LncRNA DGCR5 promotes non-small cell lung cancer progression via sponging miR-218-5p.

Since this article has been suspected of research misconduct and the corresponding authors did not respond to our request to prove originality of data and figures, "LncRNA DGCR5 promotes non-small cell lung cancer progression via sponging miR-218-5p, by J. Wang, H.-Z. Shu, C.-Y. Xu, S.-G. Guo, published in Eur Rev Med Pharmacol Sci 2019; 23 (22): 9947-9954-DOI: 10.26355/eurrev_201911_19561-PMID: 31799664" has been withdrawn. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/19561.

LncRNA DGCR5 promotes non-small cell lung cancer progression via sponging miR-218-5p.

OBJECTIVE: Non-small cell lung cancer (NSCLC) is one of the most ordinary malignant tumors worldwide. Recent researches have proved that long noncoding RNAs (lncRNAs) play vital roles in many diseases. The aim of this study was to investigate the exact function of lncRNA DiGeorge syndrome critical region gene 5 (DGCR5) in the development of NSCLC. PATIENTS AND METHODS: Real Time-quantitative Polymerase Chain Reaction (RT-qPCR) was utilized to detect DGCR5 expression in paired NSCLC patients' tissue samples and cell lines. The function of DGCR5 in NSCLC was detected through wound healing assay and transwell assay in vitro. Besides, mechanism assays were conducted to observe the interaction between DGCR5 and microRNA-218-5p (miR-218-5p). RESULTS: DGCR5 was remarkably highly expressed in NSCLC tissues compared to that of adjacent normal tissues. The migration and invasion of NSCLC cells were significantly promoted via overexpression of DGCR5. However, the silence of DGCR5 significantly inhibited NSCLC cell migration and invasion. Moreover, RT-qPCR results revealed that miR-218-5p was down-regulated via overexpression of DGCR5, while miR-218-5p was up-regulated after the knockdown of DGCR5. Further experiments showed that miR-218-5p was a direct target of DGCR5 in NSCLC. CONCLUSIONS: DGCR5 enhances NSCLC cell migration and invasion via targeting miR-218-5p, indicating that DGCR5 may be a potential therapeutic target in NSCLC.

Computed tomography image source identification by discriminating CT-scanner image reconstruction process.

In this paper, we focus on the identification of the Computed Tomography (CT) scanner that has produced a CT image. To do so, we propose to discriminate CT-Scanner systems based on their reconstruction process, the footprint or the signature of which can be established based on the way they modify the intrinsic sensor noise of X-ray detectors. After having analyzed how the sensor noise is modified in the reconstruction process, we define a set of image features so as to serve as CT acquisition system footprint. These features are used to train a SVM based classifier. Experiments conducted on images issued from 15 different CT-Scanner models of 4 distinct manufacturers show it is possible to identify the origin of one CT image with high accuracy.

Medical image integrity control and forensics based on watermarking--approximating local modifications and identifying global image alterations.

In this paper we present a medical image integrity verification system that not only allows detecting and approximating malevolent local image alterations (e.g. removal or addition of findings) but is also capable to identify the nature of global image processing applied to the image (e.g. lossy compression, filtering …). For that purpose, we propose an image signature derived from the geometric moments of pixel blocks. Such a signature is computed over regions of interest of the image and then watermarked in regions of non interest. Image integrity analysis is conducted by comparing embedded and recomputed signatures. If any, local modifications are approximated through the determination of the parameters of the nearest generalized 2D Gaussian. Image moments are taken as image features and serve as inputs to one classifier we learned to discriminate the type of global image processing. Experimental results with both local and global modifications illustrate the overall performances of our approach.

Blind forensics in medical imaging based on Tchebichef image moments.

In this paper, we present a blind forensic approach for the detection of global image modifications like filtering, lossy compression, scaling and so on. It is based on a new set of image features we proposed, called Histogram statistics of Reorganized Block-based Tchebichef moments (HRBT) features, and which are used as input of a set of classifiers we learned to discriminate tampered images from original ones. In this article, we compare the performances of our features with others proposed schemes from the literature in application to different medical image modalities (MRI, X-Ray …). Experimental results show that our HRBT features perform well and in some cases better than other features.

Medical image integrity control seeking into the detail of the tampering.

In this paper, we propose a system which aims at verifying integrity of medical images. It not only detects and localizes alterations, but also seeks into the details of the image modification to understand what occurred. For that latter purpose, we developed an image signature which allows our system to approximate modifications by a simple model, a door function of similar dimensions. This signature is partly based on a linear combination of the DCT coefficients of pixel blocks. Protection data is attached to the image by watermarking. Whence, image integrity verification is conducted by comparing this embedded data to the recomputed one from the observed image. Experimental results with malicious image modification illustrate the overall performances of our system.

Improved SAGE algorithm for PET image reconstruction using rescaled block-iterative method.

Iterative reconstruction methods such as the expectation maximization maximum likelihood (EMML) method can be accelerated by using a rescaled block-iterative (RBI) algorithm. It was demonstrated that the space-alternating generalized expectation-maximization (SAGE) algorithm is superior to the EMML due to the following facts: (1) The hidden data spaces can be appropriately chosen and then be used in SAGE algorithm to speed up the convergence rate. (2) SAGE algorithm updates the parameters sequentially which makes its M-step to be treated more easily. In this paper, we present a novel algorithm that combines the RBI algorithm with SAGE algorithm. The convergence property of RBI-SAGE is discussed, and the image quality is assessed with mean absolute error and chi-square error. The experimental results show that the proposed method is more effective than the SAGE algorithm even if the projection data includes statistic noise.

Maximum likelihood algorithm for PET image reconstruction based on fuzzy random variable.

This work presents a new iterative method for reconstructing positron emission tomography (PET) images. Unlike conventional maximum likelihood-expectation maximization (MLEM), this method intends to introduce the fuzzy set principle to MLEM algorithm. In this work, the noncognitive uncertainty of the observed projection data are described by their probability density function; whereas the cognitive uncertainty of a random variable can be described by the membership function for its fuzziness. The mean of the observed projection data are regard as fuzzy random variables because of the complexity of system. The fuzzy random variable can be represented by a triangular membership function. We establish a joint probability density function that includes the effects of both fuzziness and randomness. The maximum likelihood approach is used to estimate the image vector. The order subset (OS), rescaled block-iterative (RBI), and row-action (RA) techniques are applied to our PET reconstructed method to speed up the convergence rate and to decrease the iteration numbers.

An orthogonal moment-based method for automatic verification of radiation field shape.

The purpose of this paper is to develop a new method for automated on-line verification of the treatment field shape during radiotherapy. The treatment field boundary is extracted from the digital portal image and is then approximated by a polygon. The proposed procedure used one of the approved field shapes as the reference boundary for automated comparison with subsequent portal field boundaries. The orthogonal moment-based method was applied to align treatment field boundaries that include the translational shifts, scaling factor and rotation angle. Firstly, the moments of order up to one were used to adjust the magnification and translation of the test field boundary related to the reference one; this step created a common coordinate system for the two images. Then a quadratic least-square objective function based on the orthogonal moments (e.g. Legendre moments) of the two field shapes was employed to perform rotational correction. Since moment computation by a straightforward method required a large number of multiplication and addition operations, a fast method for computing Legendre moments was also developed to decrease the calculation time. Application of the method to some simulated cases showed that our alignment procedure has an accuracy of 0.5 mm in detecting translational shift, 0.004 in detecting magnification and less than 0.3 degrees in detecting rotation angle between the test shape and the reference shape. The alignment procedure using the proposed method can be done within 2 s on a Pentium II personal computer. Therefore, our method is potentially useful for automated real-time treatment field shape verification.

Treatment planning optimization by quasi-Newton and simulated annealing methods for gamma unit treatment system.

The gamma unit is used to irradiate a target within the brain. During such a treatment many parameters, including the number of shots, the coordinates, the collimator size and the weight associated with each shot, affect the amount of dose delivered to the target volume and to the surrounding normal tissues. Hence it is not easy to determine an appropriate set of these parameters by a trial and error method. For this reason, we present here an optimization method to determine mathematically those parameters. This method is composed of two steps: firstly, a quasi-Newton method is used to deal with the continuous variables such as position and weight of shots; the result obtained at the end of this step then serves as the initial configuration for the next step, in which a simulated annealing method is applied to optimize all the aforementioned parameters. Application of the proposed methods to two examples shows that our optimization algorithm runs in a satisfactory way.