The non-myocyte cells of the healthy heart account for more than 60% of the cardiac cells, include cardiac fibroblasts (CFs) and endothelial cells (ECs), and are actively involved in the remodeling process. 110,111 Fibroblasts, which are responsible for the synthesis of ECM components, account Bicalutamide Androgen Receptor inhibitor for approximately 90% of the non-myocyte cell mass. 110,111 In the stressed

myocardium, fibroblasts differentiate into active myofibroblasts upon a wide range of stimuli (e.g. TGF-β). 89,110 These activated cells can regulate the secretion of ECM components and ECM degrading-enzymes (matrix metalloproteinases, MMPs) and tend to proliferate and migrate, acting to remodel the cardiac interstitium. 89 This process may result in cardiac fibrosis, a hallmark of pathological hypertrophy and HF, which presents with aberrant proliferation of CFs and excessive deposition of ECM proteins in the interstitium and perivascular regions of the myocardium, ultimately impairing

cardiac function. 89 Several lines of evidence indicate that dysregulation of miRNAs during HF occurs in CFs, besides CMCs, thereby contributing to the development of cardiac fibrosis. In particular, the increased miR-21 expression observed in human HF, 70 has been attributed mainly to fibroblasts using the TAC mouse model of HF. 84 Specifically, miR-21 is selectively upregulated in the fibroblasts of the failing heart and has been shown to target Spry1, a negative regulator of ERK-MAPK pathway, which functions to enhance growth factor secretion and fibroblast survival, thus promoting interstitial fibrosis. 84 MiR-21 was also found upregulated in CFs of the infarct zone after ischemia-reperfusion in mice, where it was shown to induce MMP2 (an ECM degrading enzyme) via direct targeting of PTEN, but its role in fibrosis was not further investigated in this model. 112

A more recent study by Liang et al revealed additional evidence supporting a role for mir-21 in fibrosis: miR-21 was upregulated in the border zone of murine hearts after MI, whereas the negative regulator of TGFβ, TGFβRIII, was underexpressed. Further experiments in CFs showed that mir-21 overexpression can enhance collagen production, in part through TGFβRIII suppression, and conversely TGFβRIII overexpression can inhibit mir-21 and reduce collagen production in CFs. 113 Taken together, these studies imply that Carfilzomib mir-21 upregulation under pathologic conditions in the myocardium may impair cardiac function by contributing to cardiac fibrosis. The miR-29 family has also been found deregulated in the failing heart and associated with the pathological mediator of fibrosis TGFβ. The members of the miR-29 family (miR-29a, b, c) are mainly expressed in the CFs of the murine heart and have been found downregulated in response to a variety of remodeling-inducing stresses (TAC, chronic calcineurin signaling, MI). In vitro experiments in cultured CFs showed that this reduction in miR-29 levels may be triggered upon TGFβ stimulus.

81172068 P- Reviewer: Bellanti F, Hann HW, Morioka

D S- Editor: Song XX L- Editor: Wang TQ E- Editor: Lu YJ
Core tip: Autoimmune diseases affect approximately 5% of the human population, leading to serious disability and effective methods to treat these diseases are still not perfect. Mesenchymal stem Caspase pathway cells (MSCs) are assumed to be promising agents, both for regenerative medicine and cell therapy for autoimmune disorders. Under the influence of some factors, mesenchymal stem cells secrete cytokines which induce suppression of the immune response. Studies on the secreted cytokines and the precise mechanisms involved in these suppressive mechanisms would create possibilities for efficient application of MSCs as a therapeutic means for treatment of autoimmune diseases. INTRODUCTION Maintenance of immunological self-tolerance and immune homeostasis in the organism is under the control of a complex and sophisticated process of immunoregulation and its dysfunction could be a critical factor in the development of autoreactive and potentially life-threatening conditions. Profound understanding of the precise mechanisms underlying this immunoregulatory process could lay the ground to develop a more suitable and efficient therapy for autoimmune diseases. Regulation

of the immune response by mesenchymal stem cells (MSCs) is mediated by a number of cell subtypes and secreted factors and recently new cell-based therapeutic approaches have emerged as successful strategies for treatment of various inflammatory and autoimmune conditions. In the last decades, mesenchymal stem cells, one type of adult stem cells, have gained considerable interest as extremely promising cell therapeutic

agents[1,2] due to their unique combination of immunomodulatory properties and self-renewal and multilineage differentiation capacity[3,4]. MSCs have been shown to exert profound anti-inflammatory and immunomodulatory effects on almost all the cells of the innate and adaptive Carfilzomib immune systems via a variety of mechanisms, notably cytokine and chemokine secretion[5]. Mesenchymal stem cells are a population of undifferentiated multipotent adult stem cells that naturally reside within the human body and are generally defined as plastic-adherent, fibroblast-like cells possessing extensive self-renewal properties and potential to differentiate in vitro and in vivo into a variety of mesenchymal lineage cells[4,6]. MSCs were initially described in the bone marrow by Friedenstein et al[7,8] as a small subpopulation of colony-forming unit fibroblasts which could be distinguished from the rest of the bone marrow cells on the basis of their plastic adherence, spindle-shaped appearance and rapid expansion[7].

3.1. The Detail Techniques of ACSA (1) Affinity Measure. Affinity of the algorithm is the objective of model, the smaller the better. In order to extend the search space, the algorithm accepts solutions which fail to satisfy the constraints. However, penalty coefficient will be added to the affinity measure. (2) The Design of Antibody. The Everolimus RAD001 length of antibody equals the amount of shippers in I. The antibody codes are in J, and the amount should not exceed the maximum number p. To better understand the design of antibody, a simple example consisting of seven

shippers and four candidate freight transport centers is proposed. p equals three (see Figure 1). Candidate center 3 is not included in the antibody, which means candidate center 3 is not chosen as a transport center. Figure 1 The design of antibody for the optimization model. (3) Mutation Operation. The mutation operation is shown in Figure 2. p equals four. If the amount of chosen candidate centers reaches maximum, randomly choose a code e. Change both e and the codes whose values are the same as e (see Figure 2(a)). Else randomly choose a code e and change its value (see Figure 2(b)). Figure 2 The mutation operation of model M-I. 3.2. Cloud Model (1) Cloud Model. CM is used to transform the qualitative data into quantitative data. A Cloud Drop is a realization of the

qualitative concept; the distribution of Cloud Drops is called Cloud. Three numerical characteristics are used to describe the Cloud; those are expected value Ex, entropy En, and hyper entropy He. The typical CMs are Normal Cloud, Trapezoid Cloud, and Triangle Cloud. If distribution function of Cloud follows the normal distribution, the CM is called Normal Cloud. Three Normal Clouds with different characteristics are shown in Figure 3. Compared the three Clouds, it can be found that the bigger the characteristics are, the more divergent

the Cloud will be. Figure 3 Three examples of the Normal Cloud. The characteristics of Normal Cloud can be got by the following operations: Ex=f¯,En=f¯−fmin⁡c1,He=Enc2, (12) where c1 and c2 are control coefficients. f¯ is the average value of affinities in the group. fi is affinity of the antibody. fmin is the minimum affinity of the antibody. (2) Cloud Generator. Cloud Generator (CG) is the algorithm of CM. The inputs of the generator are the three numerical characteristics. The outputs are Cloud Drops. CG can realize the mapping from qualitative Cilengitide data to quantitative data. There are many CGs such as Forward Cloud Generator, Backward Cloud Generator, X Condition Cloud Generator, and Y Condition Cloud Generator. The Forward Cloud Generator is used to generate Cloud Drops based on the samples which are in set (Ex, En, and He). The Cloud Drops can be got by the following formulas: En′=NORMEn,He2,Qcloud=e−fi−Ex2/2En′2. (13) Q cloud is a Cloud Drop which means the uncertainty degree of the inputs, Qcloud ∈ (0,1). 4. Progress of the Algorithm C-ACSA combines the advantages of CM and ACSA.

k is a constant. According to the assumption, each illegal pedestrian’s behavior

can be observed by kl(t) pedestrians around him. Because the number of pedestrians who are in the watching Imatinib structure state is No(t), there are pkNo(t)i(t) watching pedestrians who will cross the street illegally. As a result, the increasing rate of pedestrian crossing the street illegally is pkNoi: Ndidt=pkNoi. (1) Also, as i(t) + o(t) = 1, at the initial time (t = 0), the proportion of illegal pedestrians is i0; then Ndidt=pkNi1−i,i0=i0. (2) Solve the equation, the results can be it = 1/(1 + (1/i0 − 1)e−pkt). 4.2. Simulation Model of Pedestrian Violation Behavior A

complex system simulation software “NetLogo” is applied to simulate the spread model of pedestrian’s violation crossing behavior. Figure 4 shows the simulation results of the spread model. The red dots represent the pedestrians crossing the street illegally, while the green dots represent the pedestrians waiting for the green light. Through the simulation analysis, the spreading rules of violation behavior in different network structures are obtained. In addition, further analysis is proposed to study the factor of spreading rate in the pedestrian’s crossing behavior in group. Figure 4 (a) Pedestrian violation behavior

spreading trend in the degree of 5. (b) Pedestrian violation behavior spreading trend in the degree of 6. (c) Pedestrian violation behavior spreading trend in the degree of 8. The pedestrian violation behavior spread model based on improved SI is established. In the simulation process, to analyze the influencing factors of the behavior spreading, two key parameters are changed: the average degree of the network and the spreading rate. Spreading rate is set as 10%, and the spreading characteristics of violation behavior are simulated in the Entinostat network when the average degree of the network is 2, 3, 5, 6, and 8. In addition, to analyze the factor of spreading rate, the spreading characteristics of pedestrian violation behavior are simulated when the average degree of the network is 6 and spreading rates are 10% and 15%. 4.2.1. Spreading Characteristics of Violation Behavior in Different Network Structures According to the simulation results, when the average degree of the network is less than 3, illegal behavior could not be spread on the pedestrian network.