Long Noncoding RNA LINC01518 Modulates Proliferation and Migration in TGF-ß1-Treated Human Tenon Capsule Fibroblast Cells Through the Regulation of hsa-miR-216b-5p.

In this study, we investigated the expression and functions of long noncoding RNAs (LncRNAs) of LINC01518 in an in vitro model of TGF-ß1-treated human Tenon capsule fibroblast (HTF) cells. qRT-PCR was used to examine LINC01518 expression in in situ human glaucoma tissues, and in vitro HTF cells treated with TGF-ß1. Lentivirus-mediated LINC01518 knockdown was performed in HTF cells to investigate its effect on TGF-ß1-induced cell proliferation, migration and autophagy signaling pathway. The potential ceRNA candidate of LINC01518, hsa-miR-216b-5p, was probed by dual-luciferase assay and qRT-PCR. Hsa-miR-216b-5p was also knocked down in LINC01518-downregulated HTF cells to investigate the function of this lncRNA-miRNA epigenetic axis in TGF-ß1-treated HTF cells. LINC01518 was upregulated in human glaucoma tissues and cultured HTF cells. LINC01518 downregulation significantly suppressed TGF-ß1-induced cell proliferation, migration and autophagy signaling pathway in HTF cells. Hsa-miR-216b-5p was confirmed to be a ceRNA target of LINC01518. Knocking down hsa-miR-216b-5p reversed the suppressing effects of LINC01518 downregulation in TGF-ß1-treated HTF cells. Our study demonstrated that LINC01518 is a functional factor in regulating proliferation and migration in TGF-ß1-treated HTF cells, and hsa-miR-216b -5p may also be involved. Targeting the epigenetic axis of LINC01518/hsa-miR-216b-5p may provide new insight into the pathological development of human glaucoma.

Constructing an unprecedented {MnII38} matryoshka doll with a [Mn18(CO3)9] inorganic core and magnetocaloric effect.

An unprecedented inner [Mn18(CO3)9] inorganic core and [Mn20] metal-organic periphery compose a high-nuclearity homometallic single-valent {MnII38} molecular aggregate with a [Mn6] ⊂ [Mn12] ⊂ [Mn8] ⊂ [Mn12] matryoshka doll-like skeleton that displays a significant magnetocaloric effect (MCE).

[Anti-scarring effect of rapamycin following filtering surgery in rabbit eyes].

OBJECTIVE: To study the effect of rapamycin on scar formation in rabbit eyes following filtering operation and explore the possible mechanism. METHODS: Ninety-six healthy adult rabbits were subjected to trabeculectomy of the left eye and subsequently randomly divided into 4 groups (n=24) for treatment with castor oil (control) or rapamycin (1%, 3%, or 5%) eye drops of the operated eyes 4 times a day. The morphology and function of the filtering blebs of the rabbits were compared at 7, 14, 21 and 28 days after the operation; at each of the time points, 6 rabbits from each group were euthanized for detection of expressions of proliferating cell nuclear antigen (PCNA) and α-smooth muscle actin (α-SMA) in the tissues in the surgical area using immunohistochemistry. Cultured rabbit subconjunctival fibroblasts (RTFSs) were treated with different concentrations of rapamycin (0.06, 0.25, 1, and 4 mg/L) and the cell apoptosis was detected using flow cytometry. RESULTS: In the first, second and third weeks after the operation, the rate of functional follicle formation was significantly higher in the 3 rapamycin groups than in the control group (P < 0.05), and the number of α- SMA-positive fibroblasts decreased over time in the 3 rapamycin groups. In cultured RTFSs, treatment with rapamycin at different concentrations resulted in increased apoptosis of the cells, and rapamycin above 0.25 mg/L significantly increased the cell apoptosis in a dose-dependent manner. CONCLUSIONS: Rapamycin can inhibit hyperplasia of the filtering passage tissue, helps to preserve the functional filtering blebs and prolong their life span, and induces apoptosis of RTFS.

Silibinin declines blue light-induced apoptosis and inflammation through MEK/ERK/CREB of retinal ganglion cells.

Purpose: This study aimed to assess the protective effects of silibinin on blue light-emitting diode (LED)-induced retinal ganglion cells (RGCs) damage. Methods: Silibinin was applied in RGCs damage in vitro model to test its protective effects. Cell viability was assessed with the MTT method and cell apoptosis was evaluated by TUNEL and Annexin V/propidium iodide staining. The expressions of apoptosis related proteins and influenced signalling pathways were measured using western blotting and immunohistochemistry. Inflammatory factors induced by RGC damage were detected using ELISA method. Results: It was found that silibinin in 50 and 100 µM treatment showed a significant protective effect in RGCs under blue light damage. Apoptosis assay showed that silibinin treatment could significantly improve the apoptotic status of RGCs. When the potentially affected signal pathway was considered, blue light would down-regulate the expression of MEK1/ERK/CREB. The levels of inflammatory factors (TNF-α, IL-1ß, IL-6 and IL-10) were significantly regulated by silibinin treatment. Conclusions: Silibinin pretreatment would demonstrate protective effect against blue light induced acute RGCs damage. Silibinin treatment has a direct suppression of apoptosis and inflammation through the activation of MEK/ERK/CREB pathway in vitro.

[Anti-scarring effect of rapamycin in rabbits following glaucoma filtering surgery].

OBJECTIVE: To study the anti- scarring effect of rapamycin in rabbits receiving glaucoma filtering surgery. METHODS: Ninety-six Chinchilla rabbits were randomized equally into 3 rapamycin treatment groups and one control group. All the rabbits underwent trabeculectomy, after which the rabbits in the 3 rapamycin groups were treated with eye drops containing 1%, 3%, or 5% rapamycin in the operated eyes, and those in the control groups were given castor oil 4 times a day. The intraocular pressure (IOP) and inflammatory reaction in the treated eyes were observed, and the PCNA-positive cells in the filtering bleb were detected using immunohistochemistry. RTFs isolated from the Tenon's capsule of the rabbits were cultured in vitro, and the expressions of caspase-3, caspase-8, and caspase-9 in the fibroblasts were detected after treatment with different concentrations of rapamycin. RESULTS: The IOP was significantly lower in rapamycin-treated group than in the control group after the surgery (P < 0.05). The counts of the PCNA-positive cells were significantly lower in rapamycin-treated rabbits than in the control group (P < 0.05). Rapamycin treatment dose-dependently increased the expressions of caspase-3 and caspase- 9 at both the mRNA (P < 0.001) and protein (P < 0.001) levels without causing significant changes in the expressions of caspase-8. CONCLUSIONS: Rapamycin can inhibit excessive proliferation of the fibroblasts in the filtering bleb to reduce scar formation after glaucoma filtration surgery in rabbits. Rapamycin also increases the expressions of caspase-3 and caspase-9 to induce apoptosis of the RTFs.

Alterations in exosomal miRNA profile upon epithelial-mesenchymal transition in human lung cancer cell lines.

BACKGROUND: Epithelial-mesenchymal transition (EMT) is regarded as a critical event during tumor metastasis. Recent studies have revealed changes and the contributions of proteins in/on exosomes during EMT. Besides proteins, microRNA (miRNA) is another important functional component of exosomes. We hypothesized that the miRNA profile of exosomes may change following EMT and these exosomal miRNAs may in return promote EMT, migration and invasion of cancer cells. RESULTS: The small RNA profile of exosomes was altered following EMT. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the specific miRNAs of M-exosomes have the potential to drive signal transduction networks in EMT and cancer progression. Co-culture experiments confirmed that M-exosomes can enter epithelial cells and promote migration, invasion and expression of mesenchymal markers in the recipient cells. CONCLUSION: Our results reveal changes in the function and miRNA profile of exosomes upon EMT. M-exosomes can promote transfer of the malignant (mesenchymal) phenotype to epithelial recipient cells. Further, the miRNAs specifically expressed in M-exosomes are associated with EMT and metastasis, and may serve as new biomarkers for EMT-like processes in lung cancer.

Metabolic memory in mitochondrial oxidative damage triggers diabetic retinopathy.

BACKGROUND: Diabetic retinopathy (DR) is a microvascular complication induced by high blood glucose. This study was conducted to investigate the effect of metabolic memory on mitochondrial oxidative damage-induced DR. METHODS: Rat retinal endothelial cells (rRECs) were isolated from SD rats and treated with high glucose (20 mM) for various times and then cultured in normal glucose (5.6 mM) medium for 2 days. The cells were assayed for the expression of respiratory chain complexes cytochrome c oxidase subunit 1 (CO1) and NADPH-1 using RT-PCR, mitochondrial membrane potentials and reactive oxygen species (ROS) production using flow cytometry and apoptosis using Annexin V/PI flow cytometry. RESULTS: rRECs displayed like short spindles after cultured for 9-10 days and reached 100% confluency. Compared with the control grown in normal glucose (5.6 mM) medium, rRECs exposed to high glucose medium for 3, 12 and 24 h had significantly increased mRNA levels of CO1 and NAPDH-1 even after being shifted back to normal glucose medium. They also had lower mitochondrial membrane potential (89.13% vs 78.21%, p < 0.05), cytochrome C level (1 in control vs 0.25 after 24 h exposure to high glucose, p < 0.05 and higher ROS production (2.77% in control vs 9.00% after 12 h exposure to high glucose, p < 0.05) and apoptosis (7.15% in control vs and 29.91% after 24 h exposure to high glucose, p < 0.05). CONCLUSION: It is likely that mitochondrial oxidative damage triggers metabolic memory via ROS overproduction, leading to diabetic retinopathy.

[Progress and analysis methods of clinical application of extracellular vesicles].

OBJECTIVE: Extracellular vesicles (EVs) are small vesicles released by cells, which contain proteins and miRNA. It is a new research field in recent years. EVs change accordingly in a variety of diseases. These vesicles can sensitively reflect the pathological changes of the body. Compared with tissue biopsy, EVs detection have the advantages of non-invasive, simple sampling and real-time monitoring. EVs are becoming new diagnostic marker. This article reviews the current status and progress of EVs in clinical application.

Comparison of isolation methods of exosomes and exosomal RNA from cell culture medium and serum.

Exosomes are cell-derived vesicles and are abundant in biological fluids; they contain RNA molecules which may serve as potential diagnostic biomarkers in 'precision medicine'. To promote the clinical application of exosomal RNA (exoRNA), many isolation methods must be compared and validated. Exosomes in cell culture medium (CCM) and serum may be isolated using ultracentrifugation (UC), ExoQuick or Total Exosome Isolation Reagent (TEI), and exoRNA may be extracted using TRIzol-LS, SeraMir, Total Exosome RNA Isolation (TER), HiPure Liquid RNA/miRNA kit (HLR), miRNeasy or exoRNeasy. ExoRNA was assessed using NanoDrop, Bioanalyzer 2100, quantitative polymerase chain reaction and high-throughput sequencing. UC showed the lowest recovery of particles, but the highest protein purity for exosome isolation. For isolation of exoRNA, we found that combinations of the TEI and TER methods resulted in high extraction efficiency and purity of small RNA obtained using CCM. High yield and a narrow size distribution pattern of small RNA were shown in exoRNA isolated by exoRNeasy from serum. In RNA profile analysis, the small RNA constituent ratio, miRNA content and amount varied as a result of methodological differences. This study showed that different methods may introduce variations in the concentration, purity and size of exosomes and exoRNA. Herein we discuss the advantages and disadvantages of each method and their application to different materials, therefore providing a reference according to research design.

Progress and analysis methods of clinical application of extracellular vesicles / 南方医科大学学报

Extracellular vesicles (EVs) are small vesicles released by cells, which contain proteins and miRNA. It is a new research field in recent years. EVs change accordingly in a variety of diseases. These vesicles can sensitively reflect the pathological changes of the body. Compared with tissue biopsy, EVs detection have the advantages of non-invasive, simple sampling and real-time monitoring. EVs are becoming new diagnostic marker. This article reviews the current status and progress of EVs in clinical application.

PDE Based Algorithms for Smooth Watersheds.

Watershed segmentation is useful for a number of image segmentation problems with a wide range of practical applications. Traditionally, the tracking of the immersion front is done by applying a fast sorting algorithm. In this work, we explore a continuous approach based on a geometric description of the immersion front which gives rise to a partial differential equation. The main advantage of using a partial differential equation to track the immersion front is that the method becomes versatile and may easily be stabilized by introducing regularization terms. Coupling the geometric approach with a proper "merging strategy" creates a robust algorithm which minimizes over- and under-segmentation even without predefined markers. Since reliable markers defined prior to segmentation can be difficult to construct automatically for various reasons, being able to treat marker-free situations is a major advantage of the proposed method over earlier watershed formulations. The motivation for the methods developed in this paper is taken from high-throughput screening of cells. A fully automated segmentation of single cells enables the extraction of cell properties from large data sets, which can provide substantial insight into a biological model system. Applying smoothing to the boundaries can improve the accuracy in many image analysis tasks requiring a precise delineation of the plasma membrane of the cell. The proposed segmentation method is applied to real images containing fluorescently labeled cells, and the experimental results show that our implementation is robust and reliable for a variety of challenging segmentation tasks.

Reconstructing Open Surfaces via Graph-Cuts.

A novel graph-cuts-based method is proposed for reconstructing open surfaces from unordered point sets. Through a Boolean operation on the crust around the data set, the open surface problem is translated to a watertight surface problem within a restricted region. Integrating the variational model, Delaunay-based tetrahedral mesh and multiphase technique, the proposed method can reconstruct open surfaces robustly and effectively. Furthermore, a surface reconstruction method with domain decomposition is presented, which is based on the new open surface reconstruction method. This method can handle more general surfaces, such as nonorientable surfaces. The algorithm is designed in a parallel-friendly way and necessary measures are taken to eliminate cracks and conflicts between the subdomains. Numerical examples are included to demonstrate the robustness and effectiveness of the proposed method on watertight, open orientable, open nonorientable surfaces and combinations of such.

A weighted dictionary learning model for denoising images corrupted by mixed noise.

This paper proposes a general weighted l(2)-l(0) norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.

A direct approach toward global minimization for multiphase labeling and segmentation problems.

This paper intends to extend the minimization algorithm developed by Bae, Yuan and Tai [IJCV, 2011] in several directions. First, we propose a new primal-dual approach for global minimization of the continuous Potts model with applications to the piecewise constant Mumford-Shah model for multiphase image segmentation. Different from the existing methods, we work directly with the binary setting without using convex relaxation, which is thereby termed as a direct approach. Second, we provide the sufficient and necessary conditions to guarantee a global optimum. Moreover, we provide efficient algorithms based on a reduction in the intermediate unknowns from the augmented Lagrangian formulation. As a result, the underlying algorithms involve significantly fewer parameters and unknowns than the naive use of augmented Lagrangian-based methods; hence, they are fast and easy to implement. Furthermore, they can produce global optimums under mild conditions.

Graph cuts for curvature based image denoising.

Minimization of total variation (TV) is a well-known method for image denoising. Recently, the relationship between TV minimization problems and binary MRF models has been much explored. This has resulted in some very efficient combinatorial optimization algorithms for the TV minimization problem in the discrete setting via graph cuts. To overcome limitations, such as staircasing effects, of the relatively simple TV model, variational models based upon higher order derivatives have been proposed. The Euler's elastica model is one such higher order model of central importance, which minimizes the curvature of all level lines in the image. Traditional numerical methods for minimizing the energy in such higher order models are complicated and computationally complex. In this paper, we will present an efficient minimization algorithm based upon graph cuts for minimizing the energy in the Euler's elastica model, by simplifying the problem to that of solving a sequence of easy graph representable problems. This sequence has connections to the gradient flow of the energy function, and converges to a minimum point. The numerical experiments show that our new approach is more effective in maintaining smooth visual results while preserving sharp features better than TV models.

Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor.

In this paper, we propose an interactive color natural image segmentation method. The method integrates color feature with multiscale nonlinear structure tensor texture (MSNST) feature and then uses GrabCut method to obtain the segmentations. The MSNST feature is used to describe the texture feature of an image and integrated into GrabCut framework to overcome the problem of the scale difference of textured images. In addition, we extend the Gaussian Mixture Model (GMM) to MSNST feature and GMM based on MSNST is constructed to describe the energy function so that the texture feature can be suitably integrated into GrabCut framework and fused with the color feature to achieve the more superior image segmentation performance than the original GrabCut method. For easier implementation and more efficient computation, the symmetric KL divergence is chosen to produce the estimates of the tensor statistics instead of the Riemannian structure of the space of tensor. The Conjugate norm was employed using Locality Preserving Projections (LPP) technique as the distance measure in the color space for more discriminating power. An adaptive fusing strategy is presented to effectively adjust the mixing factor so that the color and MSNST texture features are efficiently integrated to achieve more robust segmentation performance. Last, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. Experiments using synthesis texture images and real natural scene images demonstrate the superior performance of our proposed method.

A unified framework for automated 3-d segmentation of surface-stained living cells and a comprehensive segmentation evaluation.

This work presents a unified framework for whole cell segmentation of surface stained living cells from 3-D data sets of fluorescent images. Every step of the process is described, image acquisition, prefiltering, ridge enhancement, cell segmentation, and a segmentation evaluation. The segmentation results from two different automated approaches for segmentation are compared to manual segmentation of the same data using a rigorous evaluation scheme. This revealed that combination of the respective cell types with the most suitable microscopy method resulted in high success rates up to 97%. The described approach permits to automatically perform a statistical analysis of various parameters from living cells.

Level set method for positron emission tomography.

In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. Expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications.

Automated detection of tunneling nanotubes in 3D images.

BACKGROUND: This paper presents an automated method for the identification of thin membrane tubes in 3D fluorescence images. These tubes, referred to as tunneling nanotubes (TNTs), are newly discovered intercellular structures that connect living cells through a membrane continuity. TNTs are 50-200 nm in diameter, crossing from one cell to another at their nearest distance. In microscopic images, they are seen as straight lines. It now emerges that the TNTs represent the underlying structure of a new type of cell-to-cell communication. METHODS: Our approach for the identification of TNTs is based on a combination of biological cell markers and known image processing techniques. Watershed segmentation and edge detectors are used to find cell borders, TNTs, and image artifacts. Mathematical morphology is employed at several stages of the processing chain. Two image channels are used for the calculations to improve classification of watershed regions into cells and background. One image channel displays cell borders and TNTs, the second is used for cell classification and displays the cytoplasmic compartments of the cells. The method for cell segmentation is 3D, and the TNT detection incorporates 3D information using various 2D projections. RESULTS: The TNT- and cell-detection were applied to numerous 3D stacks of images. A success rate of 67% was obtained compared with manual identification of the TNTs. The digitalized results were used to achieve statistical information of selected properties of TNTs. CONCLUSION: To further explore these structures, automated detection and quantification is desirable. Consequently, this automated recognition tool will be useful in biological studies on cell-to-cell communication where TNT quantification is essential.