Investigation the behavior of different fullerenes on graphene surface.

In the present study, the regime of motion of fullerene molecules on graphene substrate in a specific temperature range is investigated. The potential energy of fullerene molecules is analyzed using classical molecular dynamics methods. Fullerene molecules C36, C50, C60, C76, C80, and C90 are selected due to spherical shapes of different sizes and good motion performance in previous studies. Analysis of the motion regime at different temperatures is one of the main objectives of this study. To achieve this aim, the translational and rotational movements of fullerene molecules are studied independently. In the first step of the investigation, Lennard-Jone's potential energy of fullerene molecules is calculated. Subsequently, the motion regime of different fullerenes is classified based on their displacement and diffusion coefficient. Findings indicate C60 is not appropriate in all conditions. However, C90 and C76 molecules are found to be appropriate candidates in most cases in different conditions. As far as a straight-line movement is considered, the deviation of fullerene molecules is compared by their angular velocities. Although C60 has a lower angular velocity due to its symmetrical shape, it may not move well due to its low diffusion coefficient. Overall, our study helps to understand the performance of different fullerene molecules on graphene substrate and find their possible applications, especially as wheels in nanomachine or nanocarrier structures.

The effects of chitosan-loaded JQ1 nanoparticles on OVCAR-3 cell cycle and apoptosis-related gene expression.

Background and purpose: Ovarian cancer is the deadliest gynecological cancer. Bromodomain and extra terminal domain (BET) proteins play major roles in the regulation of gene expression at the epigenetic level. Jun Qi (JQ1) is a potent inhibitor of BET proteins. Regarding the short half-life and poor pharmacokinetic profile, JQ1 was loaded into newly developed nano-carriers. Chitosan nanoparticles are one of the best and potential polymers in cancer treatment. The present study aimed to build chitosan-JQl nanoparticles (Ch-J-NPs), treat OVCAR-3 cells with Ch-J-NPs, and evaluate the effects of these nanoparticles on cell cycle and apoptosis-associated genes. Experimental approach: Ch-J-NPs were synthesized and characterized. The size and morphology of Ch-J-NPs were defined by DLS and FE-SEM techniques. OVCAR-3 cells were cultured and treated with Ch-J-NPs. Then, IC50 was measured using MTT assay. The groups were defined and cells were treated with IC50 concentration of Ch-J-NPs, for 48 h. Finally, cells in different groups were assessed for the expression of genes of interest using quantitative RT-PCR. Findings/Results: IC50 values for Ch-J-NPs were 5.625 µg/mL. RT-PCR results demonstrated that the expression of genes associated with cell cycle activity (c-MYC, hTERT, CDK1, CDK4, and CDK6) was significantly decreased following treatment of cancer cells with Ch-J-NPs. Conversely, the expression of caspase-3, and caspase-9 significantly increased. BAX (pro-apoptotic) to BCL2 (anti-apoptotic) expression ratio, also increased significantly after treatment of cells with Ch-J-NPs. Conclusion and implications: Ch-J-NPs showed significant anti-cell cyclic and apoptotic effects on OVCAR-3 cells.

Engineering biomimetic scaffolds for bone regeneration: Chitosan/alginate/polyvinyl alcohol-based double-network hydrogels with carbon nanomaterials.

In this study, new types of hybrid double-network (DN) hydrogels composed of polyvinyl alcohol (PVA), chitosan (CH), and sodium alginate (SA) are introduced, with the hypothesis that this combination and incorporating multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) will enhance osteogenetic differentiation and the structural and mechanical properties of scaffolds for bone tissue engineering applications. Initially, the impact of varying mass ratios of the PVA/CH/SA mixture on mechanical properties, swelling ratio, and degradability was examined. Based on this investigation, a mass ratio of 4:6:6 was determined to be optimal. At this ratio, the hydrogel demonstrated a Young's modulus of 47.5 ± 5 kPa, a swelling ratio of 680 ± 6 % after 3 h, and a degradation rate of 46.5 ± 5 % after 40 days. In the next phase, following the determination of the optimal mass ratio, CNTs and GNPs were incorporated into the 4:6:6 composite resulting in a significant enhancement in the electrical conductivity and stiffness of the scaffolds. The introduction of CNTs led to a notable increase of 36 % in the viability of MG63 osteoblast cells. Additionally, the inhibition zone test revealed that GNPs and CNTs increased the diameter of the inhibition zone by 49.6 % and 52.6 %, respectively.

In-silico study of drug delivery to atherosclerosis in the human carotid artery using metal-organic frameworks based on adhesion of nanocarriers.

This study investigates nanocarriers (NCs) for drug delivery targeting carotid artery atherosclerosis. This targeted drug delivery mechanism is based on ligand-receptor bindings facilitated by coating NCs with P-selectin aptamers, which exhibit high affinities for P-selectin plaque receptors. Recognizing the significant advantages of metal-organic frameworks (MOFs), such as their high drug-loading percentages, we chose them as nanocarriers for this research. Our evaluation considers critical factors: NC surface density (the number of attached nanocarriers per unit of plaque area), toxicity (percentage of NCs missing the target), and efficient drug transfer to plaque tissue. Employing molecular dynamics (MD) for drug loading calculations via van der Waals interactions and computational fluid dynamics (CFD) for toxicity, surface density, and drug transfer assessments, we achieve a comprehensive analysis. A cardiac cycle-based metric guides optimal MOF release conditions, establishing an ideal dosage of 600 NCs per cycle. MOF-801 exhibits outstanding drug delivery performance, particularly in plaque targeting. While a magnetic field enhances NC adhesion, its impact on drug transfer is limited, emphasizing the need for further optimization in magnetic targeting for NC-based therapies. This study provides crucial insights into NC drug delivery performance in carotid artery atherosclerosis, advancing the field of targeted drug delivery for atherosclerosis treatment.

Valproic acid effects on human adipose-derived stem cell differentiation into oligodendrocytes and improved remyelination in a mouse model of Multiple Sclerosis.

Valproic acid (VPA), a neuroprotective agent and inhibitor of GSK3-ß, along with human Adipose-Derived Stem Cells (hADSCs) have been proposed to be potential therapeutic agents for neurodegenerative disorders. In the present study, we have assessed the effects of VPA alone or in combination with hADSCs on oligodendrocyte differentiation, remyelination, and functional recovery in a mouse model of Multiple Sclerosis (MS). These MS-model mice were randomly divided into cuprizone, sham, VPA, hADSC, and VPA/hADSC groups, with 10 mice considered a control group (healthy mice). The hanging wire test was used to measure motor behavior. To estimate the level of myelination, we performed toluidine blue staining and immunofluorescent staining for OLIG2 and MOG-positive cells. Real-time PCR was used to evaluate the expression of ß-catenin, human and mouse Mbp, Mog, and Olig2 genes. Remyelination and motor function improved in mice receiving VPA, hADSCs, and especially VPA/hADSCs compared to the Cup and Sham groups (P < 0.01). Additionally, the number of MOG and OLIG2 positive cells significantly increased in the VPA/hADSCs group compared to the Cup and Sham groups (P < 0.01). The expression of ß-catenin, myelin and the other oligodendrocyte-specific genes was significantly higher in the VPA recipient groups. Valproic acid can enhance the differentiation of stem cells into oligodendrocytes, making it a potential candidate for MS treatment.

microRNA-184 in the landscape of human malignancies: a review to roles and clinical significance.

MicroRNAs (miRNAs) are a class of non-coding RNAs (ncRNAs) with a short length of 19-22 nucleotides. miRNAs are posttranscriptional regulators of gene expression involved in various biological processes like cell growth, apoptosis, and angiogenesis. miR-184 is a well-studied miRNA, for which most studies report its downregulation in cancer cells and tissues and experiments support its role as a tumor suppressor inhibiting malignant biological behaviors of cancer cells in vitro and in vivo. To exert its functions, miR-184 affects some signaling pathways involved in tumorigenesis like Wnt and ß-catenin, and AKT/mTORC1 pathway, oncogenic factors (e.g., c-Myc) or apoptotic proteins, such as Bcl-2. Interestingly, clinical investigations have shown miR-184 with good performance as a prognostic/diagnostic biomarker for various cancers. Additionally, exogenous miR-184 in cell and xenograft animal studies suggest it as a therapeutic anticancer target. In this review, we outline the studies that evaluated the roles of miR-184 in tumorigenesis as well as its clinical significance.

The role of microRNAs in the pathophysiology of human central nervous system: A focus on neurodegenerative diseases.

microRNAs (miRNAs) are suggested to play substantial roles in regulating the development and various physiologic functions of the central nervous system (CNS). These include neurogenesis, cell fate and differentiation, morphogenesis, formation of dendrites, and targeting non-neural mRNAs. Notably, deregulation of an increasing number of miRNAs is associated with several neurodegenerative diseases including Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis and CNS tumors. They are particularly known to affect the amyloid ß (Aß) cleavage and accumulation, tau protein homeostasis, and expression of alpha-synuclein (α-syn), Parkin, PINK1, and brain-derived neurotrophic factor (BDNF) that play pivotal roles in the pathogenesis of neurodegenerative diseases. These include miR-16, miR-17-5p, miR-20a, miR-106a, miR-106b, miR-15a, miR-15b, miR-103, miR-107, miR-298, miR-328, miR-195, miR-485, and miR-29. In CNS tumors, several miRNAs, including miR-31, miR-16, and miR-21 have been identified to modulate tumorigenesis through impacting tumor invasion and apoptosis. In this review article, we have a look at the recent advances on our knowledge about the role of miRNAs in human brain development and functions, neurodegenerative diseases, and their clinical potentials.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

BACKGROUND: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.

Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review.

BACKGROUND: The collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated, such as by the General Data Protection Regulation (GDPR). These regulations mandate strict data security and data protection and, thus, create major challenges for collecting and using large data sets. Technologies such as federated learning (FL), especially paired with differential privacy (DP) and secure multiparty computation (SMPC), aim to solve these challenges. OBJECTIVE: This scoping review aimed to summarize the current discussion on the legal questions and concerns related to FL systems in medical research. We were particularly interested in whether and to what extent FL applications and training processes are compliant with the GDPR data protection law and whether the use of the aforementioned privacy-enhancing technologies (DP and SMPC) affects this legal compliance. We placed special emphasis on the consequences for medical research and development. METHODS: We performed a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). We reviewed articles on Beck-Online, SSRN, ScienceDirect, arXiv, and Google Scholar published in German or English between 2016 and 2022. We examined 4 questions: whether local and global models are "personal data" as per the GDPR; what the "roles" as defined by the GDPR of various parties in FL are; who controls the data at various stages of the training process; and how, if at all, the use of privacy-enhancing technologies affects these findings. RESULTS: We identified and summarized the findings of 56 relevant publications on FL. Local and likely also global models constitute personal data according to the GDPR. FL strengthens data protection but is still vulnerable to a number of attacks and the possibility of data leakage. These concerns can be successfully addressed through the privacy-enhancing technologies SMPC and DP. CONCLUSIONS: Combining FL with SMPC and DP is necessary to fulfill the legal data protection requirements (GDPR) in medical research dealing with personal data. Even though some technical and legal challenges remain, for example, the possibility of successful attacks on the system, combining FL with SMPC and DP creates enough security to satisfy the legal requirements of the GDPR. This combination thereby provides an attractive technical solution for health institutions willing to collaborate without exposing their data to risk. From a legal perspective, the combination provides enough built-in security measures to satisfy data protection requirements, and from a technical perspective, the combination provides secure systems with comparable performance with centralized machine learning applications.

Investigating the correlation between the protein adhesion simulation and the biocompatibility of polymeric substrate for skin-tissue-engineering applications.

Investigating the protein adhesion properties of polymeric scaffolds through computational simulations can predict the biocompatibility of scaffolds before an experimental assay is carried out. This prediction can be highly beneficial since it can cut costs and the time it takes for experimental assays. The current study aims to test the hypothesis that there is a correlation between the biocompatibility of a composite scaffold and the molecular dynamics simulations of protein adhesion. To this end, chitosan and gelatin were selected for fabricating a composite skin-tissue wound scaffold with five different polymer ratios. This polymeric blend has not been simulated for protein adhesion. The cell proliferation and viability of the samples were quantified via MTT assay using fibroblast cells. Then a series of molecular dynamics simulations were performed to measure the adhesion energy of two prominent extracellular matrix proteins - fibronectin, and collagen type I. Besides, a higher gelatin percentage in the scaffold leads to a decrease in the porosity. The results demonstrated a strong correlation between the experimental data and molecular dynamics simulations. The sample with equal amounts of chitosan and gelatin had the highest cell viability and the strongest adhesion energy, of 239 kcal mol-1 for collagen type I, and 149 kcal mol-1 for fibronectin. This correlation was also evident in other samples: samples with gelatin-to-chitosan ratios of 3 : 1 and 1 : 3 had the lowest cell viability and the weakest adhesion energy, respectively.

Evaluation of in vivo lithium chloride effects as a GSK3-ß inhibitor on human adipose derived stem cells differentiation into oligodendrocytes and re-myelination in an animal model of multiple sclerosis.

BACKGROUND: The application of neuroprotective agents in combination with stem cells is considered a potential effective treatment for multiple sclerosis (MS). Therefore, the effects of lithium chloride as a neuroprotective agent and a GSK3-ß inhibitor were evaluated in combination with human adipose derived stem cells on re-myelination, oligodendrocyte differentiation, and functional recovery. METHODS: After inducing a mouse model of MS and proving it by the hanging wire test, the mice were randomly assigned to five experimental groups: Cup, Sham, Li, hADSC, and Li + hADSC. Additionally, a control group with normal feeding was considered. Finally, toluidine blue staining was carried out to estimate the level of myelination. Furthermore, immunofluorescent staining was used to evaluate the mean of OLIG2 and MOG positive cells. The mRNA levels of ß-Catenin, myelin and oligodendrocyte specific genes were determined via the Real-Time PCR. RESULTS: The results of the hanging wire test and toluidine blue staining showed a significant increase in myelin density and improvements in motor function in groups, which received lithium and stem cells, particularly in the Li + hADSC group compared with the untreated groups (P < 0.01). Moreover, immunostaining results indicated that the mean percentages of MOG and OLIG2 positive cells were significantly higher in the Li + hADSC group than in the other groups (P < 0.01). Finally, gene expression studies indicated that the use of lithium could increase the expression of ß-Catenin, myelin and oligodendrocyte specific genes. CONCLUSION: The use of Lithium Chloride can increase stem cells differentiation into oligodendrocytes and improve re-myelination in MS.

Investigation of the motion of fullerene-wheeled nano-machines on thermally activated curved gold substrates.

The current study presents one of the first investigations in which the simultaneous effect of the curved gold substrates and temperature changes on C60 and C60-wheeled nano-machines' migration was evaluated. For this aim, the cylindrical and concave substrates with different radii were chosen to attain the size of the most appropriate substrate for nano-machines. Results indicated that the chassis' flexibility substantially affected the nanocar's mobility. Nano-machines' deviation from their desired direction was adequately restricted due to selected substrate geometries (The cylindrical and concave). Besides, for the first time, the effect of the substrate radius changes on nano-machine's motion has been investigated. Our findings revealed that adjusting the value of radius results in a long-range movement for nano-machines as well as a sufficient amount of diffusion coefficient even at low temperatures ([Formula: see text] or [Formula: see text]). As a result, the aforementioned substrates could be utilized as the optimized geometries for C60 and nanocar at all temperatures. At the same time, the nanotruck displayed an appropriate performance merely on the small cylindrical substrate ([Formula: see text]) at high temperatures ([Formula: see text] and [Formula: see text]).

Investigation of fullerene motion on thermally activated gold substrates with different shapes.

In the current study, the regime of motion of fullerene molecules on substrates with different shapes at a range of specific temperatures has been investigated. To do so, the potential energy of fullerene molecules was analyzed using the classical molecular dynamics method. C20, C36, C50, C60, C72, C76, C80, and C90 fullerene molecules were selected due to their spherical shapes with different sizes. In addition, to completely analyze the behavior of these molecules, different gold substrates, including flat, concave, the top side of the step (upward step), and the downside of the step (downward step) substrates, were considered. Specifying the regime of the motion at different temperatures is one of the main goals of this study. For this purpose, we have studied the translational and rotational motions of fullerene molecules independently. In the first step of the investigation, Lennard-Jones potential energy of fullerene molecules was calculated. Subsequently, the regime of motion of different fullerenes has been classified, based on their displacement and sliding velocity. Our findings indicated that C60 is appropriate in less than [Formula: see text] of the conditions. However, C20, C76 and C80 molecules were found to be appropriate candidates in most cases in different conditions while they were incompetent only in seven situations. As far as a straight-line movement is considered, the concave geometry demonstrated a better performance compared to the other substrates. In addition, C72 indicated less favorable performance concerning the range of movement and diffusion coefficients. All in all, our investigation helps to understand the performance of different fullerene molecules on gold substrates and find their probable application, especially as a wheel in nano-machine structures.

Privacy-aware multi-institutional time-to-event studies.

Clinical time-to-event studies are dependent on large sample sizes, often not available at a single institution. However, this is countered by the fact that, particularly in the medical field, individual institutions are often legally unable to share their data, as medical data is subject to strong privacy protection due to its particular sensitivity. But the collection, and especially aggregation into centralized datasets, is also fraught with substantial legal risks and often outright unlawful. Existing solutions using federated learning have already demonstrated considerable potential as an alternative for central data collection. Unfortunately, current approaches are incomplete or not easily applicable in clinical studies owing to the complexity of federated infrastructures. This work presents privacy-aware and federated implementations of the most used time-to-event algorithms (survival curve, cumulative hazard rate, log-rank test, and Cox proportional hazards model) in clinical trials, based on a hybrid approach of federated learning, additive secret sharing, and differential privacy. On several benchmark datasets, we show that all algorithms produce highly similar, or in some cases, even identical results compared to traditional centralized time-to-event algorithms. Furthermore, we were able to reproduce the results of a previous clinical time-to-event study in various federated scenarios. All algorithms are accessible through the intuitive web-app Partea (https://partea.zbh.uni-hamburg.de), offering a graphical user interface for clinicians and non-computational researchers without programming knowledge. Partea removes the high infrastructural hurdles derived from existing federated learning approaches and removes the complexity of execution. Therefore, it is an easy-to-use alternative to central data collection, reducing bureaucratic efforts but also the legal risks associated with the processing of personal data to a minimum.

The effects of high fructose fruits and honey on the serum level of metabolic factors and nonalcoholic fatty liver disease.

INTRODUCTION: The effect of the natural sources of fructose such as high fructose fruits and honey on the risk of fatty liver is still challenging. This study aimed to compare the effect of fructose, high fructose fruits, and honey on the metabolic factors and non-alcoholic fatty liver disease (NAFLD). METHODS: Forty-four rats were divided into four groups including normal diet group, high fructose group (HF), high fructose fruits group (HFF), and honey group (HO). After 120 days of intervention, the levels of insulin resistance, hepatic enzyme, and lipid profile were measured. Also, the expression levels of the acetyl-coA carboxylase (ACC), sterol regulatory element-binding protein 1c (SREBP-1c), Interleukin 6 (IL-6), and transforming growth factor-beta (TGF-ß) genes were assessed. In addition, a histopathologic assessment was performed on liver tissues. RESULTS: Insulin resistance (IR) increased significantly in the HF, HFF, and HO groups (All P < 0.05). The levels of liver enzymes was significantly increased only in the group receiving the HF regimen (P < 0.01). A significant decrease in total cholesterol and HDL-C (high density lipoprotein cholesterol) levels was found in HO group compared to the control group (P < 0.05). The expression levels of ACC and SREBP-1c genes in HF, HFF, and HO groups were significantly higher than the control group (All P < 0.05). The HF group had a greater increase in the level of gene expression of IL-6 and TGF-ß (All P < 0.05). Histopathological assessment did not find any changes in fatty liver formation and inflammatory damage. CONCLUSION: Consumption of fructose-rich honey and fruits improved the status of inflammatory markers and liver enzymes compared with the industrial fructose-rich products.

Flimma: a federated and privacy-aware tool for differential gene expression analysis.

Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange is often restricted by privacy legislation, meta-analyses are frequently employed to pool local results. However, the accuracy might drop if class labels are inhomogeneously distributed among cohorts. Flimma ( https://exbio.wzw.tum.de/flimma/ ) addresses this issue by implementing the state-of-the-art workflow limma voom in a federated manner, i.e., patient data never leaves its source site. Flimma results are identical to those generated by limma voom on aggregated datasets even in imbalanced scenarios where meta-analysis approaches fail.

The effect of applied electric field on the micromorphology of Pt nanoparticles synthesized by laser ablation.

In the present study, laser ablation technique (Nd:YAG) has been applied to synthesize platinum nanoparticles (NPs). Also, the effect of applied electric field on the physical, structural, and morphological properties of Pt NPs has been investigated during the nanosecond pulsed laser ablation of platinum. Based on the results extracted from TEM and scanning electron microscopy images, the formation of high percentage of NPs with spherical shape is demonstrated in all samples. The increase of applied electric field creates few rectangular, hexagonal, and rhombic NPs with the average size decreased from 20 to 9 nm. The significant influence of increasing electric field is also observed in UV-vis spectra by appearing the blue shift of the localized surface plasmon resonance peak. The UV-vis spectra also confirm the metallic nature of Pt NPs and the existence of inhomogeneous-sized particles and the coagulation of particle because of the long tail in higher wavelengths. In addition, atomic force microscopy images have been analyzed through MountainsMap Premium program and fractal dimension. As can be seen, increasing the applied electric field make the surface more irregular and the maximum value of Df reveals the most irregular topography for sample with 50 V/cm electric field. Finally, the bending and stretching frequencies of the functional bending groups connected to the NPs surface have been characterized by Fourier transform infrared spectroscopy. Electrical field-assisted laser ablation in liquids method allows a better control of the size, morphology, structure, and chemical composition of nanoparticles.

Evaluation of Edaravone effects on the differentiation of human adipose derived stem cells into oligodendrocyte cells in multiple sclerosis disease in rats.

AIMS: Among all the treatments for Multiple Sclerosis, stem cell transplantation, such as ADSCs, has attracted a great deal of scientific attention. On the other hand, Edaravone, as an antioxidant component, in combination with stem cells, could increase the survival and differentiation potential of stem cells. MAIN METHODS: 42 rats were divided into: Control, Cuprizone (CPZ), Sham, Edaravone (Ed), hADSCs, and Ed/hADSCs groups. Following induction of cuprizone, induced MS model, behavioral tests were designed to evaluate motor function during. Luxal fast blue staining was done to measure the level of demyelination and remyelination. Immunofluorescent staining was used to evaluate the amount of MBP, OLIG2, and MOG proteins. The mRNA levels of human MBP, MOG, and OLIG2 and rat Mbp, Mog, and Olig2 were determined via RT-PCR. KEY FINDINGS: Flow cytometry analysis exhibited that the extracted cells were positive for CD73 (93.8 ± 3%) and CD105 (91.6 ± 3%), yet negative for CD45 (2.06 ± 0.5%). Behavioral tests, unveiled a significant improvement in the Ed (P < 0.001), hADSCs (P < 0.001), and Ed/hADSCs (P < 0.001) groups compared to the others. In the Ed/hADSCs group, the myelin density was significantly higher than that in the Ed treated and hADSCs treated groups (P < 0.01). Edaravone and hADSCs increased the expression of Mbp, Mog, and Olig2 genes in the cuprizone rat models. Moreover, significant differences were seen between the Ed treated and hADSCs treated groups and the Ed/hADSCs group (P < 0.05 for Mbp and Olig2 and P < 0.01 for Mog). SIGNIFICANCE: Edaravone in combination with hADSCs reduced demyelination and increased oligodendrogenesis in the cuprizone rat models.