Effect of mesenchymal stem cells-derived exosomes on tumor microenvironment: Tumor progression versus tumor suppression

Mesenchymal stem cells (MSCs) are multipotent cells with the potential to differentiate into different cell types. Owing to their immunosuppressive and anti-inflammatory properties, they are widely used in regenerative medicine, but they have a dual effect on cancer progression and exert both growth-stimulatory or -inhibitory effects on different cancer types. It has been proposed that these controversial effects of MSC in tumor microenvironment (TME) are mediated by their polarization to proinflammatory or anti-inflammatory phenotype. In addition, they can polarize the immune system cells that in turn influence tumor progression. One of the mechanisms involved in the TME communications is extracellular vesicles (EVs). MSCs, as one of cell populations in TME, produce a large amount of EVs that can influence tumor development. Similar to MSC, MSC-EVs can exert both anti- or protumorigenic effects. In the current study, we will investigate the current knowledge related to MSC role in cancer progression with a focus on the MSC-EV content in limiting tumor growth, angiogenesis, and metastasis. We suppose MSC-EVs can be used as safe vehicles for delivering antitumor agents to TME.     

Three-Dimensional Numerical Model of Cell Morphology during Migration in Multi-Signaling Substrates

Cell Migration associated with cell shape changes are of central importance in many biological processes ranging from morphogenesis to metastatic cancer cells. Cell movement is a result of cyclic changes of cell morphology due to effective forces on cell body, leading to periodic fluctuations of the cell length and cell membrane area. It is well-known that the cell can be guided by different effective stimuli such as mechanotaxis, thermotaxis, chemotaxis and/or electrotaxis. Regulation of intracellular mechanics and cell’s physical interaction with its substrate rely on control of cell shape during cell migration. In this notion, it is essential to understand how each natural or external stimulus may affect the cell behavior. Therefore, a three-dimensional (3D) computational model is here developed to analyze a free mode of cell shape changes during migration in a multi-signaling micro-environment. This model is based on previous models that are presented by the same authors to study cell migration with a constant spherical cell shape in a multi-signaling substrates and mechanotaxis effect on cell morphology. Using the finite element discrete methodology, the cell is represented by a group of finite elements. The cell motion is modeled by equilibrium of effective forces on cell body such as traction, protrusion, electrostatic and drag forces, where the cell traction force is a function of the cell internal deformations. To study cell behavior in the presence of different stimuli, the model has been employed in different numerical cases. Our findings, which are qualitatively consistent with well-known related experimental observations, indicate that adding a new stimulus to the cell substrate pushes the cell to migrate more directionally in more elongated form towards the more effective stimuli. For instance, the presence of thermotaxis, chemotaxis and electrotaxis can further move the cell centroid towards the corresponding stimulus, respectively, diminishing the mechanotaxis effect. Besides, the stronger stimulus imposes a greater cell elongation and more cell membrane area. The present model not only provides new insights into cell morphology in a multi-signaling micro-environment but also enables us to investigate in more precise way the cell migration in the presence of different stimuli.     

Cardioprotective role of extracellular vesicles: A highlight on exosome beneficial effects in cardiovascular diseases

Extracellular vesicles (EVs) are nano-sized vesicles, released from many cell types including cardiac cells, have recently emerged as intercellular communication tools in cell dynamics. EVs are an important mediator of signaling within cells that influencing the functional behavior of the target cells. In heart complex, cardiac cells can easily use EVs to transport bioactive molecules such as proteins, lipids, and RNAs to the regulation of neighboring cell function. Cross-talk between intracardiac cells plays pivotal roles in the heart homeostasis and in adaptive responses of the heart to stress. EVs were released by cardiomyocytes under baseline conditions, but stress condition such as hypoxia intensifies secretome capacity. EVs secreted by cardiac progenitor cells and cardiosphere-derived cells could be pinpointed as important mediators of cardioprotection and cardiogenesis. Furthermore, EVs from many different types of stem cells could potentially exert a therapeutic effect on the damaged heart. Recent evidence shows that cardiac-derived EVs are rich in microRNAs, suggesting a key role in the controlling of cellular processes. EVs harboring exosomes may be clinically useful in cell-free therapy approaches and potentially act as prognosis and diagnosis biomarkers of cardiovascular diseases.     

Hybrid nanoreservoirs based on dextran-capped dendritic mesoporous silica nanoparticles for CD133-targeted drug delivery

In this study, the chemical features of dendritic mesoporous silica nanoparticles (DMSNs) provided the opportunity to design a nanostructure with the capability to intelligently transport the payload to the tumor cells. In this regard, doxorubicin (DOX)-encapsulated DMSNs was electrostatically surface-coated with polycarboxylic acid dextran (PCAD) to provide biocompatible dextran-capped DMSNs (PCAD-DMSN@DOX) with controlled pH-dependent drug release. Moreover, a RNA aptamer against a cancer stem cell (CSC) marker, CD133 was covalently attached to the carboxyl groups of DEX to produce a CD133-PCAD-DMSN@DOX. Then, the fabricated nanosystem was utilized to efficiently deliver DOX to CD133+ colorectal cancer cells (HT29). The in vitro evaluation in terms of cellular uptake and cytotoxicity demonstrated that the CD133-PCAD-DMSN@DOX specifically targets HT29 as a CD133 overexpressed cancer cells confirmed by flow cytometry and 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide assay. The potentially promising intelligent-targeted platform suggests that targeted dextran-capped DMSNs may find impressive application in cancer therapy.     

Precise two-dimensional D-bar reconstructions of human chest and phantom tank via sinc-convolution algorithm

ABSTRACT: BACKGROUND: Electrical Impedance Tomography (EIT) is used as a fast clinical imaging technique formonitoring the health of the human organs such as lungs, heart, brain and breast. Eachpractical EIT reconstruction algorithm should be efficient enough in terms of convergencerate, and accuracy. The main objective of this study is to investigate the feasibility of preciseempirical conductivity imaging using a sinc-convolution algorithm in D-bar framework. METHODS: At the first step, synthetic and experimental data were used to compute an intermediate objectnamed scattering transform. Next, this object was used in a 2-day integral equation whichwas precisely and rapidly solved via sinc-convolution algorithm to find the square root of theconductivity for each pixel of image. For the purpose of comparison, multigrid and NOSERalgorithms were implemented under a similar setting. Quality of reconstructions of syntheticmodels was tested against GREIT approved quality measures. To validate the simulationresults, reconstructions of a phantom chest and a human lung were used. RESULTS: Evaluation of synthetic reconstructions shows that the quality of sinc-convolutionreconstructions is considerably better than that of each of its competitors in terms ofamplitude response, position error, ringing, resolution and shape-deformation. In addition, theresults confirm near-exponential and linear convergence rates for sinc-convolution andmultigrid, respectively. Moreover, the least degree of relative errors and the most degree oftruth were found in sinc-convolution reconstructions from experimental phantom data.Reconstructions of clinical lung data show that the related physiological effect is wellrecovered by sinc-convolution algorithm. CONCLUSIONS: Parametric evaluation demonstrates the efficiency of sinc-convolution to reconstruct accurateconductivity images from experimental data. Excellent results in phantom and clinicalreconstructions using sinc-convolution support parametric assessment results and suggest thesinc-convolution to be used for precise clinical EIT applications.     

<Emphasis Type="Italic">Teucrium polium</Emphasis> Complex with Molybdate Enhance Cultured Islets Secretory Function

Islet transplantation has become a promising treatment in the therapy of type 1 diabetes. Its function improvement, after isolation and before transplantation, is crucial because of their loss both in number and function of islets after isolation procedures. Trace elements sodium orthovanadate (SOV) and sodium molybdate (SM), as well as medicinal plant Teucrium polium L. (TP), showed and possessed high beneficial antioxidative potential and even hypoglycemic properties via their effect on islets. We evaluated the effect of these components in combination on cultured islet function in order to improve pancreatic islet transplantation. Rat pancreatic islets were cultured for 24 h then incubated with different concentrations of TP (0.01 and 0.1 mg/mL) alone and in combination with SOV (1 mM) or SM (1 mM). Insulin concentration in buffer media was measured as islet secretory function. Administration of TP (0.01 mg/mL), SM, and SOV alone or in combination with each other significantly increased insulin secretion at high glucose concentration (16.7 mM); insulin secretion was significantly greater in the group containing both TP and SM than other treated groups (p < 0.05). The combination of the mentioned trace elements especially molybdate with TP could improve islet cells function before transplantation.     

Effect of geographical location and stochastic weather variation on life cycle assessment of biodiesel production from camelina in the northwestern USA

Purpose

The effect of regional factors on life cycle assessment (LCA) of camelina seed production and camelina methyl ester production was assessed in this study. While general conclusions from LCA studies point to lower environmental impacts of biofuels, it has been shown in many studies that the environmental impacts are dependent on location, production practices, and even local weather variations.

Methods

A cradle-to-farm gate and well-to-pump approaches were used to conduct the LCA. To demonstrate the impact of agro-climatic and management factors (weather condition, soil characteristics, and management practices) on the overall emissions for four different regions including Corvallis, OR, Pendleton, OR, Pullman, WA, and Sheridan, WY, field emissions were simulated using the DeNitrification-DeComposition (DNDC) model. openLCA v.1.4.2 software was used to quantify the environmental impacts of camelina seed and camelina methyl ester production.

Results and discussion

The results showed that greenhouse gas (GHG) emissions during camelina production in different regions vary between 49.39 and 472.51 kg CO₂-eq./ha due to differences in agro-climatic and weather variations. The GHG emissions for 1 kg of camelina produced in Corvallis, Pendleton, Pullman, and Sheridan were 0.76 ± 11, 0.55 ± 10, 0.47 ± 18, and 1.26 ± 6 % kg CO₂-eq., respectively. The GHG emissions for 1000 MJ of camelina biodiesel using camelina produced in Corvallis, Pendleton, Pullman, and Sheridan were 53.60 ± 5, 48.87 ± 5, 44.33 ± 7, and 78.88 ± 4 % kg CO₂-eq., respectively. Other impact categories such as acidification and ecotoxicity for 1000 MJ of camelina biodiesel varied across the regions by 43 and 103 %, respectively.

Conclusions

It can be concluded that process-based crop models such as DNDC in conjunction with Monte Carlo analysis are helpful tools to quantitatively estimate the influence of regional factors on field emissions which consequently can provide information about the expected variability in LCA results.