Developing a Deeper Understanding of Autism: Connecting Knowledge through Literature Mining

Fluoride Exposure Induces Inhibition of Sodium-and Potassium-Activated Adenosine Triphosphatase (Na+, K+-ATPase) Enzyme Activity: Molecular Mechanisms and Implications for Public Health

In this study, several lines of evidence are provided to show that Na + , K + -ATPase activity exerts vital roles in normal brain development and function and that loss of enzyme activity is implicated in neurodevelopmental, neuropsychiatric and neurodegenerative disorders, as well as increased risk of cancer, metabolic, pulmonary and cardiovascular disease. Evidence is presented to show that fluoride (F) inhibits Na + , K + -ATPase activity by altering biological pathways through modifying the expression of genes and the activity of glycolytic enzymes, metalloenzymes, hormones, proteins, neuropeptides and cytokines, as well as biological interface interactions that rely on the bioavailability of chemical elements magnesium and manganese to modulate ATP and Na + , K + -ATPase enzyme activity. Taken together, the findings of this study provide unprecedented insights into the molecular mechanisms and biological pathways by which F inhibits Na + , K + -ATPase activity and contributes to the etiology and pathophysiology of diseases associated with impairment of this essential enzyme. Moreover, the findings of this study further suggest that there are windows of susceptibility over the life course where chronic F exposure in pregnancy and early infancy may impair Na + , K + -ATPase activity with both short- and long-term implications for disease and inequalities in health. These findings would warrant considerable attention and potential intervention, not to mention additional research on the potential effects of F intake in contributing to chronic disease.

Autism research dynamic through ontology-based text mining

Purpose

– The increase of prevalence of autism spectrum disorders (ASD) has been accompanied by much new research. The amount and the speed of growth of scientific information available online have strongly influenced the way of work in the research community which calls for new methods and tools to support it. The purpose of this paper is to present ontology-based text mining in the field of autism trend analysis that may help to understand the broader picture of the disorder since its discovery.

Design/methodology/approach

– The data sets consisted of abstracts of more than 18,000 articles on ASD published from 1943 to the end of 2012 found in MEDLINE and of the documents’ titles for all those articles where the abstracts were not available.

Findings

– In this way, the authors demonstrated a steeper exponential curve of ASD publications compared with all publications in MEDLINE. In addition, the main research topics over time were identified using the “open discovery” approach. Finally, the relationship between a priori setting up research topics including communication, genetics, environmental risk factors, vaccination and adulthood were revealed.

Originality/value

– Using ontology-based text mining the authors were able to identify the main research topics in the field of autism during the time, as well as to show the dynamics of some research topics as a priori setting up. The computerised methodology that was used allowed the authors to analyse a much larger quantity of information, saving time and manual work.

An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data

OBJECTIVE

Our goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects' Autism Diagnostic Interview-Revised (ADI-R) assessment data.

MATERIALS AND METHODS

Knowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data.

RESULTS

We extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94.

DISCUSSION

The ontology allows automatic inference of subjects' disease phenotypes and diagnosis with high accuracy.

CONCLUSION

The ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology.

Modeling the autism spectrum disorder phenotype

Autism Spectrum Disorder (ASD) is highly heritable, and although there has been active research in an attempt to discover the genetic factors underlying ASD, diagnosis still depends heavily on behavioral assessments. Recently, several large-scale initiatives, including those of the Autism Consortium, have contributed to the collection of extensive information from families affected by ASD. Our goal was to develop an ontology that can be used 1) to provide improved access to the data collected by those who study ASD and other neurodevelopmental disorders, and 2) to assess and compare the characteristics of the instruments that are used in the assessment of ASD. We analyzed two dozen instruments used to assess ASD, studying the nature of the questions asked and items assessed, the method of delivery, and the overall scope of the content. These data together with the extensive literature on ASD contributed to our iterative development of an ASD phenotype ontology. The final ontology comprises 283 concepts distributed across three high-level classes, 'Personal Traits', 'Social Competence', and 'Medical History'. The ontology is fully integrated with the Autism Consortium database, allowing researchers to pose ontology-based questions. The ontology also allows researchers to assess the degree of overlap among a set of candidate instruments according to several objective criteria. The ASD phenotype ontology has promise for use in research settings where extensive phenotypic data have been collected, allowing a concept-based approach to identifying behavioral features of importance and for correlating these with genotypic data.