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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Smurf1 plays a role in EGF inhibition of BMP2-induced osteogenic differentiation

It has been demonstrated that epidermal growth factor (EGF) plays a role in supporting the proliferation of bone marrow stromal cells in bone but inhibits their osteogenic differentiation. However, the mechanism underlying EGF inhibition of osteoblast differentiation remains unclear. Smurf1 is an E3 ubiquitin ligase that targets Smad1/5 and Runx2, which are critical transcription factors for bone morphogenetic protein 2 (BMP2)-induced osteoblast differentiation. In this study, we investigated the effect of EGF on the expression of Smurf1, and the role of Smurf1 in EGF inhibition of osteogenic differentiation using C2C12 cells, a murine myoblast cell line. EGF increased Smurf1 expression, which was blocked by inhibiting the activity of either JNK or ERK. Chromatin immunoprecipitation and Smurf1 promoter assays demonstrated that c-Jun and Runx2 play roles in the EGF induction of Smurf1 transcription. EGF suppressed BMP2-induced expression of osteogenic marker genes, which were rescued by Smurf1 knockdown. EGF downregulated the protein levels of Runx2 and Smad1 in a proteasome-dependent manner. EGF decreased the transcriptional activity of Runx2 and Smurf1, which was partially rescued by Smurf1 silencing. Taken together, these results suggest that EGF increases Smurf1 expression via the activation of JNK and ERK and the subsequent binding of c-Jun and Runx2 to the Smurf1 promoter and that Smurf1 mediates the inhibitory effect of EGF on BMP2-induced osteoblast differentiation. - Highlights: • EGF increases the expression level of Smurf1 in mesenchymal precursor cells. • EGF reduces the protein levels and transcriptional activity of Runx2 and Smad1. • EGF suppresses BMP2-induced osteogenic differentiation, which is rescued by Smurf1 knockdown.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Polymer microfiber meshes facilitate cardiac differentiation of c-kit{sup +} human cardiac stem cells

Electrospun microfiber meshes have been shown to support the proliferation and differentiation of many types of stem cells, but the phenotypic fate of c-kit{sup +} human cardiac stem cells (hCSCs) have not been explored. To this end, we utilized thin (~5 µm) elastomeric meshes consisting of aligned 1.7 µm diameter poly (ester-urethane urea) microfibers as substrates to examine their effect on hCSC viability, morphology, proliferation, and differentiation relative to cells cultured on tissue culture polystyrene (TCPS). The results showed that cells on microfiber meshes displayed an elongated morphology aligned in the direction of fiber orientation, lower proliferation rates, but increased expressions of genes and proteins majorly associated with cardiomyocyte phenotype. The early (NK2 homeobox 5, Nkx2.5) and late (cardiac troponin I, cTnI) cardiomyocyte genes were significantly increased on meshes (Nkx=2.5 56.2±13.0, cTnl=2.9±0.56,) over TCPS (Nkx2.5=4.2±0.9, cTnl=1.6±0.5, n=9, p<0.05 for both groups) after differentiation. In contrast, expressions of smooth muscle markers, Gata6 and myosin heavy chain (SM-MHC), were decreased on meshes. Immunocytochemical analysis with cardiac antibody exhibited the similar pattern of above cardiac differentiation. We conclude that aligned microfiber meshes are suitable for guiding cardiac differentiation of hCSCs and may facilitate stem cell-based therapies for treatment of cardiac diseases. - Highlights: • First study to characterize c-kit{sup +} human cardiac stem cells on microfiber meshes. • Microfiber meshes seem reducing cell proliferation, but no effect on cell viability. • Microfiber meshes facilitate the elongation of human cardiac stem cells in culture. • Cardiac but not smooth muscle differentiation were enhanced on microfiber meshes. • Microfiber meshes may be used as cardiac patches in cell-based cardiac therapy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

PTEN silencing enhances neuronal proliferation and differentiation by activating PI3K/Akt/GSK3β pathway in vitro

Highlights: • PTEN had a dual effect on the neuronal cells in vitro. • PTEN knockdown significantly promoted neuronal cell proliferation and differentiation. • PTEN knockdown activated Akt/GSK3β and Wnt/β-catenin pathways in vitro. • GSK3β as a key node involved in PTEN controlling cell proliferation and differentiation in PC12 cells. • PTEN-GSK3β pathway modulated neuronal proliferation via β-catenin. The failure of neuronal proliferation and differentiation is a major obstacle for neural repair and regeneration after traumatic central nervous system (CNS) injury. PTEN acts as an intrinsic brake on the neuronal cells, but its roles and mechanism still remain to be clarified. Herein, for the first time we confirmed that PTEN had a dual effect on the neuronal cells in vitro. Firstly, we found that PTEN knockdown significantly promoted cell proliferation and differentiation. Then, PTEN knockdown activated PI3K/Akt and Wnt/β-catenin pathways in vitro. Further evidence revealed that GSK3β as a key node involved in PTEN controlling cell proliferation and differentiation in PC12 cells. In addition, we identified that PTEN-GSK3β pathway modulated neuronal proliferation via β-catenin. Taken together, these results suggest that PTEN silencing enhances neuronal proliferation and differentiation by activating PI3K/Akt/GSK3β pathway that it may be a promising therapeutic approach for CNS injury.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Validation of reference genes for real-time PCR of cord blood mononuclear cells, differentiating endothelial progenitor cells, and mature endothelial cells

Highlights: • RT-qPCR was used to assess and follow the differentiation of EPCs. • A set of reliable and non-variable genes is key to obtain accurate RT-qPCR results. • The relative gene expression of CD34+ and differentiating endothelial progenitor cells was determined. • The optimal set of reference for the gene expression studies performed by RT-qPCR was determined. In the last ten years, endothelial progenitor cells (EPCs) have gained interest as an attractive cell population in regenerative medicine for vascular applications. This population is defined as the precursor of endothelial mature cells (ECs) through a process of differentiation. To our knowledge, no single marker can be used to discriminate them from mature ECs. To effectively study their differentiation kinetics, gene expression must be assessed. Quantitative real-time PCR (RT-qPCR) is widely used to analyze gene expression. To minimize the impact of variances from RT-qPCR, a rigorous selection of reference genes must be performed prior to any experiments due to variations in experimental conditions. In this study, CD34+ mononuclear cells were extracted from human cord blood and differentiated into EPCs after seeding for a maximum period of 21 days. To choose the best combinations of reference genes, we compared the results of EPCs, CD34+ mononuclear cells, and mature endothelial cells to ensure that the differentiation kinetics did not affect the expression of our selected reference genes. The expression levels of seven genes, namely, YWHAZ, GAPDH, HPRT1, RPLP0, UBC, B2M, and TBP were thus compared. The algorithms geNorm, NormFinder, BestKeeper, and the Comparative ΔCt method were employed to assess the expression of each candidate gene. Overall results reveal that the expression stability of reference genes may differ depending on the statistical program used. YWHAZ, GAPDH, and UBC composed the optimal set of reference genes for the gene expression studies performed by RT-qPCR in our experimental conditions. This work can thus serve as a starting point for the selection of candidate reference genes to normalize the levels of gene expression in endothelial progenitor cell populations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Extracellular IL-37 promotes osteogenic and odontogenic differentiation of human dental pulp stem cells via autophagy

The osteogenic and odontogenic differentiation of dental pulp stem cells (DPSCs) contribute to restoration and regeneration of dental tissue. Previous study indicated that interleukin-37 (IL-37) was an anti-inflammatory factor that affected other pro-inflammatory signals. The aim of this study was to explore the effects of IL-37 on the differentiation of DPSCs. DPSCs were cultured in growth medium with different concentrations of IL-37. We selected the optimal concentration for the following experiments by alkaline phosphatase (ALP) activity analysis, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and Western blot. Cell counting kit assay (CCK-8) and 5-Ethynyl-2-Deoxyuridine (EdU) assay were conducted to assess the effects of IL-37 on the proliferation of DPSCs. ALP activity assay and staining, alizarin red S (ARS) staining, qRT‐PCR, Western blot as well as immunofluorescence staining were conducted to assess differentiation ability of DPSCs. Western blot, immunofluorescence staining and transmission electron microscopy (TEM) were utilized to examine cell autophagy. Results showed that IL-37 enhanced the osteogenic and odontogenic differentiation ability of DPSCs with no significant influence on the proliferation of DPSCs. Autophagy in DPSCs was activated by IL-37. Activation of autophagy enhanced osteogenesis and odontogenesis of DPSCs, whereas inhibition of autophagy suppressed DPSCs osteogenic and odontogenic differentiation. In conclusion, IL-37 increased osteogenic and odontogenic differentiation via autophagy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Paleomagnetic Evidence for a Partially Differentiated Ordinary Chondrite Parent Asteroid

Abstract The textures and accretion ages of chondrites have been used to argue that their parent asteroids never differentiated. Without a core, undifferentiated planetesimals could not have generated magnetic fields through dynamo activity, so chondrites are not expected to have experienced such fields. However, the magnetic remanence carried by the CV chondrites is consistent with dynamo‐generated fields, hinting that partially differentiated asteroids consisting of an unmelted crust atop a differentiated interior may exist. Here, we test this hypothesis by applying synchrotron X‐ray microscopy to metallic veins in the slowly cooled H6 chondrite Portales Valley. The magnetic remanence carried by nanostructures in these veins indicates that this meteorite recorded a magnetic field over a period of tens to hundreds of years at ∼100 Myr after solar system formation. These properties are inconsistent with external field sources such as the nebula, solar wind, or impacts, but are consistent with dynamo‐generated fields, indicating that the H chondrite parent body contained an advecting metallic core and was therefore partially differentiated. We calculate the thermal evolution of the chondritic portions of partially differentiated asteroids that form through incremental accretion across 10 5 to 10 6 years, finding this can agree with the measured ages and cooling rates of multiple H chondrites. We also predict that the cores of these bodies could have been partially liquid and feasibly generating a dynamo at 100 Myr after solar system formation. These observations contribute to a growing body of evidence supporting a spectrum of internal differentiation within some asteroids with primitive surfaces.

Bryson, J. F. J.↗
DOE OSTI2023

Differentiable modelling to unify machine learning and physical models for geosciences

Process-based modelling offers interpretability and physical consistency in many domains of geosciences but struggles to leverage large datasets efficiently. Machine-learning methods, especially deep networks, have strong predictive skills yet are unable to answer specific scientific questions. Here, in this Perspective, we explore differentiable modelling as a pathway to dissolve the perceived barrier between process-based modelling and machine learning in the geosciences and demonstrate its potential with examples from hydrological modelling. ‘Differentiable’ refers to accurately and efficiently calculating gradients with respect to model variables or parameters, enabling the discovery of high-dimensional unknown relationships. Differentiable modelling involves connecting (flexible amounts of) prior physical knowledge to neural networks, pushing the boundary of physics-informed machine learning. It offers better interpretability, generalizability, and extrapolation capabilities than purely data-driven machine learning, achieving a similar level of accuracy while requiring less training data. Additionally, the performance and efficiency of differentiable models scale well with increasing data volumes. Under data-scarce scenarios, differentiable models have outperformed machine-learning models in producing short-term dynamics and decadal-scale trends owing to the imposed physical constraints. Differentiable modelling approaches are primed to enable geoscientists to ask questions, test hypotheses, and discover unrecognized physical relationships. Future work should address computational challenges, reduce uncertainty, and verify the physical significance of outputs.

58 GEOSCIENCES↗
DOE OSTI2026

Study of fully coupled three-dimensional envelope instability using automatic differentiation

Automatic differentiation is a powerful tool for computing derivatives of simulation results with respect to given parameters. In this Letter, we have applied this tool to investigate the instability of a dynamical system governed by 21 ordinary differential equations. This second-order instability (named envelope instability) is driven by space-charge effects and has a significant impact on the operational regimes of particle accelerators. Our study delves into the three-dimensional envelope instability, incorporating both transverse and longitudinal coupling. Conventionally, analyzing this complex system would necessitate solving 441 ordinary differential equations, which is computationally intractable. However, by employing automatic differentiation, we were able to track only 21 equations. This approach allowed us to uncover an additional instability stopband, which arises from space-charge-induced coupling and has not been reported in previous studies. This research highlights the significant advantages of automatic differentiation in analyzing complicated dynamical systems involving a large number of ordinary differential equations.

Qiang, Ji [Lawrence Berkeley National Laboratory (↗
DOE OSTI2021

Informing nuclear physics via machine learning methods with differential and integral experiments

Information from differential nuclear-physics experiments and theory is often too uncertain to accurately define nuclear-physics observables such as cross sections or energy spectra. Integral experimental data, representing the applications of these observables, are often more precise but depend simultaneously on too many of them to unambiguously identify issues in the observable with human expert analysis alone. Here, we explore how we can leverage physics knowledge gained from differential experimental data, nuclear theory, integral experiments, and neutron-transport calculations to better understand nuclear-physics observables in the context of the application area represented by integral experiments. We support this task with machine-learning methods to discern trends in a large amount of convoluted data. Differential and integral information was used in an analysis augmented by the random forest and the Shapley additive explanations metric. We chose as an application area one that is represented by criticality measurements and pulsed-sphere neutron-leakage spectra. We show one representative example ( 241 Pu fission observables) where the combination of differential and integral information allowed to resolve issues in data representing these observables. As a starting point, the machine learning (ML) algorithms highlighted several observables as leading potentially to bias in simulating integral experiments. Differential information, paired with sensitivity to integral quantities, allowed us then to pinpoint one specific observable ( 241 Pu fission cross section) as the main driver of bias. The comparison to integral experiments, on the other hand, allowed us to indicate a likely reliable experiment among several discrepant ones for this observables. In other cases (e.g., 239 Pu observables), we were not able to resolve the confounding introduced by integral experiments but instead highlighted the need for targeted new experiments and theory developments to better constrain the nuclear-physics space for the application area represented by integral experiments. We were able to combine information from differential experimental data, nuclear-physics theory, integral experiments, and neutron-transport simulations of the latter experiments with the help of the random forest algorithm and expert judgment. This combination of knowledge allows to improve our description of nuclear-physics observables as applied to a particular application area.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

The Memory Scaling of Reverse-Mode Differentiation in Particle Accelerator Simulations with Space Charge

The recent development of differentiable simulation codes for particle accelerators has enabled gradient-based workflows that promise finer control and more realistic modeling of accelerator facilities. However, when using reverse-mode automatic differentiation, the memory usage continuously increases during the simulation, and can potentially exceed the available hardware memory - especially when costly space charge computation is included. To study the memory requirements for differentiable simulations, we have implemented space charge in Cheetah, a PyTorch-based beam tracking code that supports reverse-mode differentiation. We find that the memory usage for reverse-mode differentiation grows linearly with the number of macroparticles and cells, and that it is proportional to the number of space charge kicks involved in the simulation. This general scaling can be used to evaluate whether a given differentiable simulation is feasible given hardware memory constraints.

Dhamrait, Arjun↗
DOE OSTI2019

Application of symmetries to differential equations: symmetry reduction and solution transformation examples

In the following lecture notes, we provide an outline of what we mean by symmetries of differential equations and illustrate their utility in simplification of differential equations which may lead to solutions. Loosely, a symmetry of a differential equation is a local group of transformations acting on the space of independent and dependent variables that transform solutions of a differential equation to new solutions, i.e., if Δ(x,u,p)=0 denotes a differential equation with a solution prescribed by [x,u,p], then knowlege of a symmetry facilitates the attainment of a new solution, [$\tilde{x}$, $\tilde{u}$, $\tilde{p}$], such that Δ(x,u,p)=Δ($\tilde{x}$, $\tilde{u}$, $\tilde{p}$)=0. In Equations (1) and (2), x, u and p denote the independent variables, dependent variables and partial derivatives, respectively and the tildes denote the transformations. Equation (2) will subsequently be referred to as the symmetry criterion. We can apply the knowlede of the existence of a symmetry in two ways: 1) If not solution is known apriori, the symmetry can be applied to simplify the differential equation through the method of symmetry reduction. Often, the resultant simplification may enable a solution to be determined; 2) If we already possess a solution to a different equation, knowledge of an admissible symmetry enables us to generate new solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Enamel Matrix Derivative has No Effect on the Chondrogenic Differentiation of Mesenchymal Stem Cells

Background: Treatment of large bone defects due to trauma, tumor resection, or congenital abnormalities is challenging. Bone tissue engineering using mesenchymal stem cells (MSCs) represents a promising treatment option. However, the quantity and quality of engineered bone tissue are not sufficient to fill large bone defects. The aim of this study was to determine if the addition of enamel matrix derivative (EMD) improves in vitro chondrogenic priming of MSCs to ultimately improve in vivo MSC mediated endochondral bone formation. Methods: MSCs were chondrogenically differentiated in 2.0 × 10{sup 5} cell pellets in medium supplemented with TGFβ3 in the absence or presence of 1, 10, or 100 μg/mL EMD. Samples were analyzed for gene expression of RUNX2, Col II, Col X, and Sox9. Protein and glycoaminoglycan (GAG) production were also investigated via DMB assays, histology, and immunohistochemistry. Osteogenic and adipogenic differentiation capacity were also assessed. Results: The addition of EMD did not negatively affect chondrogenic differentiation of adult human MSCs. EMD did not appear to alter GAG production or expression of chondrogenic genes. Osteogenic and adipogenic differentiation were also unaffected though a trend toward decreased adipogenic gene expression was observed. Conclusion: EMD does not affect chondrogenic differentiation of adult human MSCs. As such the use of EMD in combination with chondrogenically primed MSCs for periodontal bone tissue repair is unlikely to have negative effects on MSC differentiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Calculating Control Rod Differential Worth Using Adjoint-Weighted Method

Recently, there are more and more research on using Monte Carlo method to calculate reactor important parameters. Control rod worth is one of the important parameters as it can determine the critical control rod position. There are two presentations of the control rod worth for utilization purpose, integral and differential. The former is the total reactivity change, while the latter is the reactivity change caused by per unit movement. In Monte Carlo calculation, differential control rod worth of a particular position is estimated by finite difference of reactivity between a small gap divided by gap distance. Though easy to implemented, the problem of this approach lies in that too large gap will enlarge error between 'differential' and 'difference', while too small gap will be disturbed by error caused by random fluctuation. Monte Carlo perturbation theory has been used to calculate the k{sub eff} sensitivity to certain parameters for a long time. By calculating the differential coefficient along with the k{sub eff}, this method is quite time-consuming and relatively accurate compared to finite difference method. Nevertheless, most of perturbation research focus on the perturbation of nuclide density or nuclear data. For geometrical perturbation, limited research was done before. Takahashi estimated the k{sub eff} sensitivity to reflector movement of pulsed fast reactor. MCNP6 used adjoint-weighted method for Godiva, Zeus and uranium oxyfluoride solution container benchmarks. This paper tries to apply the perturbation method to the determination of control rod differential worth. In this paper, based on RMC, a continuous-energy Reactor Monte Carlo transport code, Iterated Fission Probability method (IFP) is researched to calculate control rod differential worth on assembly level. IFP was originally developed by Hurwitz, and has been adopted by most of Monte Carlo code to compute adjoint-weighted parameters. This paper is organized with four parts. The first is introduction. Adjoint-weighted method is introduced in the second part. Problem 4 of benchmark from CASL is built to verify the method. Conclusion is given in final part.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Regularized Differentiation for Bioburden Density Estimation in Planetary Protection

In this paper, we propose and investigate the performance of two novel shrinkage estimators for bioburden density estimation in planetary protection. The estimators are based on the regularized differentiation of a cumulative count of colony forming units collected throughout the data collecting session or the life cycle of the entire mission. The regularized differentiation recasts the problem of bioburden density estimation as a linear least squares problem. The least squares problem is then solved through regularization techniques, such as truncated singular value decomposition and penalized least squares. The regularization is necessary to avoid noise amplification during the differentiation of noisy data. The two regularization estimators are compared with four other commonly used estimators to simultaneously evaluate the means of multivariable independent Poisson distributions: the maximum likelihood, noninformative Bayes estimator with Jeffreys prior, Empirical Bayes using conjugate gamma-Poisson model with gamma parameters selected by method of moments, and the Clevenson-Zidek estimator. It is shown through computer-simulated data that the regularized differentiation based on ridge regression has the smallest mean-squared error among all estimators. The analysis of shrinkage mechanism implemented by regularized differentiation is performed, and it is shown that the regularized differentiation amounts to performing a weighted averaging of all the samples. The weights are determined by the regularization parameter automatically selected by the L-curve technique. Since the method of least squares makes no distributional assumptions about the data, it presents an attractive technique for bioburden density estimation when there are concerns about the misspecification of the distributional model. The paper concludes with the analysis of the bioburden data collected during InSight mission and directions for future work.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2018

On the periodic solutions of the Michelson continuous and discontinuous piecewise linear differential system

Applying new results from the averaging theory for continuous and discontinuous differential systems, we study the periodic solutions of two distinct versions of the Michelson differential system: a Michelson continuous piecewise linear differential system and a Michelson discontinuous piecewise linear differential system. The tools here used can be applied to general nonsmooth differential systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Chapter 4 - Recent Advances in Identification of Differential Equations from Noisy Data: IDENT Review

Differential equations and numerical methods are extensively used to model various real-world phenomena in science and engineering. With modern developments, we aim to find the underlying differential equation from a single observation of time-dependent data. If we assume that the differential equation is a linear combination of various linear and nonlinear differential terms, then the identification problem can be formulated as solving a linear system. The goal then reduces to finding the optimal coefficient vector that best represents the time derivative of the given data. We review some recent works on the identification of differential equations. We find some common themes for the improved accuracy: (i) The formulation of linear system with proper denoising is important, (ii) how to utilize sparsity and model selection to find the correct coefficient support needs careful attention, and (iii) there are ways to improve the coefficient recovery. We present an overview and analysis of recent developments on the topic.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

The transcriptional factor Apt regulates neuroblast differentiation through activating CycE expression

Highlights: • Apt exclusively expresses in NB6-4T cells. • Apt controls NB6-4T differentiation. • Apt activates the expression of Cyc E in neuronal cells. • Gcm represses the expression of CycE through Apt in glial cells. In Drosophila, the thoracic neuroblast 6-4 (NB6-4T) divides asymmetrically into a glial precursor and a neuronal precursor, while the abdominal neuroblast 6-4 (NB6-4A) divides symmetrically to produce two glial cells. The underlying mechanism by which NB6-4T and NB6-4A undergo distinct differentiation is still elusive. Here, we find that the transcription factor Apontic (Apt) exclusively expresses in NB6-4T cells and is involved in regulating NB6-4T differentiation. Loss of Apt results in neuronal precursor loss. Epistasis analysis shows that Apt controls NB6-4T differentiation through activating CycE expression. On the other hand, Gcm suppresses Apt expression in the NB6-4A cell, thus inhibiting CycE expression. Taken together, our findings reveal a Gcm-Apt-CycE axis that regulates neuroblast and glia cell differentiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Cardiac extracellular matrix hydrogel together with or without inducer cocktail improves human adipose tissue-derived stem cells differentiation into cardiomyocyte–like cells

Studies have demonstrated that differentiation of stem cells into cardiomyocytes is a complex phenomenon that requires sufficient inducing factors at various time points. Cardiac extracellular matrix (cECM) could provide tissue specific microenvironment and act as an inductive template for efficient cell differentiation. The aim of this study was to investigate the effect of cECM on differentiation of human adipose tissue-derived stem cells (hADSCs) into cardiomyocytes using cECM hydrogel in combination with a cardiac inductive cocktail. hADSCs were cultured on ECM-coated plates with and without inductive cocktail for 3weeks. qRT-PCR and western blot analysis were used to evaluate the expression pattern of special cardiac genes and proteins. When hADSCs were cultured in the presence of cECM cardiac genes including GATA4, HAND1, HAND2, NKX2.5, Troponin I, βMHC, Connexin43 were highly expressed in differentiated cells. Also Connexin43, cTnI and βMHC proteins were expressed as well. We could show that cECM by itself could affect viability, proliferation and differentiation of hADSCs. However, combination of cECM with a cardiac inducing cocktail could improve the results.

60 APPLIED LIFE SCIENCES↗