthesis of dihydrospirotryprostatin B.

Spirotryprostatins are representative members of medicinally interesting bioactive molecules of the spirooxindole natural products. In this communication, we present a novel enantioselective total synthesis of the spirooxindole alkaloid dihydrospirotryprostatin B. The synthesis takes advantage of copper-catalyzed tandem reaction of o-iodoanilide chiral sulfinamide derivatives with alkynone to rapidly construct the key quaternary carbon stereocenter of the natural product dihydrospirotryprostatin B.

Different machine learning methods based on maxillary sinus in sex estimation for northwestern Chinese Han population.

BACKGROUND & OBJECTIVE: Sex estimation is a critical aspect of forensic expertise. Some special anatomical structures, such as the maxillary sinus, can still maintain integrity in harsh environmental conditions and may be served as a basis for sex estimation. Due to the complex nature of sex estimation, several studies have been conducted using different machine learning algorithms to improve the accuracy of sex prediction from anatomical measurements. MATERIAL & METHODS: In this study, linear data of the maxillary sinus in the population of northwest China by using Cone-Beam Computed Tomography (CBCT) were collected and utilized to develop logistic, K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and random forest (RF) models for sex estimation with R 4.3.1. CBCT images from 477 samples of Han population (75 males and 81 females, aged 5-17 years; 162 males and 159 females, aged 18-72) were used to establish and verify the model. Length (MSL), width (MSW), height (MSH) of both the left and right maxillary sinuses and distance of lateral wall between two maxillary sinuses (distance) were measured. 80% of the data were randomly picked as the training set and others were testing set. Besides, these samples were grouped by age bracket and fitted models as an attempt. RESULTS: Overall, the accuracy of the sex estimation for individuals over 18 years old on the testing set was 77.78%, with a slightly higher accuracy rate for males at 78.12% compared to females at 77.42%. However, accuracy of sex estimation for individuals under 18 was challenging. In comparison to logistic, KNN and SVM, RF exhibited higher accuracy rates. Moreover, incorporating age as a variable improved the accuracy of sex estimation, particularly in the 18-27 age group, where the accuracy rate increased to 88.46%. Meanwhile, all variables showed a linear correlation with age. CONCLUSION: The linear measurements of the maxillary sinus could be a valuable tool for sex estimation in individuals aged 18 and over. A robust RF model has been developed for sex estimation within the Han population residing in the northwestern region of China. The accuracy of sex estimation could be higher when age is used as a predictive variable.

Sex estimation using maxillary sinus volume for Chinese subjects based on cone-beam computed tomography.

BACKGROUND: Sex estimate is a key stage in forensic science for identifying individuals. Some anatomical structures may be useful for sex estimation since they retain their integrity even after highly severe events. However, few studies are focusing on the Chinese population. Some researchers used teeth for sex estimation, but comparison with maxillary sinus were lack. As a result, the objective of this research is to develop a sex estimation formula for the northwestern Chinese population by the volume of the maxillary sinus and compare with the accuracy of sex estimation based on teeth through cone-beam computed tomography (CBCT). METHODS: CBCT images from 349 samples were used to establish and verify the formula. The volume of both the left and right maxillary sinuses was measured and examined for appropriate formula coefficients. To create the formula, we randomly picked 80% of the data as the training set and 20% of the samples as the testing set. Another set of samples, including 20 males and 20 females, were used to compare the accuracy of maxillary sinuses and teeth. RESULTS: Overall, sex estimation accuracy by volume of the left maxillary sinus can reach 78.57%, while by the volume of the right maxillary sinus can reach 74.29%. The accuracy for females, which can reach 91.43% using the left maxillary sinus, was significantly higher than that for males, which was 65.71%. The result also shows that maxillary sinus volume was higher in males. The comparison with the available results using measurements of teeth for sex estimation performed by our group showed that the accuracy of sex estimation using canines volume was higher than the one using maxillary sinus volume, the accuracies based on mesiodistal diameter of canine and first molar were the same or lower than the volume of maxillary sinus. CONCLUSIONS: The study demonstrates that measurement of maxillary sinus volume based on CBCT scans was an available and alternative method for sex estimation. And we established a method to accurately assess the sex of the northwest Chinese population. The comparison with the results of teeth measurements made the conclusion more reliable.

[Distribution Characteristics of Arsenic in Drinking Water in China and Its Health Risk Based on Disability-adjusted Life Years].

Long-term exposure to arsenic can lead to functional damage to many organs and can be life-threatening. It is of great significance to analyze the distribution characteristics of arsenic in water and evaluate its potential risk for preventing and controlling human health hazards caused by water-derived arsenic. Based on the published data from 2000 to 2022, the geographical distribution characteristics of arsenic in drinking water across China were systematically analyzed in detail, and the health risk of arsenic in drinking water was quantitatively assessed using disability-adjusted life years (DALYs), which represent the burden of disease. The results showed that the average concentration of arsenic in drinking water in China was (2.88 ±0.33) µg·L-1, which was lower than the limit of 10 µg·L-1 set by the standard for drinking water quality (GB 5749-2022). Nevertheless, the arsenic in drinking water in some provinces, including Shanxi, Inner Mongolia, and Ningxia, was still higher than the limit. The arsenic concentration in drinking water in northern China was higher than that in southern China, and that in rural areas was higher than that in cities. The estimated health risk of arsenic in drinking water (1.63×10-6 DALYs per person-year) was higher than the acceptable risk level of waterborne exposure of 1.0×10-6 DALYs per person-year set by the World Health Organization. The personal health risks related to arsenic in drinking water in the six geographical regions were ranked as follows:North China > Northeast China > Central South China > Northwest China > Southwest China > East China. Almost all (99.4%) of the health burden associated with water arsenic was attributable to skin and lung cancer, which caused 2 905.25 and 1 513.96 DALYs per year, respectively. Most (78.0%) of the health burden was borne by people aged 45 years or older. In addition, given the proportion of each age group in the total population, older persons over the age of 60 bear a higher drinking-water-associated arsenic burden at the individual level than others, and attention should be consequently paid to them when controlling the risk of arsenic in water.

Microglia-derived exosomes modulate myelin regeneration via miR-615-5p/MYRF axis.

Demyelination and failure of remyelination in the central nervous system (CNS) characterize a number of neurological disorders. Spontaneous remyelination in demyelinating diseases is limited, as oligodendrocyte precursor cells (OPCs), which are often present in demyelinated lesions in abundance, mostly fail to differentiate into oligodendrocytes, the myelinating cells in the CNS. In addition to OPCs, the lesions are assembled numbers of activated resident microglia/infiltrated macrophages; however, the mechanisms and potential role of interactions between the microglia/macrophages and OPCs are poorly understood. Here, we generated a transcriptional profile of exosomes from activated microglia, and found that miR-615-5p was elevated. miR-615-5p bound to 3'UTR of myelin regulator factor (MYRF), a crucial myelination transcription factor expressed in oligodendrocyte lineage cells. Mechanistically, exosomes from activated microglia transferred miR-615-5p to OPCs, which directly bound to MYRF and inhibited OPC maturation. Furthermore, an effect of AAV expressing miR-615-5p sponge in microglia was tested in experimental autoimmune encephalomyelitis (EAE) and cuprizone (CPZ)-induced demyelination model, the classical mouse models of multiple sclerosis. miR-615-5p sponge effectively alleviated disease progression and promoted remyelination. This study identifies miR-615-5p/MYRF as a new target for the therapy of demyelinating diseases.

ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys.

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.

Delivering substance use prevention interventions for adolescents in educational settings: A scoping review.

Currently, a proportion of adolescents use alcohol, tobacco, and illicit drugs, which inevitably harms their health and academic progress. Adolescence is a peak period for substance use initiation and a critical time for preventing substance use problems. Various entities, such as families, schools, and communities, have implemented a variety of interventions to alleviate adolescent substance use problems, and schools play a unique role. To explore the types, characteristics, and effectiveness of substance use interventions in educational settings for adole-scents, we conducted a scoping review and identified 32 studies after screening. We divided the 32 studies according to intervention type, including curriculum interventions focusing on cognitive-behavioral skill enhancement, exercise interventions, peer interventions and family-school cooperation, and electronic interventions. Except for the mixed results on electronic interventions, the results showed that the other interventions were beneficial to different extents in alleviating adolescent substance use problems. In addition, we analyzed and summarized the advantages and challenges of intervening in adolescent substance use in educational settings. Schools can use equipment and human resources to provide adolescents with various types of intervention measures, but they also face challenges such as stigmatization, ineffective coordination among multiple resources, and poor implementation effects. In the future, school-based intervention measures can fully utilize big data and artificial intelligence technology and collaborate with families and communities to intervene appro-priately while paying attention to the comorbidity risks of substance use disorders and psychological health issues.

Digital interventions empowering mental health reconstruction among students after the COVID-19 pandemic.

With the gradual end of the coronavirus disease 2019 (COVID-19) pandemic, the reconstruction of students' mental health is urgently necessary. Digital interventions offer advantages such as high accessibility, anonymity, and accurate identification, which can promote the reconstruction of students' mental health through the provision of psychological support platforms, psychological assessment tools, and online mental health activities. However, we recognize that digital interventions must undergo many adjustments, and corresponding ethical norms require further clarification. It is crucial for different stakeholders to collaborate and work toward maximizing the effectiveness of digital interventions for the reconstruction of mental health after the COVID-19 pandemic.

Automatic sex estimation using deep convolutional neural network based on orthopantomogram images.

Sex estimation is very important in forensic applications as part of individual identification. Morphological sex estimation methods predominantly focus on anatomical measurements. Based on the close relationship between sex chromosome genes and facial characterization, craniofacial hard tissues morphology shows sex dimorphism. In order to establish a more labor-saving, rapid, and accurate reference for sex estimation, the study investigated a deep learning network-based artificial intelligence (AI) model using orthopantomograms (OPG) to estimate sex in northern Chinese subjects. In total, 10703 OPG images were divided into training (80%), validation (10%), and test sets (10%). At the same time, different age thresholds were selected to compare the accuracy differences between adults and minors. The accuracy of sex estimation using CNN (convolutional neural network) model was higher for adults (90.97%) compared with minors (82.64%). This work demonstrated that the proposed model trained with a large dataset could be used in automatic morphological sex-related identification with favorable performance and practical significance in forensic science for adults in northern China, while also providing a reference for minors to some extent.

[Clinical Anslysis of Primary Adrenal NK/T-Cell Lymphoma].

OBJECTIVE: To investigate the clinical characteristics, diagnosis, and treatment of one patient with primary adrenal natural killer/T-cell lymphoma (PANKTCL), and to strengthen the understanding of this rare type of lymphoma. METHODS: The clinical manifestations, diagnosis and treatment process, and prognosis of the patient admitted in our hospital were retrospectively analyzed. RESULTS: Combined with pathology, imaging, bone marrow examination， etc, the patient was diagnosed with PANKTCL (CA stage, stage II; PINK-E score 3, high-risk group). Six cycles of "P-GemOx+VP-16" regimen（gemcitabine 1 g/m2 d1 + oxaliplatin 100 mg/m2 d 1 + etoposide 60 mg/m2 d 2-4 + polyethylene glycol conjugated asparaginase 3 750 IU d 5） was performed, and complete response was assessed in 4 cycles. Maintenance therapy with sintilimab was administered after the completion of chemotherapy. Eight months after the complete response, the patient experienced disease recurrence and underwent a total of four courses of chemotherapy, during which hemophagocytic syndrome occurred. The patient died of disease progression 1 month later. CONCLUSION: PANKTCL is rare, relapses easily, and has a worse prognosis. The choice of the "P-GemOx+VP-16" regimen combined with sintilimab help to improve the survival prognosis of patient with non-upper aerodigestive tract natural killer /T-cell lymphoma.

Risk factors and digital interventions for anxiety disorders in college students: Stakeholder perspectives.

The worldwide prevalence of anxiety disorders among college students is high, which negatively affects countries, schools, families, and individual students to varying degrees. This paper reviews the relevant literature regarding risk factors and digital interventions for anxiety disorders among college students from the perspectives of different stakeholders. Risk factors at the national and societal levels include class differences and the coronavirus disease 2019 pandemic. College-level risk factors include the indoor environment design of the college environment, peer relationships, student satisfaction with college culture, and school functional levels. Family-level risk factors include parenting style, family relationship, and parental level of education. Individual-level risk factors include biological factors, lifestyle, and personality. Among the intervention options for college students' anxiety disorders, in addition to traditional cognitive behavioral therapy, mindfulness-based interventions, psychological counseling, and group counseling, digital mental health interventions are increasingly popular due to their low cost, positive effect, and convenient diagnostics and treatment. To better apply digital intervention to the prevention and treatment of college students' anxiety, this paper suggests that the different stakeholders form a synergy among themselves. The nation and society should provide necessary policy guarantees, financial support, and moral and ethical supervision for the prevention and treatment of college students' anxiety disorders. Colleges should actively participate in the screening and intervention of college students' anxiety disorders. Families should increase their awareness of college students' anxiety disorders and take the initiative to study and understand various digital intervention methods. College students with anxiety disorders should actively seek psychological assistance and actively accept and participate in digital intervention projects and services. We believe that in the future, the application of methods such as big data and artificial intelligence to improve digital interventions and provide individualized treatment plans will become the primary means of preventing and treating anxiety disorders among college students.

Nutritional qualities and antioxidant activity of Pleurotus floridanus grown on composted peach sawdust substrate with different composting time.

Recently, composting cultivation method is widely used in oyster mushroom production. In this study, we focused on the effects of composting processes on nutritional qualities and antioxidant activity of Pleurotus floridanus mushroom fruiting bodies. Three treatments of different composting time (2, 4, and 5 days) were performed with an atmospheric sterilization treatment as the control. The results showed that the pH value, total carbon content, and total nitrogen content of substrate were critical parameters which would significantly affect mushroom qualities and bioactivities. Fruiting bodies of the control demonstrated significantly higher crude protein content, total amino acid content, and essential amino acid content than that of composting treatments. Moreover, fruiting bodies of treatment D4 and D5 manifested significantly higher crude polysaccharide contents. Crude polysaccharide of treatment D4 represented the highest scavenging ability toward both radical 3-ethylbenzthiazoline-6-sulfonic acid (ABTS·+ ) and Hydroxyl radical (OH·). It suggests that composting processes is suitable for oyster mushroom cultivation based on nutritional and antioxidant qualities of fruiting bodies.

Engineering Rich Active Sites and Efficient Water Dissociation for Ni-Doped MoS2/CoS2 Hierarchical Structures toward Excellent Alkaline Hydrogen Evolution.

Besides improving charge transfer, there are two key factors, such as increasing active sites and promoting water dissociation, to be deeply investigated to realize high-performance MoS2-based electrocatalysts in alkaline hydrogen evolution reaction (HER). Herein, we have demonstrated the synergistic engineering to realize rich unsaturated sulfur atoms and activated O-H bonds toward the water for Ni-doped MoS2/CoS2 hierarchical structures by an approach to Ni doping coupled with in situ sulfurizing for excellent alkaline HER. In this work, the Ni-doped atoms are evolved into Ni(OH)2 during alkaline HER. Interestingly, the extra unsaturated sulfur atoms will be modulated into MoS2 nanosheets by breaking Ni-S bonds during the formation of Ni(OH)2. On the other hand, the higher the mass of the Ni precursor (mNi) for the fabrication of our samples, the more Ni(OH)2 is evolved, indicating a stronger ability for water dissociation of our samples during alkaline HER. Our results further reveal that regulating mNi is crucial to the HER activity of the as-synthesized samples. By regulating mNi to 0.300 g, a balance between increasing active sites and promoting water dissociation is achieved for the Ni-doped MoS2/CoS2 samples to boost alkaline HER. Consequently, the optimal samples present the highest HER activity among all counterparts, accompanied by reliable long-term stability. This work will promise important applications in the field of electrocatalytic hydrogen evolution in alkaline environments.

Influencing factors, prediction and prevention of depression in college students: A literature review.

The high prevalence of depression among college students has a strong negative impact on individual physical and mental health, academic development, and interpersonal communication. This paper reviewed the extant literature by identifying nonpathological factors related to college students' depression, investigating the methods of predicting depression, and exploring nonpharmaceutical interventions for college students' depression. The influencing factors of college students' depression mainly fell into four categories: biological factors, personality and psychological state, college experience, and lifestyle. The outbreak of coronavirus disease 2019 has exacerbated the severity of depression among college students worldwide and poses grave challenges to the prevention and treatment of depression, given that the coronavirus has spread quickly with high infection rates, and the pandemic has changed the daily routines of college life. To predict and measure mental health, more advanced methods, such as machine algorithms and artificial intelligence, have emerged in recent years apart from the traditional commonly used psychological scales. Regarding nonpharmaceutical prevention measures, both general measures and professional measures for the prevention and treatment of college students' depression were examined in this study. Students who experience depressive disorders need family support and personalized interventions at college, which should also be supplemented by professional interventions such as cognitive behavioral therapy and online therapy. Through this literature review, we insist that the technology of identification, prediction, and prevention of depression among college students based on big data platforms will be extensively used in the future. Higher education institutions should understand the potential risk factors related to college students' depression and make more accurate screening and prevention available with the help of advanced technologies.

Hormone-response transcriptional landscapes of wheat seedlings and the development of a JA fluorescent reporter.

Wheat is an essential energy and protein source for humans. Climate change brings daunting challenges to wheat yield through environmental stresses, in which phytohormones play critical roles. Nevertheless, the comprehensive understanding of wheat phytohormone responses remains elusive. Here, we investigated the transcriptome response of wheat seedlings to five phytohormones, cytokinin (6-BA), abscisic acid (ABA), gibberellic acid (GA), jasmonate (JA) and salicylic acid (SA). We further selected two JA marker genes and cloned their promoters to drive the expression of 3XEGFP (tandem trimeric enhanced green fluorescent protein) in transgenic lines. The JA fluorescent reporter displayed a fast and stable response to JA treatment as an ideal tool to follow JA dynamics during fungal and cold stresses at a cellular resolution. Overall, this study provided a transcriptional landscape and facilitated generating fluorescent reporters to monitor the dynamics of phytohormones in food crops.

Impacts of composting duration on physicochemical properties and microbial communities during short-term composting for the substrate for oyster mushrooms.

A short-term composting process to prepare substrate is an effective way to cultivate oyster mushrooms (Pleurotus spp.), which can increase the yield of mushrooms and lower the rate of contamination in non-industrialized cultivation. Moreover, it is different from the traditional composting processes for fertilizers and lacks systematic study, such as microbial succession and compost quality. In this study, a series of different tests of composting duration (0, 2, 4 and 5 d) were performed. A composting duration of 4-5 d over 58 °C was suitable for mushroom cultivation based on the biological efficiency (BE) range of 69.76-73.41 % and the contamination rate of 0 %. The content of total carbon (TC) continuously decreased during composting, while the content of total nitrogen (TN) reacted in an opposite matter. The final TN and C/N ratios were 1.89 % and 28/1, respectively, which fell well within the optimal range of nutritional requirements for oyster mushroom cultivation. The composting bacteria were more diverse than the fungal species. Caldibacillus, Thermobispora, Thermopolyspora, Thermobacillus and Ureibacillus were the predominant bacterial genera during the thermophilic stage. Co-occurrence patterns of microbial communities and physicochemical properties were performed using a network analysis, which indicated that bacteria can play more efficient roles than fungi in the degradation of organic matter. The structural equation model showed that composting duration significantly affected bacterial diversity, lignocellulose degradation rates, and BE. The correlations between bioinformatics parameters with composting characters and agronomic traits were determined by the Mantel test and showed that the induction of bacterial diversity over time rapidly activated carbon metabolism during short-term composting. This study provides a new idea of agro-waste composting for mushroom cultivation.

Treatment with anacardic acid modulates dendritic cell activation and alleviates the disease development of autoimmune neuroinflammation in mice.

Anacardic acid (AA) is a phenolic acid extract found in a number of plants, crops, and fruits. It exhibits a wide range of biological activities. This study displayed that AA effectively alleviated EAE, a classical mouse model of multiple sclerosis. AA administered to the EAE greatly decreased inflammatory cell infiltration to the CNS and protected the myelin integrity in the white matter of the spinal cord. AA could block lipopolysaccharide-induced DC activation. inhibited the polarization of 2D2 mice-derived T cells by inhibiting the DCs activity. Immunoblot results indicated that the phosphorylation of NF-κB is significantly suppressed in AA-treated DCs. This work displayed that AA possessed a potential anti-inflammatory therapeutic effect for the treatment of autoimmune disease.

Engineered extracellular vesicles encapsulated Bryostatin-1 as therapy for neuroinflammation.

Targeted and effective drug delivery to central nervous system (CNS) lesions is a major challenge in the treatment of multiple sclerosis (MS). Extracellular vesicles (EVs) have great promise as a drug delivery nanosystem given their unique characteristics, including a strong cargo-loading capacity, low immunogenicity, high biocompatibility, inherent stability, high delivery efficiency, ease of manipulation, and blood-brain barrier (BBB) penetration. Clinical applications are, however, limited by their insufficient targeting capability and "dilution effects" upon systemic administration. Neural stem cells (NSCs) provide an abundant source of EVs because of their remarkable capacity for self-renewal. Here, we developed a novel therapeutic strategy for local delivery and treatment using EVPs, which are derived from NSCs with the expression of the CNS lesion targeting ligand-PDGFRα. Furthermore, we used EVPs as a targeting carrier for encapsulating Bryostatin-1 (Bryo-1), a natural compound with remarkable anti-inflammation ability. Our data showed that Bryo-1 delivered by EVPs was more stable and concentrated in the CNS than native Bryo-1. Systemic injection of a low dosage (1 × 108 particles) of EVPs + Bryo-1, versus only EVPs or Bryo-1 administration, significantly ameliorated clinical disease development, decreased the infiltration of pro-inflammatory cells, blocked myelin loss and astrogliosis, protected BBB integrity, and altered microglia pro-inflammatory phenotype in the CNS of EAE mice. Taken as a whole, our study showed that engineered EVs have a CNS targeting capacity, and it provides potentially powerful therapeutic effects for the treatment of various neuroinflammatory diseases.