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Abstract Cold atmospheric plasma (CAP) technology has emerged as a revolutionary therapeutic technology in dermatology, recognized for its safety, effectiveness, and minimal side effects. CAP demonstrates substantial antimicrobial properties against bacteria, viruses, and fungi, promotes tissue proliferation and wound healing, and inhibits the growth and migration of tumor cells. This paper explores the versatile applications of CAP in dermatology, skin health, and skincare. It provides an in-depth analysis of plasma technology, medical plasma applications, and CAP. The review covers the classification of CAP, its direct and indirect applications, and the penetration and mechanisms of action of its active components in the skin. Briefly introduce CAP’s suppressive effects on microbial infections, detailing its impact on infectious skin diseases and its specific effects on bacteria, fungi, viruses, and parasites. It also highlights CAP’s role in promoting tissue proliferation and wound healing and its effectiveness in treating inflammatory skin diseases such as psoriasis, atopic dermatitis, and vitiligo. Additionally, the review examines CAP’s potential in suppressing tumor cell proliferation and migration and its applications in cosmetic and skincare treatments. The therapeutic potential of CAP in treating immune-mediated skin diseases is also discussed. CAP presents significant promise as a dermatological treatment, offering a safe and effective approach for various skin conditions. Its ability to operate at room temperature and its broad spectrum of applications make it a valuable tool in dermatology. Finally, introduce further research is required to fully elucidate its mechanisms, optimize its use, and expand its clinical applications.

Chinese traditional medicine (CTM) has a long-standing history and plays a crucial role in complementary and alternative medicine. However, ensuring the quality and safety of CTM products has been a persistent concern due to the lack of effective quality control methods. This study addresses this concern by leveraging chemometric models, specifically partial least squares (PLS), support vector machine (SVM), and random forest, in conjunction with near-infrared spectroscopy (NIRS) data. These models are applied to establish a comprehensive quality control framework for ginkgolide production. This framework includes predicting terpenolactones content at three key production stage of ginkgolide product development. The collect extensive NIRS data throughout the ginkgolide production process and develop chemometric models using PLS, SVM, and random forest algorithms. These models are rigorously validated through cross-validation and independent testing to assess their accuracy and precision in predicting chemical content and classifying product stages. The result reveal that the SVM model, when applied to NIRS data, demonstrates outstanding performance in terms of accuracy and precision. It excels in predicting chemical content and effectively classifying the various stages of ginkgolide production.

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Efficient energy conversion and utilization remain paramount in addressing the growing energy demand and environmental concerns. Concentrating photovoltaic thermal (CPVT) systems have emerged as promising solutions by integrating photovoltaic (PV) cells with thermal components for simultaneous electricity and heat generation. In this paper, we propose the application of the Boosted Regression Tree (BRT) algorithm to predict the entropy generation rate in a CPVT system equipped with a perforated twisted tube turbulator. Brief introduction of numerical analysis of local and global rates of frictional (S˙fr) and thermal (S˙th) irreversibilities in a CPVT system equipped with a perforated twisted tube turbulator. The results approve the efficacy of the BRT algorithm in predicting the entropy generation rate. Through comprehensive simulations and data analysis, we establish a predictive model that considers factors such as solar irradiance, fluid flow rate, tube geometry, and turbulator characteristics. The BRT model exhibits remarkable accuracy in capturing the nuanced interplay of these factors, enabling reliable estimations of entropy generation rate.

Medium voltage (MV) switchgear is a vital part of modern power systems, responsible for regulating the flow of electrical power and ensuring the safety of equipment and personnel. However, switchgear can experience various types of faults that can compromise its reliability and safety. Common faults in switchgear include arcing, tracking, corona, normal cases, and mechanical faults. Accurate detection of these faults is essential for maintaining the safety of MV switchgear. In this paper, we propose a novel approach for fault detection using a hybrid model (1D-CNN-LSTM) in both the time domain (TD) and frequency domain (FD). The proposed approach involves gathering a dataset of switchgear operation data and pre-processing it to prepare it for training. The hybrid model is then trained on this dataset, and its performance is evaluated in the testing phase. The results of the testing phase demonstrate the effectiveness of the hybrid model in detecting faults. The model achieved 100% accuracy in both the time and frequency domains for classifying faults in Switchgear, including arcing, tracking, and mechanical faults. Additionally, the model achieved 98.4% accuracy in detecting corona faults in the TD. The hybrid model proposed in this study provides an effective and efficient approach for fault detection in MV switchgear. By learning spatial and temporal features simultaneously, this model can accurately classify faults in both the TD and FD. This approach has significant potential to improve the safety of MV switchgear as well as other industrial applications.

Pesticides play a pivotal role in modern agriculture, but their potential environmental and health impacts necessitate a comprehensive understanding of their interactions with biological molecules. Beta-cyfluthrin, a widely used synthetic pyrethroid insecticide, is known for its efficiency in pest control. However, its interaction with bovine serum albumin (BSA), a crucial transport protein in living organisms, has not been extensively studied. The interaction between beta-cyfluthrin, a prominent synthetic pyrethroid insecticide, and bovine serum albumin (BSA) was comprehensively investigated using fluorescence spectrometry. Furthermore, the influence of ultraviolet (UV) degradation on the interaction parameters was explored, enhancing our understanding of the impact of environmental conditions on this interaction. The Stern–Volmer equation was employed to determine quenching constants, revealing that the fluorescence quenching mechanism primarily involved static quenching. The temperature variations were studied, showing an increase in the binding constant with rising temperature prior to degradation, while post-UV degradation, an inverse correlation between the binding constant and temperature was observed. The thermodynamic parameters were derived through appropriate equations, unveiling the underlying reaction forces. In the absence of degradation, hydrophobic interactions dominated, whereas after UV degradation, interactions shifted to hydrogen bonding and van der Waals forces. The findings elucidate the nuanced effects of UV degradation on the interaction between beta-cyfluthrin and BSA. This study furnishes critical insights that serve as a scientific foundation for pesticide production and application strategies, accounting for the influence of UV degradation on the intricate interplay between pesticides and BSA.

We numerically analyze a polarization maintaining (PM) supercontinuum generation (SCG) in all-normal dispersion liquid (CS2)-core photonic crystal fiber (LC-PCF). The proposed LC-PCF affords a high birefringence (10−3 to 10−2) from 1.0 μm to 2.2 μm wavelength with fundamental mode behavior. The linear polarization and high coherence spectral width for X and Y polarized axes covers 1.32 μm∼2.28 and 1.28 μm∼2.24 μm with pump wavelength of 1.55 μm at pump power of 2 kW, respectively. Furthermore, we found that the ability of maintaining linear polarization state of SC source for X-axes is better than that of Y-axes as the pulse duration varies from 0.05 ps to 0.6 ps at the same pump wavelength. In addition, the proposed LC-PCF-based SC source is a good candidate for applications such as biomedical imaging, fluorescence lifetime imaging fields, and frequency comb sources.

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions.

Wind energy is considered as one of the most prominent sources of energy for sustainable development. This technology is of interest owing to its capability to produce clean, eco-friendly, and cost-effective energy for small-scale users and rural areas where grid power availability is insufficient. Wind power generation has developed rapidly in the past decade and is expected to play a vital role in the economic development of countries. Therefore, studying dominant economic factors is crucial to properly approach public and private financing for this emerging technology, as industrial growth and energy demands may outpace further economic studies earlier than expected. In this study, a strategy-focused method for performing economic analysis on wind energy based on financial net present value, levelized cost of energy, internal rate of return, and investment recovery period is presented. Numerical and simulation results depict the most optimal and economical system from a 3 and a 10 kW wind energy conversion system (WECS). Moreover, the aforementioned criteria are used to determine which WECS range is the most suitable investment with the shortest payback period. Finally, an economically viable and profitable wind energy system is recommended.

Based on ultraviolet absorption spectroscopy technology combined with stoichiometry, a portable photoelectric detection system with wireless transmission was designed with the advantages of simple operation, low cost, and quick response to realize the non-destructive detection of dihydrocoumarin content in coconut juice. Through the detection of a sample solution, the light intensity through the solution is measured and converted into absorbance. Particle swarm optimization (PSO) is applied to optimize support vector regression (SVR) to establish a corresponding concentration prediction model. At the same time, in order to solve the shortcomings of the conventional portable photoelectric detection equipment in data storage, data transmission, and other aspects, based on the optimal PSO-SVR model, we used Python language to develop a friendly graphical user interface (GUI), integrating data collection, storage, analysis, and prediction modeling in one, greatly simplifying the operation process. The experimental results show that, compared with the traditional methods, the system achieves the purpose of rapid and non-destructive detection and has a small gap compared with the detection results of the ultraviolet spectrophotometer. It provides a good method for the determination of dihydrocoumarin in coconut juice.

In an attempt to meet the global demand, renewable energy systems (RES) have gained an interest in it due to the availability of the resources, especially solar photovoltaic system that has been an importance since many years because of per watt cost reduction, improvement in efficiency, and abundant availability. Photovoltaic system in remote and rural areas is very useful where a grid supply is unavailable. In this scenario, power electronic converters are an integral part of the renewable energy systems particularly for electronic devices which are operated from renewable energy sources and energy storage system (fuel cell and batteries). In this article, a new topology of charge pump capacitor (CPC) which is based on high voltage gain technique DC-DC boost converter (DCBC) with dynamic modeling is proposed. To testify the efficacy of the introduced topology, a prototype has been developed in a laboratory, where input was given 10VDC and 80VDC output voltage achieved at the load side. Furthermore, the experimental result shows that the voltage stress of MOSFET switches is very less in comparison with the conventional boost converter with the same parameters as the proposed converter.

This study aims to study the discharging process to verify the influence of geometry modifications and heat transfer flow (HTF) patterns on the performance of a vertical triplex-tube latent heat container. The phase change material (PCM) is included in the middle tube, where the geometry is modified using single or multi-internal frustum tubes instead of straight tubes to enhance the discharging rate. The effects of the HTF flow direction, which is considered by the gravity and opposite-gravity directions, are also examined in four different cases. For the optimal geometry, three scenarios are proposed, i.e., employing a frustum tube for the middle tube, for the inner tube, and at last for both the inner and middle tubes. The effects of various gap widths in the modified geometries are investigated. The results show the advantages of using frustum tubes in increasing the discharging rate and reducing the solidification time compared with that of the straight tube unit due to the higher natural convection effect by proper utilization of frustum tubes. The study of the HTF pattern shows that where the HTF direction in both the inner and outer tubes are in the gravity direction, the maximum discharging rate can be achieved. For the best configuration, the discharge time is reduced negligibly compared with that for the system with straight tubes which depends on the dimensions of the PCM domain.

In this article, the adaptive neuro-fuzzy inference system (ANFIS) and multiconfiguration gas-turbines are used to predict the optimal gas-turbine operating parameters. The principle formulations of gas-turbine configurations with various operating conditions are introduced in detail. The effects of different parameters have been analyzed to select the optimum gas-turbine configuration. The adopted ANFIS model has five inputs, namely, isentropic turbine efficiency (Teff), isentropic compressor efficiency (Ceff), ambient temperature (T1), pressure ratio (rp), and turbine inlet temperature (TIT), as well as three outputs, fuel consumption, power output, and thermal efficiency. Both actual reported information, from Baiji Gas-Turbines of Iraq, and simulated data were utilized with the ANFIS model. The results show that, at an isentropic compressor efficiency of 100% and turbine inlet temperature of 1900 K, the peak thermal efficiency amounts to 63% and 375 MW of power resulted, which was the peak value of the power output. Furthermore, at an isentropic compressor efficiency of 100% and a pressure ratio of 30, a peak specific fuel consumption amount of 0.033 kg/kWh was obtained. The predicted results reveal that the proposed model determines the operating conditions that strongly influence the performance of the gas-turbine. In addition, the predicted results of the simulated regenerative gas-turbine (RGT) and ANFIS model were satisfactory compared to that of the foregoing Baiji Gas-Turbines.

In developing countries, the power production is properly less than the request of power or load, and sustaining a system stability of power production is a trouble quietly. Sometimes, there is a necessary development to the correct quantity of load demand to retain a system of power production steadily. Thus, Small Hydropower Plant (SHP) includes a Kaplan turbine was verified to explore its applicability. This paper concentrates on applying on Artificial Neural Networks (ANNs) by approaching of Feed-Forward, Back-Propagation to make performance predictions of the hydropower plant at the Himreen lake dam-Diyala in terms of net turbine head, flow rate of water and power production that data gathered during a research over a 10 year period. The model studies the uncertainties of inputs and output operation and there's a designing to network structure and then trained by means of the entire of 3570 experimental and observed data. Furthermore, ANN offers an analyzing and diagnosing instrument effectively to model performance of the nonlinear plant. The study suggests that the ANN may predict the performance of the plant with a correlation coefficient (R) between the variables of predicted and observed output that would be higher than 0.96. Keywords: Himreen Lake Dam, Small Hydropower plants, Artificial Neural Networks, Feed forward-back propagation model, Generation system's prediction

Visual object tracking is a fundamental component in many computer vision applications. Extracting robust features of object is one of the most important steps in tracking. As trackers, only formulated on RGB data, are usually affected by occlusions, appearance, or illumination variations, we propose a novel RGB-D tracking method based on genetic feature learning in this paper. Our approach addresses feature learning as an optimization problem. As owning the advantage of parallel computing, genetic algorithm (GA) has fast speed of convergence and excellent global optimization performance. At the same time, unlike handcrafted feature and deep learning methods, GA can be employed to solve the problem of feature representation without prior knowledge, and it has no use for a large number of parameters to be learned. The candidate solution in RGB or depth modality is represented as an encoding of an image in GA, and genetic feature is learned through population initialization, fitness evaluation, selection, crossover, and mutation. The proposed RGB-D tracker is evaluated on popular benchmark dataset, and experimental results indicate that our method achieves higher accuracy and faster tracking speed.

An increase in demand for renewable energy resources, energy storage technologies, and electric vehicles requires high-power level DC-DC converters. The DC-DC converter that is suitable for high-power conversion applications (i.e., resonant, full-bridge or the dual-active bridge) requires magnetic transformer coupling between input and output stage. However, transformer design in these converters remains a challenging problem, with several non-linear scaling issues that need to be simultaneously optimized to reduce losses and maintain acceptable performance. In this paper, a new transformer-less high step-up boost converter with a charge pump capacitorand capacitor-inductor-diode CLD cell is proposed using dynamic modeling. The experimental and simulation results of the proposed converter are carried out in a laboratory and through Matlab Simulink, where 10 V is given as an input voltage, and at the output, 100 V achieved in the proposed converter. A comparative analysis of the proposed converter has also been done with a conventional quadratic converter that has similar parameters. The results suggest that the proposed converter can obtain high voltage gain without operating at the maximum duty cycle and is more efficient than the conventional converter.

The growth of using wind energy on a large scale increases the demand for wind energy conversion machines (WECMs), among these converters, the doubly-fed induction generator (DFIG) is the favorite choice. However, DFIG is very sensitive to wind speed variations and grid faults during operation. In order to overcome these undesirable characteristics, this paper proposes a hierarchical fault tolerant control (FTC) to improve the performance of DFIG. The hierarchical fault tolerant control (FTC) approach consists of pitch angle control (PAC) and maximum power point tracking (MPPT). This hierarchical approach demonstrates the robust response under various (low, rated, and high) wind speed ranges and reduces the undesirable DC voltage overshoots during short-circuit disorder. The simulation results are summarized in a logical table, which depicts the order of controlling scheme and operation for a sustainable energy generation system. The proposed control scheme achieved the healthy and the robust dynamic response without deteriorating the grid power quality or stressing the converters, and approved the effectiveness to suppress the DC voltage overshoots and tolerate the lower down short-circuit disorder to its rated range.

Eddy current testing (ECT) is an accurate, widely used and well-understood inspection technique, particularly in the aircraft and nuclear industries. The coating thickness or lift-off will influence the measurement of defect depth on pipes or plates. It will be an uncertain decision condition whether the defects on a workpiece are cracks or scratches. This problem can lead to the occurrence of pipe leakages, besides causing the degradation of a company’s productivity and most importantly risking the safety of workers. In this paper, a novel eddy current testing error compensation technique based on Mamdani-type fuzzy coupled differential and absolute probes was proposed. The general descriptions of the proposed ECT technique include details of the system design, intelligent fuzzy logic design and Simulink block development design. The detailed description of the proposed probe selection, design and instrumentation of the error compensation of eddy current testing (ECECT) along with the absolute probe and differential probe relevant to the present research work are presented. The ECECT simulation and hardware design are proposed, using the fuzzy logic technique for the development of the new methodology. The depths of the defect coefficients of the probe’s lift-off caused by the coating thickness were measured by using a designed setup. In this result, the ECECT gives an optimum correction for the lift-off, in which the reduction of error is only within 0.1% of its all-out value. Finally, the ECECT is used to measure lift-off in a range of approximately 1 mm to 5 mm, and the performance of the proposed method in non-linear cracks is assessed.

One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.

The reliability of the generating system adequacy is evaluated based on the ability of the system to satisfy the load demand. In this paper, a novel optimization technique named the disparity evolution genetic algorithm (DEGA) is proposed for reliability assessment of power generation. Disparity evolution is used to enhance the performance of the probability of mutation in a genetic algorithm (GA) by incorporating features from the paradigm into the disparity theory. The DEGA is based on metaheuristic searching for the truncated sampling of state-space for the reliability assessment of power generation system adequacy. Two reliability test systems (IEEE-RTS-79 and (IEEE-RTS-96) are used to demonstrate the effectiveness of the proposed algorithm. The simulation result shows the DEGA can generate a larger variety of the individuals in an early stage of the next population generation. It is also able to estimate the reliability indices accurately.

The use of the eddy current technique (ECT) for the non-destructive testing of conducting materials has become increasingly important in the past few years. The use of the non-destructive ECT plays a key role in the ensuring the safety and integrity of the large industrial structures such as oil and gas pipelines. This paper introduce a novel ECT probe design integrated with the distributed ECT inspection system (DSECT) use for crack inspection on inner ferromagnetic pipes. The system consists of an array of giant magneto-resistive (GMR) sensors, a pneumatic system, a rotating magnetic field excitation source and a host PC acting as the data analysis center. Probe design parameters, namely probe diameter, an excitation coil and the number of GMR sensors in the array sensor is optimized using numerical optimization based on the desirability approach. The main benefits of DSECT can be seen in terms of its modularity and flexibility for the use of different types of magnetic transducers/sensors, and signals of a different nature with either digital or analog outputs, making it suited for the ECT probe design using an array of GMR magnetic sensors. A real-time application of the DSECT distributed system for ECT inspection can be exploited for the inspection of 70 mm carbon steel pipe. In order to predict the axial and circumference defect detection, a mathematical model is developed based on the technique known as response surface methodology (RSM). The inspection results of a carbon steel pipe sample with artificial defects indicate that the system design is highly efficient.

Non-destructive eddy current testing (ECT) is widely used to examine structural defects in ferromagnetic pipe in the oil and gas industry. Implementation of giant magnetoresistance (GMR) sensors as magnetic field sensors to detect the changes of magnetic field continuity have increased the sensitivity of eddy current techniques in detecting the material defect profile. However, not many researchers have described in detail the structure and issues of GMR sensors and their application in eddy current techniques for nondestructive testing. This paper will describe the implementation of GMR sensors in non-destructive testing eddy current testing. The first part of this paper will describe the structure and principles of GMR sensors. The second part outlines the principles and types of eddy current testing probe that have been studied and developed by previous researchers. The influence of various parameters on the GMR measurement and a factor affecting in eddy current testing will be described in detail in the third part of this paper. Finally, this paper will discuss the limitations of coil probe and compensation techniques that researchers have applied in eddy current testing probes. A comprehensive review of previous studies on the application of GMR sensors in non-destructive eddy current testing also be given at the end of this paper.

Abstract Due to vincristine sulfate’s (VCR sulfate) toxicity and non-specific targeting, which might adversely damage healthy cells, its clinical application is restricted. In this study, we loaded VCR sulfate on exosomes generated from mesenchymal stem cells (MSCs) to enhance its targeted distribution. Exosomes are able to deliver molecules to specific cells and tissues and have therapeutic potential. In this study, we isolated exosomes from MSCs, and using probe-sonication approach loaded them with VCR sulfate. Using SRB assay, the cytotoxicity of VCR sulfate-Exo was assessed in T47D breast cancer cells, and the results were contrasted with those of free VCR sulfate. Then We labeled markers (CD44+/CD24−) in the cell line to assess the targeting effectiveness of VCR sulfate-Exo using flow cytometry. Our results showed that the cytotoxicity of VCR sulfate-Exo was nearly the same as that of VCR sulfate. Flow cytometry analysis revealed that VRC sulfate-Exo was more effectively targeted to MSCs than free VCR sulfate. Our study shows that loading VCR sulfate to MSCs-derived exosomes can improve their targeted delivery and lessen their side effects. Additional research is required to determine VCR sulfate-Exo’s in vivo effectiveness and safety and improve the loading and delivery strategies.

Abstract Background Multiple sclerosis (MS) is a chronic condition that primarily manifests as demyelination of neuronal axons in the central nervous system, due to the loss or attack of oligodendroglia cells that form myelin. Stem cell therapy has shown promising results for the treatment of MS due to its capability to halt the immune attack, stop apoptosis and axonal degeneration, and differentiate into oligodendrocytes. Stem cell-derived Exosomes (Exosomes) have shown great capabilities for neuronal diseases as they have growth factors, complex sets of miRNA, enzymes, proteins, major peptides, lipids, and macromolecules with anti-inflammatory, angiogenesis, and neurogenesis activities. Methods This study aimed to compare the healing properties of stem cells, against Exosomes for the treatment of an experimentally induced MS dog model. Dog models of MS received either a single treatment of stem cells or a single treatment of Exosomes intrathecally and the treatment process was evaluated clinically, radiologically, histopathologically, and electron microscopy and cerebrospinal fluid analysis. Results showed marked amelioration of the clinical signs in both treated groups compared to the control one, magnetic resonance scans showed the resolution of the hyperintense lesions at the end of the study period, the histopathology and electron microscopy showed marked healing properties and remyelination in treated groups with superiority of the stem cells compared to Exosomes. Conclusions Although stem cell results were superior to Exosomes therapy; Exosomes have proven to be effective and safe important actors in myelin regeneration, and their use in diseases like MS helps to stimulate remyelination.