The Congruity/Incongruity of EFL Teachers’ Beliefs about Listening Instruction and their Listening Instructional Practices

While research on EFL teachers’ beliefs and the realization of these beliefs in their classroom practices has recently gained momentum in the field of applied linguistics, the study of teachers’ beliefs as they relate to listening has received insufficient attention in the literature. This study was conducted to investigate Iranian EFL teachers’ beliefs about listening and their beliefs-driven instructional practices. To this end, a listening beliefs questionnaire was administered to a total of 85 teachers (BA= 49, MA= 36), followed by classroom observation of 12 teachers (6 teachers per group) who were given an audio to teach. The results revealed that there was no significant difference between BA and MA teachers regarding their listening beliefs and beliefs-driven practices. The results of the Phi coefficient of correlation indicated that there was no significant relationship between teachers’ beliefs about listening instruction and their listening instructional practices. Furthermore, the results of the interview showed that time, besides other impediments, was the major obstacle for teachers to actualize their listening beliefs. The implications of the study for teacher education are discussed

Comparison of Peripapillary Retinal Nerve Fiber Layer Thickness in Patients with MS and Normal Population

Purpose: To compare peripapillary retinal nerve fiber layer thickness (RNFLT) between patients with multiple sclerosis (MS) and healthy controls using optical coherence tomography (OCT).  Patients and Methods: In this prospective case control study, peripapillary RNFLT of 120 eyes from 60 patients with multiple sclerosis (MS)  was compared to 120 eyes from 60 age and sex matched healthy controls using OCT.  The RNFLT in 4 peripapillary quadrants and the mean RNFLT of all four quadrants were compared between the case and control groups. The relation between MS variables such as age of onset, type and duration of disease, history of optic neuritis (ON) and other non-ocular episodes with RNFLT was evaluated in the case group. Results: The mean RNFLT of all four quarters was significantly lower in patients with MS compared to the controls (P < 0.001). Also RNFLT was significantly lower in each of 4 quadrants (superior, temporal, inferior; P < 0.001, nasal P = 0.003). There was no significant relation between RNFLT, the age of onset of MS disease, and history of non-ocular episodes. RNFLT had a significant relation with duration of the disease (P < 0.001), the type of MS (P < 0.001), history of ON (P = 0.002), and the number of ON episodes (P = 0.021). Conclusion: We found that RNFLT decreases in MS patients and its reduction is related to the duration and type of disease as well as history and number of ON episodes. Therefore measuring RNFLT may help in estimating the progress of MS and can potentially be included as a part of patients’ follow up protocol.Keywords: Multiple sclerosis;  Tomography; Optical Coherence;;Optic Neuritis; Retinal; Nerve Fibers

Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics

Purpose: Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms. Materials and methods: One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML algorithms were utilized. The combination of preprocessing, FS, and ML algorithms (12 × 7 × 6 = 504 models) was evaluated by multivariate analysis. Results: Our multivariate analysis showed that the best prognostic FS/ML combinations are the Mutual Information (MI)/Cox Boost, MI/Generalized Linear Model Boosting (GLMB) and MI/Generalized Linear Model Network (GLMN), all of which were done via the LOG (Sigma = 1 mm) preprocessing method (C-index = 0.77). The LOG filter with Sigma = 1 mm preprocessing method, MI, GLMB and GLMN achieved significantly higher C-indices than other preprocessing, FS, and ML methods (all p values &lt; 0.05, mean C-indices of 0.65, 0.70, and 0.64, respectively). Conclusion: ML algorithms are capable of predicting the time-to-event OS of patients using MRI-based radiomic and clinical features. MRI-based radiomics analysis in combination with clinical variables might appear promising in assisting clinicians in the survival prediction of patients with GBM. Further research is needed to establish the applicability of radiomics in the management of GBM in the clinic.</p

Impact of feature harmonization on radiogenomics analysis:Prediction of EGFR and KRAS mutations from non-small cell lung cancer PET/CT images

Objective: To investigate the impact of harmonization on the performance of CT, PET, and fused PET/CT radiomic features toward the prediction of mutations status, for epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS) genes in non-small cell lung cancer (NSCLC) patients. Methods: Radiomic features were extracted from tumors delineated on CT, PET, and wavelet fused PET/CT images obtained from 136 histologically proven NSCLC patients. Univariate and multivariate predictive models were developed using radiomic features before and after ComBat harmonization to predict EGFR and KRAS mutation statuses. Multivariate models were built using minimum redundancy maximum relevance feature selection and random forest classifier. We utilized 70/30% splitting patient datasets for training/testing, respectively, and repeated the procedure 10 times. The area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess model performance. The performance of the models (univariate and multivariate), before and after ComBat harmonization was compared using statistical analyses. Results: While the performance of most features in univariate modeling was significantly improved for EGFR prediction, most features did not show any significant difference in performance after harmonization in KRAS prediction. Average AUCs of all multivariate predictive models for both EGFR and KRAS were significantly improved (q-value &lt; 0.05) following ComBat harmonization. The mean ranges of AUCs increased following harmonization from 0.87-0.90 to 0.92-0.94 for EGFR, and from 0.85-0.90 to 0.91-0.94 for KRAS. The highest performance was achieved by harmonized F_R0.66_W0.75 model with AUC of 0.94, and 0.93 for EGFR and KRAS, respectively. Conclusion: Our results demonstrated that regarding univariate modelling, while ComBat harmonization had generally a better impact on features for EGFR compared to KRAS status prediction, its effect is feature-dependent. Hence, no systematic effect was observed. Regarding the multivariate models, ComBat harmonization significantly improved the performance of all radiomics models toward more successful prediction of EGFR and KRAS mutation statuses in lung cancer patients. Thus, by eliminating the batch effect in multi-centric radiomic feature sets, harmonization is a promising tool for developing robust and reproducible radiomics using vast and variant datasets.</p

An Integrated Bioinformatics Approach to Identify Network Derived Hub Genes in Starving Zebrafish

The present study was aimed at identifying causative hub genes within modules formed by co-expression and protein&ndash;protein interaction (PPI) networks, followed by Bayesian network (BN) construction in the liver transcriptome of starved zebrafish. To this end, the GSE11107 and GSE112272 datasets from the GEO databases were downloaded and meta-analyzed using the MetaDE package, an add-on R package. Differentially expressed genes (DEGs) were identified based upon expression intensity N(&micro; = 0.2, &sigma;2 = 0.4). Reconstruction of BNs was performed by the bnlearn R package on genes within modules using STRINGdb and CEMiTool. ndufs5 (shared among PPI, BN and COEX), rps26, rpl10, sdhc (shared between PPI and BN), ndufa6, ndufa10, ndufb8 (shared between PPI and COEX), skp1, atp5h, ndufb10, rpl5b, zgc:193613, zgc:123327, zgc:123178, wu:fc58f10, zgc:111986, wu:fc37b12, taldo1, wu:fb62f08, zgc:64133 and acp5a (shared between COEX and BN) were identified as causative hub genes affecting gene expression in the liver of starving zebrafish. Future work will shed light on using integrative analyses of miRNA and DNA microarrays simultaneously, and performing in silico and experimental validation of these hub-causative (CST) genes affecting starvation in zebrafish

A Review on the Most Important Medicinal Plants Effective in Cardiac Ischemia-Reperfusion Injury

Ischemia, referring to reduction and restriction of perfusion to myocardial tissue which involves coronary artery through the formation of misplaced clots and thrombosis, is one of the most important cardiovascular diseases. Plant-based compounds help to improve or prevent disease by affecting the factors involved in the disease. This review was conducted to report the medicinal plants and factors effective in cardiac ischemia-reperfusion (UR) injury to supplement the knowledge about this disease and its prevention and treatment using certain medicinal plants and their active compounds. For this purpose, medicinal plants and their potential antioxidant activities, effects on lipid levels and plaque formation, atherosclerosis and development of cardiovascular diseases and ischemia were reviewed. Methods: To conduct this review, relevant articles published between 1983 and 2018 were retrieved from the Google Scholar, PubMed, Scientific Information Database, Web of Science, and Scopus using search terms antioxidant, ischemia, reperfusion, heart, infarct, inflammation, cholesterol and medicinal plants. Then, the eligible articles were reviewed. Results: The active compounds of plants, including phenolic compounds, flavonoids, and antioxidant compounds, can be effective on certain pathogenic factors particularly in decreasing cholesterol and blood pressure, preventing an increase in free radicals and ultimately reducing blood clots and vascular resistance to reduce and prevent ischemic disease and its harmful effects. Conclusion: Medicinal plants discussed in this article seem to be able to prevent cardiac damage and the disease progression via affecting the factors that are involved in ischemia

Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI