The role of microRNA-induced apoptosis in diverse radioresistant cancers.

Resistance to cancer radiotherapy is one of the biggest concerns for success in treating and preventing recurrent disease. Malignant tumors may develop when they block genetic mutations associated with apoptosis or abnormal expression of apoptosis; Tumor treatment may induce the expression of apoptosis-related genes to promote tumor cell apoptosis. MicroRNAs have been shown to contribute to forecasting prognosis, distinguishing between cancer subtypes, and affecting treatment outcomes in cancer. Constraining these miRNAs may be an attractive treatment strategy to help overcome radiation resistance. The delivery of these future treatments is still challenging due to the excess downstream targets that each miRNA can control. Understanding the role of miRNAs brings us one step closer to attaining patient treatment and improving patient outcomes. This review summarized the current information on the role of microRNA-induced apoptosis in determining the radiosensitivity of various cancers.

A Framework for Detecting System Performance Anomalies Using Tracing Data Analysis.

Advances in technology and computing power have led to the emergence of complex and large-scale software architectures in recent years. However, they are prone to performance anomalies due to various reasons, including software bugs, hardware failures, and resource contentions. Performance metrics represent the average load on the system and do not help discover the cause of the problem if abnormal behavior occurs during software execution. Consequently, system experts have to examine a massive amount of low-level tracing data to determine the cause of a performance issue. In this work, we propose an anomaly detection framework that reduces troubleshooting time, besides guiding developers to discover performance problems by highlighting anomalous parts in trace data. Our framework works by collecting streams of system calls during the execution of a process using the Linux Trace Toolkit Next Generation(LTTng), sending them to a machine learning module that reveals anomalous subsequences of system calls based on their execution times and frequency. Extensive experiments on real datasets from two different applications (e.g., MySQL and Chrome), for varying scenarios in terms of available labeled data, demonstrate the effectiveness of our approach to distinguish normal sequences from abnormal ones.

Cytogenetic effects of radiation and genetic polymorphisms of the XRCC1 and XRCC3 repair genes in industrial radiographers.

Different types of DNA damages caused by ionizing radiation may enhance the cancer risk in exposed individuals. Inherited variations in DNA repair genes cause the inter-individual variability in response to ionizing radiation. The purpose of this study was to determine the association between single nucleotide polymorphism (SNP) of two important DNA repair genes (XRCC1 R399Q and XRCC3 T241M) and the level of DNA damage investigated by micronucleus (MN) frequency in peripheral blood lymphocytes of 120 industrial radiographers (IR) and 120 non-exposed control individuals. The frequencies of MN and nucleoplasmic bridges were significantly higher in the IR group than in the control group (33.83 ± 11.96 vs. 7.47 ± 2.96, p < 0.0001 and 1.69 ± 1.86 vs. 0.12 ± 0.33, p < 0.0001). MN frequencies in the IR group were associated with the cumulative radiation doses (p < 0.0001, r = 0.58 for last 1 year of exposure and p < 0.0001, r = 0.67 for last 5 years of exposure). Polymorphism of XRCC3 T241M was associated with higher MN frequencies in the IR group. However, the same result was not observed between XRCC3 SNP and MN frequency in the control group. Consequently, XRCC3 241Met alleles may cause the increased DNA damage in the industrial radiographers.

Learning normalized inputs for iterative estimation in medical image segmentation.

In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving accurate segmentations on a variety of image modalities and different anatomical regions.

Sub-cortical brain morphometry and its relationship with cognition in rolandic epilepsy.

PURPOSE: Rolandic epilepsy (RE), also called benign epilepsy with centrotemporal spikes (BECTS) is the most common childhood epilepsy syndrome. RE is associated with cognitive difficulties, which can affect children's quality of life. The underlying causes of these cognitive impairments are unclear. The objective of this prospective study is to investigate sub-cortical morphological alterations in RE children with left, right, or bilateral hemispheric focus and its association with cognition. METHODS: Participants include 41 children with rolandic epilepsy and 38 healthy controls (age 8-14 years), recruited from CHU Sainte-Justine Montreal Children Hospital (N=40) and Basel's Children Hospital (N=39). Quantitative volumetric assessment of putamen and caudate structures was performed on T1-weighted MR scans along with the morphological analysis to test for differences between patients and controls. These analyses were performed considering the side of epilepsy focus in all participants. Correlations were investigated between the sub-cortical morphometry and cognitive indices such as intelligence quotient (IQ), verbal comprehension index (VCI), perceptual reasoning index (PRI), working memory index (WMI), and processing speed index (PSI). RESULTS: Children with bilateral BECTS showed statistically significant volume reduction in right caudate (p<.05), while no statistically significant putamen volumetric changes were detected in BECTS participants compared to normal controls. According to a spectral-based groupwise shape analysis, regional alterations were found in both putamen and caudate structures of children with BECTS. In particular, children with left BECTS showed significant outward local deformity in left putamen and individuals with bilateral BECTS showed inward local group differences in both right putamen and right caudate. The correlation assessment showed positive association between the volume of the left caudate and cognitive indices in the group containing all BECTS participants. Negative correlation was found between putamen sub-regional shape alterations and cognition in individuals with right BECTS and in all BECTS participants. Negative associations between caudate sub-regional morphologies and cognitive indices were detected in left cohort. SIGNIFICANCE: We have confirmed putamen and caudate shape alterations in children with BECTS. However, our results further suggest that variations in sub-cortical shape affect cognitive functions. Importantly, we have demonstrated that shape alterations and their relation with cognition depend on the side of epilepsy focus. Our results point to different syndromic entities in the BECTS population.

A cytogenetic biomonitoring of industrial radiographers occupationally exposed to low levels of ionizing radiation by using cbmn assay.

Industrial radiography is the process of using either gamma-emitting radionuclide sources or X-ray machines to examine the safety of industrial materials. The average annual effective dose in industrial radiography is one of the highest among radiation workers. The aim of this study was to investigate the cytogenetic effects of ionizing radiation in the peripheral blood lymphocytes of 60 industrial radiographers and 40 non-exposed individuals as the control group by using cytokinesis-block micronucleus (CBMN) assay. Totally, the frequencies of micronuclei (MN), nucleoplasmic bridges (NPBs) and nuclear buds (NBUDs) were significantly higher in the industrial radiographers than in the controls (p = 0.000). The mean MN frequency per 1000 binucleated cells in the industrial radiographers with last 5-y radiation dose of >100 mSv was significantly higher than those with ≤100 mSv (34.81 ± 12.7‰ vs. 26.33 ± 7.94‰, p = 0.024). The effect of age was observed in the control group and subjects with the age of >30 y showed significantly higher MN frequency compared with the subjects with the age of ≤30 y (9.45 ± 3.71‰ vs. 6.81 ± 3.05‰, p = 0.02). No obvious trend of increased MN as a function of either duration of employment or age or smoking status was observed in the industrial radiographers. The results show the increased levels of cytogenetic damages in the industrial radiographers. Even the workers exposed to the permissible doses are subjected to elevated frequencies of DNA damages. These findings confirm the importance of cytogenetic biomonitoring program beside physical dosimetry, surveying radiation safety of equipment and periodic training of workers for improvement of safety and radiation protection.

PHYSICIANS' KNOWLEDGE ABOUT RADIATION DOSE AND POSSIBLE RISKS OF COMMON MEDICAL TESTS: A SURVEY IN IRAN.

Recent data suggest that knowledge of radiation exposures among physicians is inadequate. This study, therefore, aimed to evaluate their knowledge of the radiation doses their patients received and awareness of associated biological risks of radiation exposure. A questionnaire in multiple-choice format consisted of four sections with a total of 10 questions based on the literature review. A total of 136 questionnaires were returned from 69 general practitioners and 67 physicians in various specialties from 10 different hospitals in the capital city of Tehran, Iran. Fifty-four percent of general practitioners and twenty-five percent of specialties declared that they are not aware of biological risks of radiation exposure. Fifty-six percent of physicians did not know the correct definition of absorbed dose. Only 33% of physicians knew the dose exposure of a chest X-ray and only 31% knew the approximate doses of various procedures relative to a chest X-ray. Forty-seven percent of physicians incorrectly distinguished the stochastic effects of radiation from the deterministic effects, and thirty-eight of physicians did not know the organs of the body that are most sensitive to ionizing radiation. Only 23.5% of physicians were aware of the one in 2000 risk of induction of fatal carcinoma from computed tomography of the abdomen. Seventy-nine percent of physicians incorrectly underestimated the contribution of nuclear and radiological tests in exposure of an average person. The mean score of the specialties trended toward being more accurate than general practitioners (4.18 ± 1.28 vs. 3.89 ± 1.46, respectively, from a potential accurate total score of 9), but these differences were not statistically significant. Among specialists, orthopedics had the highest scores. The present study demonstrated the limited knowledge of radiation exposures among general practitioners and specialists and a need to improve their knowledge by means of targeted training and re-education.

Statistical shape analysis of subcortical structures using spectral matching.

Studying morphological changes of subcortical structures often predicate neurodevelopmental and neurodegenerative diseases, such as Alzheimer's disease and schizophrenia. Hence, methods for quantifying morphological variations in the brain anatomy, including groupwise shape analyses, are becoming increasingly important for studying neurological disorders. In this paper, a novel groupwise shape analysis approach is proposed to detect regional morphological alterations in subcortical structures between two study groups, e.g., healthy and pathological subjects. The proposed scheme extracts smoothed triangulated surface meshes from segmented binary maps, and establishes reliable point-to-point correspondences among the population of surfaces using a spectral matching method. Mean curvature features are incorporated in the matching process, in order to increase the accuracy of the established surface correspondence. The mean shapes are created as the geometric mean of all surfaces in each group, and a distance map between these shapes is used to characterize the morphological changes between the two study groups. The resulting distance map is further analyzed to check for statistically significant differences between two populations. The performance of the proposed framework is evaluated on two separate subcortical structures (hippocampus and putamen). Furthermore, the proposed methodology is validated in a clinical application for detecting abnormal subcortical shape variations in Alzheimer's disease. Experimental results show that the proposed method is comparable to state-of-the-art algorithms, has less computational cost, and is more sensitive to small morphological variations in patients with neuropathologies.