Eigenimage2Eigenimage (E2E): A Self-Supervised Deep Learning Network for Hyperspectral Image Denoising.

The performance of deep learning-based denoisers highly depends on the quantity and quality of training data. However, paired noisy-clean training images are generally unavailable in hyperspectral remote sensing areas. To solve this problem, this work resorts to the self-supervised learning technique, where our proposed model can train itself to learn one part of noisy input from another part of noisy input. We study a general hyperspectral image (HSI) denoising framework, called Eigenimage2Eigenimage (E2E), which turns the HSI denoising problem into an eigenimage (i.e., the subspace representation coefficients of the HSI) denoising problem and proposes a learning strategy to generate noisy-noisy paired training eigenimages from noisy eigenimages. Consequently, the E2E denoising framework can be trained without clean data and applied to denoise HSIs without the constraint with the number of frequency bands. Experimental results are provided to demonstrate the performance of the proposed method that is better than the other existing deep learning methods for denoising HSIs. A MATLAB demo of this work is available at https://github.com/LinaZhuang/HSI-denoiser-Eigenimage2Eigenimagehttps://github.com/LinaZhuang/HSI-denoiser-Eigenimage2Eigenimage for the sake of reproducibility.

NSCKL: Normalized Spectral Clustering With Kernel-Based Learning for Semisupervised Hyperspectral Image Classification.

Spatial-spectral classification (SSC) has become a trend for hyperspectral image (HSI) classification. However, most SSC methods mainly consider local information, so that some correlations may not be effectively discovered when they appear in regions that are not contiguous. Although many SSC methods can acquire spatial-contextual characteristics via spatial filtering, they lack the ability to consider correlations in non-Euclidean spaces. To address the aforementioned issues, we develop a new semisupervised HSI classification approach based on normalized spectral clustering with kernel-based learning (NSCKL), which can aggregate local-to-global correlations to achieve a distinguishable embedding to improve HSI classification performance. In this work, we propose a normalized spectral clustering (NSC) scheme that can learn new features under a manifold assumption. Specifically, we first design a kernel-based iterative filter (KIF) to establish vertices of the undirected graph, aiming to assign initial connections to the nodes associated with pixels. The NSC first gathers local correlations in the Euclidean space and then captures global correlations in the manifold. Even though homogeneous pixels are distributed in noncontiguous regions, our NSC can still aggregate correlations to generate new (clustered) features. Finally, the clustered features and a kernel-based extreme learning machine (KELM) are employed to achieve the semisupervised classification. The effectiveness of our NSCKL is evaluated by using several HSIs. When compared with other state-of-the-art (SOTA) classification approaches, our newly proposed NSCKL demonstrates very competitive performance. The codes will be available at https://github.com/yuanchaosu/TCYB-nsckl.

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing.

Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing (HU), yet their ability to simultaneously generalize various spectral variabilities (SVs) and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various SVs. Inspired by the powerful learning ability of deep learning (DL), we attempt to develop a general DL approach for HU, by fully considering the properties of endmembers extracted from the hyperspectral imagery, called endmember-guided unmixing network (EGU-Net). Beyond the alone autoencoder-like architecture, EGU-Net is a two-stream Siamese deep network, which learns an additional network from the pure or nearly pure endmembers to correct the weights of another unmixing network by sharing network parameters and adding spectrally meaningful constraints (e.g., nonnegativity and sum-to-one) toward a more accurate and interpretable unmixing solution. Furthermore, the resulting general framework is not only limited to pixelwise spectral unmixing but also applicable to spatial information modeling with convolutional operators for spatial-spectral unmixing. Experimental results conducted on three different datasets with the ground truth of abundance maps corresponding to each material demonstrate the effectiveness and superiority of the EGU-Net over state-of-the-art unmixing algorithms. The codes will be available from the website: https://github.com/danfenghong/IEEE_TNNLS_EGU-Net.

Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN.

Multisensor fusion is of great importance in Earth observation related applications. For instance, hyperspectral images (HSIs) provide wealthy spectral information while light detection and ranging (LiDAR) data provide elevation information, and using HSI and LiDAR data together can achieve better classification performance. In this paper, an unsupervised feature extraction framework, named as patch-to-patch convolutional neural network (PToP CNN), is proposed for collaborative classification of hyperspectral and LiDAR data. More specific, a three-tower PToP mapping is first developed to seek an accurate representation from HSI to LiDAR data, aiming at merging multiscale features between two different sources. Then, by integrating hidden layers of the designed PToP CNN, extracted features are expected to possess deeply fused characteristics. Accordingly, features from different hidden layers are concatenated into a stacked vector and fed into three fully connected layers. To verify the effectiveness of the proposed classification framework, experiments are executed on two benchmark remote sensing data sets. The experimental results demonstrate that the proposed method provides superior performance when compared with some state-of-the-art classifiers, such as two-branch CNN and context CNN.

Incorporating Negative Sample Training for Ship Detection Based on Deep Learning.

While ship detection using high-resolution optical satellite images plays an important role in various civilian fields-including maritime traffic survey and maritime rescue-it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea⁻land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters. In this study, Faster Region-based Convolutional Neural Network (Faster R-CNN) is applied to detect ships without the need for land masking. We propose an effective training strategy for the Faster R-CNN by incorporating a large number of images containing only terrestrial regions as negative samples without any manual marking, which is different from the selection of negative samples by targeted way in other detection methods. The experiments using Gaofen-1 satellite (GF-1), Gaofen-2 satellite (GF-2), and Jilin-1 satellite (JL-1) images as testing datasets under different ship detection conditions were carried out to evaluate the effectiveness of the proposed strategy in the avoidance of false alarms on land. The results show that the method incorporating negative sample training can largely reduce false alarms in terrestrial areas, and is superior in detection performance, algorithm complexity, and time consumption. Compared with the method based on sea⁻land segmentation, the proposed method achieves the absolute increment of 70% of the F1-measure, when the image contains large land area such as the GF-1 image, and achieves the absolute increment of 42.5% for images with complex harbors and many coastal ships, such as the JL-1 images.

Multi-GPU Based Parallel Design of the Ant Colony Optimization Algorithm for Endmember Extraction from Hyperspectral Images.

Spectral unmixing is a vital procedure in hyperspectral remote sensing image exploitation. The linear mixture model has been widely utilized to unmix hyperspectral images by extracting a set of pure spectral signatures, called endmembers in hyperspectral jargon, and estimating their respective fractional abundances in each pixel of the scene. Many algorithms have been proposed to extract endmembers automatically, which is a critical step in the spectral unmixing chain. In recent years, the ant colony optimization (ACO) algorithm has been developed for endmember extraction from hyperspectral data, which was regarded as a combinatorial optimization problem. Although the ACO for endmember extraction (ACOEE) can acquire accurate endmember results, its high computational complexity has limited its application in the hyperspectral data analysis. The GPUs parallel computing technique can be utilized to improve the computational performance of ACOEE, but the architecture of GPUs determines that the ACOEE should be redesigned to take full advantage of computing resources on GPUs. In this paper, a multiple sub-ant-colony-based parallel design of ACOEE was proposed, in which an innovative mechanism of local pheromone for sub-ant-colonies is utilized to enable ACOEE to be preferably executed on the multi-GPU system. The proposed method can avoid much synchronization among different GPUs to affect the computational performance improvement. The experiments on two real hyperspectral datasets demonstrated that the computational performance of ACOEE significantly benefited from the proposed methods.

High-Resolution Aerial Imagery Semantic Labeling with Dense Pyramid Network.

Semantic segmentation of high-resolution aerial images is of great importance in certain fields, but the increasing spatial resolution brings large intra-class variance and small inter-class differences that can lead to classification ambiguities. Based on high-level contextual features, the deep convolutional neural network (DCNN) is an effective method to deal with semantic segmentation of high-resolution aerial imagery. In this work, a novel dense pyramid network (DPN) is proposed for semantic segmentation. The network starts with group convolutions to deal with multi-sensor data in channel wise to extract feature maps of each channel separately; by doing so, more information from each channel can be preserved. This process is followed by the channel shuffle operation to enhance the representation ability of the network. Then, four densely connected convolutional blocks are utilized to both extract and take full advantage of features. The pyramid pooling module combined with two convolutional layers are set to fuse multi-resolution and multi-sensor features through an effective global scenery prior manner, producing the probability graph for each class. Moreover, the median frequency balanced focal loss is proposed to replace the standard cross entropy loss in the training phase to deal with the class imbalance problem. We evaluate the dense pyramid network on the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam 2D semantic labeling dataset, and the results demonstrate that the proposed framework exhibits better performances, compared to the state of the art baseline.

Group Sparse Representation Based on Nonlocal Spatial and Local Spectral Similarity for Hyperspectral Imagery Classification.

Spectral-spatial classification has been widely applied for remote sensing applications, especially for hyperspectral imagery. Traditional methods mainly focus on local spatial similarity and neglect nonlocal spatial similarity. Recently, nonlocal self-similarity (NLSS) has gradually gained support since it can be used to support spatial coherence tasks. However, these methods are biased towards the direct use of spatial information as a whole, while discriminative spectral information is not well exploited. In this paper, we propose a novel method to couple both nonlocal spatial and local spectral similarity together in a single framework. In particular, the proposed approach exploits nonlocal spatial similarities by searching non-overlapped patches, whereas spectral similarity is analyzed locally within the locally discovered patches. By fusion of nonlocal and local information, we then apply group sparse representation (GSR) for classification based on a group structured prior. Experimental results on three real hyperspectral data sets demonstrate the efficiency of the proposed approach, and the improvements are significant over the methods that consider either nonlocal or local similarity.

Apelin: an endogenous peptide essential for cardiomyogenic differentiation of mesenchymal stem cells via activating extracellular signal-regulated kinase 1/2 and 5.

Growing evidence has shown that apelin/APJ system functions as a critical mediator of cardiac development as well as cardiovascular function. Here, we investigated the role of apelin in the cardiomyogenic differentiation of mesenchymal stem cells derived from Wharton's jelly of human umbilical cord in vitro. In this research, we used RNA interference methodology and gene transfection technique to regulate the expression of apelin in Wharton's jelly-derived mesenchymal stem cells and induced cells with a effective cardiac differentiation protocol including 5-azacytidine and bFGF. Four weeks after induction, induced cells assumed a stick-like morphology and myotube-like structures except apelin-silenced cells and the control group. The silencing expression of apelin in Wharton's jelly-derived mesenchymal stem cells decreased the expression of several critical cardiac progenitor transcription factors (Mesp1, Mef2c, NKX2.5) and cardiac phenotypes (cardiac α-actin, ß-MHC, cTnT, and connexin-43). Meanwhile, endogenous compensation of apelin contributed to differentiating into cells with characteristics of cardiomyocytes in vitro. Further experiment showed that exogenous apelin peptide rescued the cardiomyogenic differentiation of apelin-silenced mesenchymal stem cells in the early stage (1-4 days) of induction. Remarkably, our experiment indicated that apelin up-regulated cardiac specific genes in Wharton's jelly-derived mesenchymal stem cells via activating extracellular signal-regulated kinase (ERK) 1/2 and 5.

Therapeutic Potential of Umbilical Cord Mesenchymal Stem Cells for Inhibiting Myofibroblastic Differentiation of Irradiated Human Lung Fibroblasts.

Radiation-induced lung injury (RILI) limits the benefits of radiotherapy in patients with lung cancer. Radiation-induced differentiation of lung fibroblasts to myofibroblasts plays a key role in RILI. Recent studies have shown that mesenchymal stem cells (MSCs) can protect against lung fibrosis and that Wnt/ß-catenin signaling is involved in fibrotic processes. In the present study, we explored the therapeutic potential of human umbilical cord MSCs (HUMSCs) for preventing radiation-induced differentiation of human lung fibroblasts (HLFs) to myofibroblasts. There are two advantages in the use of HUMSCs; namely, they are easily obtained and have low immunogenicity. Irradiated HLFs were co-cultured with HUMSCs. Expression of α-smooth muscle actin (α-SMA), a myofibroblast marker, was measured by Western blot analysis and immunohistochemistry. Irradiation (X-rays, 5 Gy) induced the differentiation of HLFs into myofibroblasts, which was inhibited by co-culture with HUMSCs. Irradiation also caused activation of the canonical Wnt/ß-catenin signaling in HLFs, as judged by increased phosphorylation of glycogen synthase kinase 3ß, nuclear accumulation of ß-catenin, and elevated levels of Wnt-inducible signaling protein-1 (WISP-1) in the conditioned medium. However, co-culture with HUMSCs attenuated the radiation-induced activation of the Wnt/ß-catenin signaling. We also measured the expression of FRAT1 that can enhance the Wnt/ß-catenin signaling by stabilizing ß-catenin. Co-culture with HUMSCs decreased FRAT1 protein levels in irradiated nHLFs. Thus, co-culture with HUMSCs attenuated the radiation-induced activation of Wnt/ß-catenin signaling in HLFs, thereby inhibiting myofibroblastic differentiation of HLFs. Wnt/ß-catenin signaling is a potential therapeutic target for limiting RILI in patients receiving radiotherapy for lung cancer.

[Impact analysis of atmospheric state for target detection in hyperspectral radiance image].

Target detection based on hyperspectral radiance images can improve data processing efficiency to meet the requirements of real-time processing. However, the spectral radiance acquired by the remote sensor will be affected by the atmosphere. In the present paper, hyperspectral imaging process is simulated to analyze the effects of the changes in atmospheric state on target detection in hyperspectral radiance image. The results show that hyperspectral radiance image can be directly used for target detection, different atmospheric states have little impacts on the RXD detection, whereas the MF detection is dependent on the accuracy of the input spectrum, and good results can only be obtained by the MF detector when the atmospheric states are similar between the radiance spectrum of the target to be detected and the simulated hyperspectral image.

Human umbilical cord Wharton's jelly stem cells: immune property genes assay and effect of transplantation on the immune cells of heart failure patients.

Stem cells derived from umbilical cord Wharton's jelly (WJSCs) are not immunogenic and have immunosuppressive effects. To evaluate the related mechanisms and the effect of transplantation on body immune cells, we examined immune property genes expression in WJSCs and levels of T-lymphocytes subgroups and immunoglobulins (Ig) in heart failure (HF) patients with and without WJSCs transplantation. WJSCs express immune tolerance genes HLA-E, HLA-G and HLA-F and immunomodulation genes VEGF, TGFß1, HGF, HMOX1, IL1ß, IL-6, LIF, LGALS-1/3/8, COX1/2 and PTGE, while they do not express immune response-related genes HLA-DR, HLA-DQ, HLA-DP, CD80, CD86, CD40 and CD40L. No obvious changes of T-lymphocytes subgroups and plasma IgG/IgM were observed in HF patients with WJSCs transplantation. Our results suggest that the immune properties of WJSCs are due to the expression of immune avoidance and immunomodulation genes in the absence of immune response-related genes. WJSCs are secure in immunological aspects when used as seed cells for cardiac repair.

Human amniotic fluid-derived c-kit(+) and c-kit (-) stem cells: growth characteristics and some differentiation potential capacities comparison.

Amniotic fluid (AF) contains heterogeneous and multipotential cell types. A pure mesenchymal stem cells group can be sorted from AF using flow cytometry. In order to evaluate a possible therapeutic application of these cells, the human AF-derived c-kit(+) stem cells (c-kit(+) AFS) were compared with the c-kit(-) (unselected) stem cells (c-kit(-) AFS). Our findings revealed that the optimal period to obtain c-kit(+) AFS cells was between 16 and 22 weeks of gestation. Following cell sorting, c-kit(+) AFS cells shared similar morphological and proliferative characteristics as the c-kit(-) AFS cells. Both c-kit(+) and c-kit(-) AFS cells had the characteristics of mesenchymal stem cells through surface marker identification by flow cytometric and immunocytochemical analysis. Both c-kit(+) and c-kit(-) AFS cells could differentiate along adipogenic and osteogenic lineages. However, the myocardial differentiation capacity was enhanced in c-kit(+) AFS cells by detecting GATA-4, cTnT, α-actin, Cx43 mRNA and protein expression after myocardial induction; whereas induced c-kit(-) AFS cells were only detected with GATA-4 mRNA and protein expression. The c-kit(+) AFS cells could have potential clinical application for myogenesis in cardiac regenerative therapy.

[A method of endmember extraction in hyperspectral remote sensing images based on discrete particle swarm optimization (D-PSO)].

For the inaccuracy of endmember extraction caused by abnormal noises of data during the mixed pixel decomposition process, particle swarm optimization (PSO), a swarm intelligence algorithm was introduced and improved in the present paper. By re-defining the position and velocity representation and data updating strategies, the algorithm of discrete particle swarm optimization (D-PSO) was proposed, which made it possible to search resolutions in discrete space and ultimately resolve combinatorial optimization problems. In addition, by defining objective functions and feasible solution spaces, endmember extraction was converted to combinatorial optimization problem, which can be resolved by D-PSO. After giving the detailed flow of applying D-PSO to endmember extraction and experiments based on simulative data and real data, it has been verified the algorithm's flexibility to handle data with abnormal noise and the reliability of endmember extraction were verified. Furthermore, the influence of different parameters on the algorithm's performances was analyzed thoroughly.

[Independent component analysis for spectral unmixing in hyperspectral remote sensing image].

Hyperspectral remote sensing plays an important role in earth observation on land, ocean and atmosphere. A key issue in hyperspectral data exploitation is to extract the spectra of the constituent materials (endmembers) as well as their proportions (fractional abundances) from each measured spectrum of mixed pixel in hyperspectral remote sensing image, called spectral un-mixing. Linear spectral mixture model (LSMM) provides an effective analytical model for spectral unmixing, which assumes that there is a linear relationship among the fractional abundances of the substances within a mixed pixel. To be physically meaningful, LSMM is subject to two constraints: the first constraint requires all abundances to be nonnegative and the second one requires all abundances to be summed to one. Independent component analysis (ICA) has been proposed as an advanced tool to un-mix hyperspectral image. However, ICA is based on the assumption of mutually independent sources, which violates the constraint conditions in LSMM. This embarrassment compromises ICA applicability to hyperspectral data. To overcome this problem, the present paper introduces a solution of minimization of total correlation of the components. Interestingly, with the minimization of total correlation of the components, the angle of the direction between each components is invariable. A Parallel oblique-ICA (Pob-ICA) algorithm is proposed to correct the angle of the searching direction between the components. Two novelties result from our proposed Pob-ICA algorithm. First, the algorithm completely satisfies the physical constraint conditions in LSMM and overcomes the limitation of statistical independency assumed by ICA. Second, the last component, which is missed in other existing ICA algorithms, can be estimated by our proposed algorithm. In experiments, Pob-ICA algorithm demonstrates excellent performance in the simulative and real hyperspectral images.

[Transfection of bone marrow mesenchymal stem cells by adeno-associated virus 2/1 vector].

This study was aimed to investigate the transfection efficacy of recombinant adeno-associated virus 2/1 (rAAV2/1) on bone marrow mesenchymal stem cells (BMMSCs) at different multiplicities of infection (MOI) and time, and effect of transfection on growth of rat BMMSCs. The rat BMMSCs cultured in vitro were transfected by using rAAV2/1 with enhanced green fluorescent protein (rAAV2/1-EGFP) at MOI of 1 x 10(4), 1 x 10(5) and 1 x 10(6); the EGFP expression was observed by fluorescent microscopy at 3, 7 and 14 days. The viability, proliferation multiple, differentiation ability of daughter cells were detected for evaluating the effect of rAAV2/1 on survival, proliferation and differentiation of BMMSCs and the fluorescence index (FI) were determined by flow cytometry. The results indicated that after transfection with rAAV2/1 for 24 hours the green fluorescence in BMMSCs were observed, but also the fluorescence gradually was enhanced along with prolonging of time, and reached to steady level after 7 days; the viability, proliferation multiple, differentiation ability of BMMSCs transfected by rAAV2/1-EGFP at different MOI showed no significant changes at 3,7 and 14 days (p > 0.05), meanwhile at same MOI the proliferation multiple obviously increased in comparison between 7 day vs 3 day and 14 days vs 7 days (p < 0.01). The flow cytometric detection showed that the transfection efficacy of rAAV2/1-EGFP on BMMSCs and FI increased significantly as the multiplicity of infection and culture time increased (p < 0.05). It is concluded that rAAV2/1-EGFP is able to transfect into BMMSCs effectively, but the transfection efficiency and fluorescence index increase significantly along with increase of multiplicity of infection and culture time. rAAV2/1-EGFP do not affect viability, proliferation multiple and differentiation ability of BMMSCs. rAAV2/1 is a kind of active vector for gene transfer to reform BMMSCs.

[Autologous mononuclear bone marrow cell transplantation by intracoronary route for patients with ischemic heart disease: observation of 2-years follow-up].

OBJECTIVE: To investigate the long-term effect and safety of intracoronary autologous bone marrow mononuclear cell (BMMC) transplantation in patients with ischemic heart disease (IHD). METHODS: Seventy-six patients with IHD, 26 patients with acute myocardial infarction (AMI) and 26 patients with chronic ischemic heart failure (CIHF), underwent routine treatment plus intracoronary autologous BMMC transplantation, and 24 patients, including 10 patients with AMI and 14 patients with CIHF underwent routine treatment as controls. Autologous BMMC transplantation was performed via a balloon catheter placed into the infarct-related artery during balloon dilatation by high pressure infusion to occlude the artery, which was performed 6 - 8 times for 2 minutes each with 2-minute interval or via a balloon catheter without occluding the infarct-related artery. Follow-up was conducted for 2 years. RESULTS: The surgery was safety without major periprocedural complications. There were no other new arrhythmias found by Holter recorder during the 2-years follow-up. In the AMI patients receiving BNNC transplantation, the left ventricular ejection fraction (LVEF) 1 and 2 years later increased by 5.79% (P < 0.05), 3.79% (P > 0.05) respectively; but there was no change in left ventricular end diastolic volume (LVEDV) and left ventricular end systolic volume (LVESV). The LVEF 1 and 2 years later of the control group increased by 8.8% and 9.2% respectively (both P < 0.01) and the LVESV 1 and 2 years later decreased by 20.4% and 27.8% respectively (both P < 0.05), the myocardium defect area 2 years later was not significantly different from that 3 months later. The heart function of the control group became markedly worse. CONCLUSION: Autologous BMMC intracoronary transplantation is safe and effective, especially in patients with CIHF.

[The autologous bone marrow mononuclear cell transplantation by intracoronary route treat patients with severe heart failure after myocardial infarction].

OBJECTIVE: To investigate the chronic effects of intracoronary autologous bone marrow mononuclear cell (BM-MNCs) transplantation in patients with refractory heart failure (RIHF) after myocardial infarction. METHODS: Thirty patients with RIHF (LVEF < 40%) were enrolled in this nonrandomized study, autologous BM-MNCs (5.0 +/- 0.7) x 10(7) were transplanted with via infarct-related coronary artery in 16 patients and 14 patients received standard medical therapy served as control. Baseline and follow up evaluations included complete clinical evaluations, plasma BNP, ANP, ET-1 measurements, echocardiography, PET, and Holter monitoring. RESULTS: Baseline characteristics were similar between the 2 groups. There were no major periprocedural complications. One patient developed ventricular premature contractions during cell infusion for several seconds and recovered spontaneously. Compared to pre-transplantation, plasma BNP and ET-1 significantly decreased and plasma ANP significantly increased at 7 days post transplantation; 6 minutes walking distance increased from (72.1 +/- 31.5) to (201.6 +/- 23.3) m (P < 0.01), LVEF increased 9.9% (P < 0.001) and FDG-PET revealed vital myocardium area increased (10.3 +/- 3.4)% (P < 0.01) at 3 months after BM-MNCs transplantation. At 6 months follow up, the NYHA class improved from (3.4 +/- 0.1 to 2.4 +/- 0.2, P < 0.001) and no patient died and 1 patient rehospitalized due to lower extremities edema. In control group, LVEF decreased 7.2% compared to baseline (P < 0.001) and was significantly lower than transplantation group at 3 months (P < 0.001). At 6 months follow up, the NYHA class increased from (3.5 +/- 0.1 to 3.9 +/- 0.1, P < 0.05), 2 patients died and 10 patients rehospitalized due to aggravated heart failure. CONCLUSION: Present study demonstrates that intracoronary transplantation of autologous BM-MNCs is safe and effective for treating patients with RIHF after myocardial infarction.