Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.

We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu

Using Design Thinking to Spread iPACE™: An Interprofessional Medical Education Innovation in an Academic Medical Center

Problem Statement: The Interprofessional Partnership to Advance Care and Education (iPACE™) model and its core principles are spreading across the MaineHealth system. Thus, there is a need for a standardized approach that is adaptable and incorporates the requirements of diverse patient care settings. Background: In 2017, the original iPACE™ model was designed and piloted on a new teaching unit for adult internal medicine at Maine Medical Center. Analysis of the pilot data showed improved teaming, care team experiences, interprofessional collaborations, and patient satisfaction. Because the pilot model will require adaptation to be successfully implemented in other disciplines, the authors sought a framework to facilitate implementation of core iPACE™ principles in diverse clinical care settings. Application/Recommendation: The Design Thinking (DT) framework was selected as a structured, standardized approach to accelerate innovation and implementation of the iPACE™ model in a new patient care setting. The DT framework consists of 6 consecutive process steps and iteration loops: Understand, Observe, Point of View, Ideate, Prototype, and Test. This paper outlines specific metrics and activities in each step, as well as opportunities for tailoring each step based on the care setting

Efficacy of Chlorine-based, Enzymatic and Combined Chlorine-enzyme Treatments on Biofilm Removal

Glyphosate resistance evolution in weeds is a growing problem in world agriculture. Here, we have investigated the mechanism(s) of glyphosate resistance in a Lolium rigidum population (DAG1) from South Africa. Nucleotide sequencing revealed the existence of at least three EPSPS homologues in the L. rigidum genome and identified a novel proline 106 to leucine substitution (P106L) in 52% DAG1 individuals. This mutation conferred a 1.7-fold resistance increase to glyphosate at the whole plant level. Additionally, a 3.1-fold resistance increase, not linked to metabolism or translocation, was estimated between wild-type P106-DAG1 and P106-STDS sensitive plants. Point accepted mutation analysis suggested that other amino acid substitutions at EPSPS position 106 are likely to be found in nature besides the P106/S/A/T/L point mutations reported to date. This study highlights the importance of minor mechanisms acting additively to confer significant levels of resistance to commercial field rates of glyphosate in weed populations subjected to high selection pressure

Silencing of Histone Deacetylase 6 Decreases Cellular Malignancy and Contributes to Primary Cilium Restoration, Epithelial-to-Mesenchymal Transition Reversion, and Autophagy Inhibition in Glioblastoma Cell Lines

Glioblastoma multiforme, the most common type of malignant brain tumor as well as the most aggressive one, lacks an effective therapy. Glioblastoma presents overexpression of mesenchymal markers Snail, Slug, and N-Cadherin and of the autophagic marker p62. Glioblastoma cell lines also present increased autophagy, overexpression of mesenchymal markers, Shh pathway activation, and lack of primary cilia. In this study, we aimed to evaluate the role of HDAC6 in the pathogenesis of glioblastoma, as HDAC6 is the most overexpressed of all HDACs isoforms in this tumor. We treated glioblastoma cell lines with siHDAC6. HDAC6 silencing inhibited proliferation, migration, and clonogenicity of glioblastoma cell lines. They also reversed the mesenchymal phenotype, decreased autophagy, inhibited Shh pathway, and recovered the expression of primary cilia in glioblastoma cell lines. These results demonstrate that HDAC6 might be a good target for glioblastoma treatment

Pathological Features in Paediatric Patients with TK2 Deficiency

Thymidine kinase (TK2) deficiency causes mitochondrial DNA depletion syndrome. We aimed to report the clinical, biochemical, genetic, histopathological, and ultrastructural features of a cohort of paediatric patients with TK2 deficiency. Mitochondrial DNA was isolated from muscle biopsies to assess depletions and deletions. The TK2 genes were sequenced using Sanger sequencing from genomic DNA. All muscle biopsies presented ragged red fibres (RRFs), and the prevalence was greater in younger ages, along with an increase in succinate dehydrogenase (SDH) activity and cytochrome c oxidase (COX)-negative fibres. An endomysial inflammatory infiltrate was observed in younger patients and was accompanied by an overexpression of major histocompatibility complex type I (MHC I). The immunofluorescence study for complex I and IV showed a greater number of fibres than those that were visualized by COX staining. In the ultrastructural analysis, we found three major types of mitochondrial alterations, consisting of concentrically arranged lamellar cristae, electrodense granules, and intramitochondrial vacuoles. The pathological features in the muscle showed substantial differences in the youngest patients when compared with those that had a later onset of the disease. Additional ultrastructural features are described in the muscle biopsy, such as sarcomeric de-structuration in the youngest patients with a more severe phenotype

ExploreASL: An image processing pipeline for multi-center ASL perfusion MRI studies

Arterial spin labeling (ASL) has undergone significant development since its inception, with a focus on improving standardization and reproducibility of its acquisition and quantification. In a community-wide effort towards robust and reproducible clinical ASL image processing, we developed the software package ExploreASL, allowing standardized analyses across centers and scanners. The procedures used in ExploreASL capitalize on published image processing advancements and address the challenges of multi-center datasets with scanner-specific processing and artifact reduction to limit patient exclusion. ExploreASL is self-contained, written in MATLAB and based on Statistical Parameter Mapping (SPM) and runs on multiple operating systems. To facilitate collaboration and data-exchange, the toolbox follows several standards and recommendations for data structure, provenance, and best analysis practice. ExploreASL was iteratively refined and tested in the analysis of >10,000 ASL scans using different pulse-sequences in a variety of clinical populations, resulting in four processing modules: Import, Structural, ASL, and Population that perform tasks, respectively, for data curation, structural and ASL image processing and quality control, and finally preparing the results for statistical analyses on both single-subject and group level. We illustrate ExploreASL processing results from three cohorts: perinatally HIV-infected children, healthy adults, and elderly at risk for neurodegenerative disease. We show the reproducibility for each cohort when processed at different centers with different operating systems and MATLAB versions, and its effects on the quantification of gray matter cerebral blood flow. ExploreASL facilitates the standardization of image processing and quality control, allowing the pooling of cohorts which may increase statistical power and discover between-group perfusion differences. Ultimately, this workflow may advance ASL for wider adoption in clinical studies, trials, and practice

Adolescent pregnancies in the Amazon Basin of Ecuador: a rights and gender approach to adolescents' sexual and reproductive health

In the Andean region of Latin America over one million adolescent girls get pregnant every year. Adolescent pregnancy (AP) has been associated with adverse health and social outcomes, but it has also been favorably viewed as a pathway to adulthood. AP can also be conceptualized as a marker of inequity, since it disproportionately affects girls from the poorest households and those who have not been able to attend school

Prognostic gene expression signature for high-grade serous ovarian cancer.

BACKGROUND: Median overall survival (OS) for women with high-grade serous ovarian cancer (HGSOC) is ∼4 years, yet survival varies widely between patients. There are no well-established, gene expression signatures associated with prognosis. The aim of this study was to develop a robust prognostic signature for OS in patients with HGSOC. PATIENTS AND METHODS: Expression of 513 genes, selected from a meta-analysis of 1455 tumours and other candidates, was measured using NanoString technology from formalin-fixed paraffin-embedded tumour tissue collected from 3769 women with HGSOC from multiple studies. Elastic net regularization for survival analysis was applied to develop a prognostic model for 5-year OS, trained on 2702 tumours from 15 studies and evaluated on an independent set of 1067 tumours from six studies. RESULTS: Expression levels of 276 genes were associated with OS (false discovery rate < 0.05) in covariate-adjusted single-gene analyses. The top five genes were TAP1, ZFHX4, CXCL9, FBN1 and PTGER3 (P < 0.001). The best performing prognostic signature included 101 genes enriched in pathways with treatment implications. Each gain of one standard deviation in the gene expression score conferred a greater than twofold increase in risk of death [hazard ratio (HR) 2.35, 95% confidence interval (CI) 2.02-2.71; P < 0.001]. Median survival [HR (95% CI)] by gene expression score quintile was 9.5 (8.3 to -), 5.4 (4.6-7.0), 3.8 (3.3-4.6), 3.2 (2.9-3.7) and 2.3 (2.1-2.6) years. CONCLUSION: The OTTA-SPOT (Ovarian Tumor Tissue Analysis consortium - Stratified Prognosis of Ovarian Tumours) gene expression signature may improve risk stratification in clinical trials by identifying patients who are least likely to achieve 5-year survival. The identified novel genes associated with the outcome may also yield opportunities for the development of targeted therapeutic approaches