The Impact of Teaching Medical Devices Course Using Active Learning Strategies in The Academic Achievement of The Fourth Year Biomedical Engineering Students at The University of Science and Technology, Republic of Yemen

Abstract:The aim of the current research is to explore the impact using active learning strategies in the teaching of medical devices course in the achievement of fourth year biomedical engineering students compared to the conventional teaching method.To achieve this aim, authors have stated the following statistical hypothesis: There is a statistically significant difference (at the 0.05 level) between the mean scores of students of the experimental group (taught using active learning) and control group students (taught in the usual way) in the post application of the test grades at each of the following levels: remembering, understanding, application, analysis. Experimental design known as pre and post measuring design is used in this research. Research sample consisted of two groups of the fourth level engineering biomedical students.    First group (control group) and second group (experimental group) consist of 20 students. To measure the performance of pre and post in the experimental and control groups in academic achievement, researchers developed a test to measure achievement in objective (a pump fluid, and a defibrillator) at levels: remembering, understanding, application, analysis was confirmed the veracity of the tool are viewing on a group of arbitrators, as it was ascertained where the validity test reliability coefficient (0.72). After we get results from pre and post-test, data were manipulated and processed using t test to validate research hypothesis. Pre-research tool is applied on the control and experimental groups. Then two selected subjects (Infusion pump and defibrillator) were taught for the two groups using two different methods. Experimental group was taught using active learning strategies whereas control group was taught using conventional method. After teaching period (4 classes) for every group, we applied research tool on the two groups.  Results show statistically significance difference (at 0.01) between control and experimental average scores at post-test in the following levels: Remembering, understanding, implementation and analyzing. Authors recommended encouraging teachers using active teaching strategies in teaching students to increase student’s scientific achievements.

Salivary Oral Microbiome of Children With Juvenile Idiopathic Arthritis: A Norwegian Cross-Sectional Study

Background: The oral microbiota has been connected to the pathogenesis of rheumatoid arthritis through activation of mucosal immunity. The objective of this study was to characterize the salivary oral microbiome associated with juvenile idiopathic arthritis (JIA), and correlate it with the disease activity including gingival inflammation. Methods: Fifty-nine patients with JIA (mean age, 12.6 ± 2.7 years) and 34 healthy controls (HC; mean age 12.3 ± 3.0 years) were consecutively recruited in this Norwegian cross-sectional study. Information about demographics, disease activity, medication history, frequency of tooth brushing and a modified version of the gingival bleeding index (GBI) and the simplified oral hygiene index (OHI-S) was obtained. Microbiome profiling of saliva samples was performed by sequencing of the V1-V3 region of the 16S rRNA gene, coupled with a species-level taxonomy assignment algorithm; QIIME, LEfSe and R-package for Spearman correlation matrix were used for downstream analysis. Results: There were no significant differences between JIA and HC in alpha- and beta-diversity. However, differential abundance analysis revealed several taxa to be associated with JIA: TM7-G1, Solobacterium and Mogibacterium at the genus level; and Leptotrichia oral taxon 417, TM7-G1 oral taxon 352 and Capnocytophaga oral taxon 864 among others, at the species level. Haemophilus species, Leptotrichia oral taxon 223, and Bacillus subtilis, were associated with healthy controls. Gemella morbillorum, Leptotrichia sp. oral taxon 498 and Alloprevotella oral taxon 914 correlated positively with the composite juvenile arthritis 10-joint disease activity score (JADAS10), while Campylobacter oral taxon 44 among others, correlated with the number of active joints. Of all microbial markers identified, only Bacillus subtilis and Campylobacter oral taxon 44 maintained false discovery rate (FDR) Conclusions: In this exploratory study of salivary oral microbiome we found similar alpha- and beta-diversity among children with JIA and healthy. Several taxa associated with chronic inflammation were found to be associated with JIA and disease activity, which warrants further investigation

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

Perspectives in systems nephrology

Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER−) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER– patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted <i>P</i>-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification

Supplementary File 10 from Integrative metatranscriptomic analysis reveals disease-specific microbiome-host interactions in oral squamous cell carcinoma

Host gene-microbial genus global_correlations, rs>=0.6 and FDR=<0.05</p

Supplementary File 5 from Integrative metatranscriptomic analysis reveals disease-specific microbiome-host interactions in oral squamous cell carcinoma

Host differentially expressed genes and pathways by lymph node status</p

Supplementary File 6 from Integrative metatranscriptomic analysis reveals disease-specific microbiome-host interactions in oral squamous cell carcinoma

Microbial relative transcriptional abundances in the samples.</p

Supplementary File 12 from Integrative metatranscriptomic analysis reveals disease-specific microbiome-host interactions in oral squamous cell carcinoma

Host pathways (GSEA genesets) correlating with microbial enzyme classes</p