Effect of Carvacrol on the Biological Behavior of Leukemia Cells and Its Mechanism / 中国实验血液学杂志

OBJECTIVE@#To explore the effect of carvacrol on the biological behavior of leukemia cells and its regulation to circ-0008717/miR-217 molecular axis.@*METHODS@#Human acute lymphoblastic leukemia cells Molt-4 were cultured in vitro, and different concentrations of carvacrol were added to the cells. si-NC and si-circ-0008717 were transfected into Molt-4 cells (si-NC group, si-circ-0008717 group). pcDNA, pcDNA-circ-0008717, anti-miR-NC, anti-miR-217 were transfected into Molt-4 cells and then added to carvacrol-treated cells (carvacrol+pcDNA group, carvacrol+pcDNA-circ-0008717 group, carvacrol+anti-miR-NC group, carvacrol+anti-miR-217 group). MTT, plate clone formation experiment, and flow cytometry were used to detect the viability of the cell, colony formation number, and apoptosis rate of cells, respectively. The RT-qPCR method was used to detect the expression levels of circ-0008717 and miR-217. The dual luciferase reporter gene experiment was used to detect the targeting relationship between circ-0008717 and miR-217.@*RESULTS@#After carvacrol treatment, the cell viability decreased significantly (r=-0.9405), expression level of circ-0008717 decreased (r=-0.9117), colonies formed number decreased (r=-0.9256), while the cell apoptosis rate increased (r= 0.8464), and the expression level of miR-217 increased (r=0.9468). Compared with the si-NC group, the expression level of miR-217 in si-circ-0008717 group increased (P＜0.001), the cell apoptosis rate increased (P＜0.001), while cell viability decreased (P＜0001), the number of colonies formed decreased (P＜0.001). Compared with the carvacrol+pcDNA group, the cell viability of the carvacrol+pcDNA-circ-0008717 group increased (P＜0.001), the number of colonies formed increased (P＜0.001), while the cell apoptosis rate decreased (P＜0.001). circ-0008717 could target miR-217. The cell viability of the carvacrol+anti-miR-217 group increased (P＜0.001), and the number of colonies formed increased (P＜0.001), while the cell apoptosis rate decreased (P＜0001) as compared with the carvacrol+anti-miR-NC group.@*CONCLUSION@#Carvacrol can promote the expression of miR-217 by down-regulating the expression of circ-0008717, thereby reducing the proliferation and cloning ability of leukemia cells and promoting cell apoptosis.

Learning the Distribution Preserving Semantic Subspace for Clustering.

This paper proposes a new clustering method for images called distribution preserving indexing (DPI). It aims to find a lower dimensional semantic space approximating the original image space in the sense of preserving the distribution of the data. In the theory, the intrinsic structure of the data clusters can be described by the distribution of the data effectively. Therefore, the cluster structure of the data in a lower dimensional semantic space derived by the DPI becomes clear. Unlike these distance-based clustering methods, which reveal the intrinsic Euclidean structure of data, our method attempts to discover the intrinsic cluster structure of the data space that actually is the union of some sub-manifolds. Moreover, we propose a revised kernel density estimator for the case of high-dimensional data, which is a crucial step in DPI. In addition, we provide a theoretical analysis of the bound of our method. Finally, the extensive experiments compared with other algorithms, on COIL20, CBCL, and MNIST demonstrate the effectiveness of our proposed approach.

Noise Level Estimation for Natural Images Based on Scale-Invariant Kurtosis and Piecewise Stationarity.

Noise level estimation is crucial in many image processing applications, such as blind image denoising. In this paper, we propose a novel noise level estimation approach for natural images by jointly exploiting the piecewise stationarity and a regular property of the kurtosis in bandpass domains. We design a K-means-based algorithm to adaptively partition an image into a series of non-overlapping regions, each of whose clean versions is assumed to be associated with a constant, but unknown kurtosis throughout scales. The noise level estimation is then cast into a problem to optimally fit this new kurtosis model. In addition, we develop a rectification scheme to further reduce the estimation bias through noise injection mechanism. Extensive experimental results show that our method can reliably estimate the noise level for a variety of noise types, and outperforms some state-of-the-art techniques, especially for non-Gaussian noises.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Cross-Domain Recognition by Identifying Joint Subspaces of Source Domain and Target Domain.

This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between the source and target domains, the proposed method is more flexible to capture individual class variations across domains. By adopting a natural and widely used assumption that the data samples from the same class should lay on an intrinsic low-dimensional subspace, even if they come from different domains, the proposed method circumvents the limitation of the global domain shift, and solves the cross-domain recognition by finding the joint subspaces of the source and target domains. Specifically, given labeled samples in the source domain, we construct a subspace for each of the classes. Then we construct subspaces in the target domain, called anchor subspaces, by collecting unlabeled samples that are close to each other and are highly likely to belong to the same class. The corresponding class label is then assigned by minimizing a cost function which reflects the overlap and topological structure consistency between subspaces across the source and target domains, and within the anchor subspaces, respectively. We further combine the anchor subspaces to the corresponding source subspaces to construct the joint subspaces. Subsequently, one-versus-rest support vector machine classifiers are trained using the data samples belonging to the same joint subspaces and applied to unlabeled data in the target domain. We evaluate the proposed method on two widely used datasets: 1) object recognition dataset for computer vision tasks and 2) sentiment classification dataset for natural language processing tasks. Comparison results demonstrate that the proposed method outperforms the comparison methods on both datasets.

Application of Real-time Quantitative PCR in Detecting Atypical BCR/ABL mRNA Transcripts in Chronic Myelocytic Leukemia / 中国实验血液学杂志

<p><b>OBJECTIVE</b>To detect atypical BCR/ABL mRNA transcript by real-time quantitative PCR in CML patients without e13a2/e14a2,e19a2 or e1a2 transcripts, and investigate its value of clinical application.</p><p><b>METHODS</b>Twelve cases of CML with positive for t(9;22) translocation, but negative for common major and minor breakpoint cluster regions comfirmed by chromosome karyotyping or FISH analysis, were collected from July 2012 to December 2015. These 12 cases were then detected for b2a3(e13a3), b3a3(e14a3), e6a2, e8a2 and e1a3 fusion variants by real-time quantitative PCR.</p><p><b>RESULTS</b>Among 12 cases 4 variant transcripts were detected, including e1a3 in 1 case (8.33%), e8a2 in 2 cases (16.67%), b2a3 in 5 cases (41.67%) and b3a3 in 4 cases (33.33%), with total positivity of 100%, moreover b2a3 and b3a3 were predominant.</p><p><b>CONCLUSION</b>The detecting atypical BCR/ABL mRNA transcripts by real-time quantitative PCR is suitable for the diagnosis of CML negative for P210, P190 and P230 by standard real-time PCR test, and this detection is still the standard and economic method for monitoring minimal residual disease in CML patients with variants of BCR/ABL fusion gene.</p>

Person Re-Identification by Dual-Regularized KISS Metric Learning.

Person re-identification aims to match the images of pedestrians across different camera views from different locations. This is a challenging intelligent video surveillance problem that remains an active area of research due to the need for performance improvement. Person re-identification involves two main steps: feature representation and metric learning. Although the keep it simple and straightforward (KISS) metric learning method for discriminative distance metric learning has been shown to be effective for the person re-identification, the estimation of the inverse of a covariance matrix is unstable and indeed may not exist when the training set is small, resulting in poor performance. Here, we present dual-regularized KISS (DR-KISS) metric learning. By regularizing the two covariance matrices, DR-KISS improves on KISS by reducing overestimation of large eigenvalues of the two estimated covariance matrices and, in doing so, guarantees that the covariance matrix is irreversible. Furthermore, we provide theoretical analyses for supporting the motivations. Specifically, we first prove why the regularization is necessary. Then, we prove that the proposed method is robust for generalization. We conduct extensive experiments on three challenging person re-identification datasets, VIPeR, GRID, and CUHK 01, and show that DR-KISS achieves new state-of-the-art performance.

Regulatory effect of curcumin on p300 and HDAC1 in B-NHL cells / 中国实验血液学杂志

The purpose of this study was to investigate the effect of curcumin on proliferation of B-NHL Raji cell line and explore the relationship between this effect and regulatory expression of p300 and HDAC1 transcription. The in vitro cultured Raji cells were treated with curcumin at various concentrations (6.25-50 micromol/L) and at different time points (0, 6, 12, 24 and 48 hours), the inhibitory ratio of cell growth was measured by MTT assay, the cell apoptosis rate was detected by flow cytometry with Annexin V-FITC/PI double staining, the changes of p300 and HDAC1 mRNA expression and protein level in Raji cells were determined by RT-PCR and Western blot. The results showed that the curcumin could inhibit Raji cell proliferation in significant time-and concentration-dependent manners, IC50 at 24 hours was 25 micromol/L; the curcumin could induce apoptosis of Raji cells in concentration-dependent manner, apoptosis rate was 14.38%-61.18%. The curcumin significantly inhibited activity and expression of p300 and HDAC1. At IC50 concentration, expression of p300 and HDAC1 mRNA and protein level decreased with time-dependent manner, difference between tested and control groups was significant (P < 0.05). It is concluded that the curcumin can inhibit proliferation of B-NHL Raji cells and promote apoptosis of those cells. Curcumin can inhibit the activity and expression of the transcriptional co-activator p300 and HDAC1, which may be involved in its pharmacological mechanisms on B lymphoma cells.