Inflammation is a beneficial and physiological process, but there are a number of inflammatory diseases which have detrimental effects on the body. In addition, the drugs used to treat inflammation have toxic side effects when used over a long period of time. Mesenchymal stem cells (MSCs) are pluripotent stem cells that can be isolated from a variety of tissues and can be differentiate into diverse cell types under appropriate conditions. They also exhibit noteworthy anti-inflammatory properties, providing new options for the treatment of inflammatory diseases. The therapeutic potential of MSCs is currently being investigated for various inflammatory diseases, such as kidney injury, lung injury, osteoarthritis (OA), rheumatoid arthritis (RA), and inflammatory bowel disease (IBD). MSCs can perform multiple functions, including immunomodulation, homing, and differentiation, to enable damaged tissues to form a balanced inflammatory and regenerative microenvironment under severe inflammatory conditions. In addition, accumulated evidence indicates that exosomes from extracellular vesicles of MSCs (MSC-Exos) play an extraordinary role, mainly by transferring their components to recipient cells. In this review, we summarize the mechanism and clinical trials of MSCs and MSC-Exos in various inflammatory diseases in detail, with a view to contributing to the treatment of MSCs and MSC-Exos in inflammatory diseases.

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Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower.

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