Classifying seizures and epilepsies: limits of science and semantics.
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ABSTRACT:
SUBMITTER: Devinsky O
PROVIDER: S-EPMC3193097 | biostudies-other | 2011 Sep
REPOSITORIES: biostudies-other
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Project description:BackgroundThe development of e-Science presents a major set of opportunities and challenges for the future progress of biological and life scientific research. Major new tools are required and corresponding demands are placed on the high-throughput data generated and used in these processes. Nowhere is the demand greater than in the semantic integration of these data. Semantic Web tools and technologies afford the chance to achieve this semantic integration. Since pathway knowledge is central to much of the scientific research today it is a good test-bed for semantic integration. Within the context of biological pathways, the BioPAX initiative, part of a broader movement towards the standardization and integration of life science databases, forms a necessary prerequisite for its successful application of e-Science in health care and life science research. This paper examines whether BioPAX, an effort to overcome the barrier of disparate and heterogeneous pathway data sources, addresses the needs of e-Science.ResultsWe demonstrate how BioPAX pathway data can be used to ask and answer some useful biological questions. We find that BioPAX comes close to meeting a broad range of e-Science needs, but certain semantic weaknesses mean that these goals are missed. We make a series of recommendations for re-modeling some aspects of BioPAX to better meet these needs.ConclusionOnce these semantic weaknesses are addressed, it will be possible to integrate pathway information in a manner that would be useful in e-Science.
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Project description:BackgroundAutomated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN).ObjectiveOur objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes.MethodsWe used 2 classification methods: (1) extracting from discharge notes some features (terms, n-gram phrases, and SNOMED CT categories) that we used to train a set of supervised machine learning models (support vector machine, random forests, and gradient boosting machine), and (2) building a feature matrix, by a pretrained word embedding model, that we used to train a CNN. We used these methods to identify the chapter-level ICD-10-CM diagnosis codes in a set of discharge notes. We conducted the evaluation using 103,390 discharge notes covering patients hospitalized from June 1, 2015 to January 31, 2017 in the Tri-Service General Hospital in Taipei, Taiwan. We used the receiver operating characteristic curve as an evaluation measure, and calculated the area under the curve (AUC) and F-measure as the global measure of effectiveness.ResultsIn 5-fold cross-validation tests, our method had a higher testing accuracy (mean AUC 0.9696; mean F-measure 0.9086) than traditional NLP-based approaches (mean AUC range 0.8183-0.9571; mean F-measure range 0.5050-0.8739). A real-world simulation that split the training sample and the testing sample by date verified this result (mean AUC 0.9645; mean F-measure 0.9003 using the proposed method). Further analysis showed that the convolutional layers of the CNN effectively identified a large number of keywords and automatically extracted enough concepts to predict the diagnosis codes.ConclusionsWord embedding combined with a CNN showed outstanding performance compared with traditional methods, needing very little data preprocessing. This shows that future studies will not be limited by incomplete dictionaries. A large amount of unstructured information from free-text medical writing will be extracted by automated approaches in the future, and we believe that the health care field is about to enter the age of big data.
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Project description:Progressive myoclonus epilepsies (PMEs) are a group of genetic neurological disorders characterised by the occurrence of epileptic seizures, myoclonus and progressive neurological deterioration including cerebellar involvement and dementia. The primary cause of PMEs is variable and alterations in the corresponding mutated genes determine the progression and severity of the disease. In most cases, they lead to the death of the patient after a period of prolonged disability. PMEs also share poor information on the pathophysiological bases and the lack of a specific treatment. Recent reports suggest that neuroinflammation is a common trait under all these conditions. Here, we review similarities and differences in neuroinflammatory response in several PMEs and discuss the window of opportunity of using anti-inflammatory drugs in the treatment of several of these conditions.
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Project description:BackgroundDiphtheria, Tetanus, and whole-cell Pertussis (DTwP) vaccination-associated seizures form the commonest type of serious adverse event following immunization in India and are an important reason for vaccine hesitancy. Our study explored the genetic explanation of DTwP vaccination-associated seizures or subsequent epilepsies.MethodsBetween March 2017 and March 2019, we screened 67 children with DTwP vaccination-associated seizures or subsequent epilepsies, and of those, we studied 54 without prior seizures or neurodevelopmental deficits. Our study design was cross-sectional with a 1-year follow-up having both retrospective and prospective cases. We performed clinical exome sequencing focused on 157 epilepsy-associated genes and multiplex ligation-dependent probe amplification of the SCN1A gene at enrolment. We applied the Vineland Social Maturity Scale for neurodevelopmental assessment at follow-up.FindingsOf 54 children enrolled and underwent genetic testing (median age 37.5 months, interquartile range 7.7-67.2; diagnosis at enrolment: epilepsy 29, febrile seizure 21, and febrile seizure-plus 4), we found 33 pathogenic variants of 12 genes. Of 33 variants, 13 (39%) were novel. Most pathogenic variants were found in SCN1A gene (n = 21/33; 64%), SCN8A in 2 children, and 10 children had one variant in CDKL5, DEPDC5, GNAO1, KCNA2, KCNT1, KCNQ2, NPRL3, PCDH19, RHOBTB2, and SLC2A1. Five or more seizures (odds ratio [OR] = 5.3, confidence interval [CI]: 1.6-18.4, p = 0.006), drug-resistant epilepsy (OR = 9.8, 95% CI: 2.6-30.7, p = 0.001) and neurodevelopmental impairment (social quotient < 70) (OR = 5.6, 95% CI: 1.65-17.6, p = 0.006) were significant predictors of genetic diagnosis.InterpretationOur study provides proof-of-concept for genetic aetiology in children with DTwP vaccination-associated seizures or subsequent epilepsies and has important implications for vaccination policies in developing countries.FundingInternational Pediatric Association Foundation, Inc. (IPAF): İhsan Doğramaci research award 2016/2017; Indian Council of Medical Research (ICMR), New Delhi, India: No.3/1/3/JRF-2016/HRD/LS/71/10940.
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Project description: Not available
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Project description:Nucleotide alterations in the gene encoding proline-rich transmembrane protein 2 (PRRT2) have been identified in most patients with benign partial epilepsies in infancy (BPEI)/benign familial infantile epilepsy (BFIE). However, not all patients harbor these PRRT2 mutations, indicating the involvement of genes other than PRRT2. In this study, we performed whole exome sequencing analysis for a large family affected with PRRT2-unrelated BPEI. We identified a non-synonymous single nucleotide variation (SNV) in the voltage-sensitive chloride channel 6 gene (CLCN6). A cohort study of 48 BPEI patients without PRRT2 mutations revealed a different CLCN6 SNV in a patient, his sibling and his father who had a history of febrile seizures (FS) but not BPEI. Another study of 48 patients with FS identified an additional SNV in CLCN6. Chloride channels (CLCs) are involved in a multitude of physiologic processes and some members of the CLC family have been linked to inherited diseases. However, a phenotypic correlation has not been confirmed for CLCN6. Although we could not detect significant biological effects linked to the identified CLCN6 SNVs, further studies should investigate potential CLCN6 variants that may underlie the genetic susceptibility to convulsive disorders.
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Project description:People feel they understand complex phenomena with far greater precision, coherence, and depth than they really do; they are subject to an illusion-an illusion of explanatory depth. The illusion is far stronger for explanatory knowledge than many other kinds of knowledge, such as that for facts, procedures or narratives. The illusion for explanatory knowledge is most robust where the environment supports real-time explanations with visible mechanisms. We demonstrate the illusion of depth with explanatory knowledge in Studies 1-6. Then we show differences in overconfidence about knowledge across different knowledge domains in Studies 7-10. Finally, we explore the mechanisms behind the initial confidence and behind overconfidence in Studies 11 and 12. Implications for the roles of intuitive theories in models of concepts and cognition are discussed.
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Project description:Classifying scientific publications according to Field-of-Science taxonomies is of crucial importance, powering a wealth of relevant applications including Search Engines, Tools for Scientific Literature, Recommendation Systems, and Science Monitoring. Furthermore, it allows funders, publishers, scholars, companies, and other stakeholders to organize scientific literature more effectively, calculate impact indicators along Science Impact pathways and identify emerging topics that can also facilitate Science, Technology, and Innovation policy-making. As a result, existing classification schemes for scientific publications underpin a large area of research evaluation with several classification schemes currently in use. However, many existing schemes are domain-specific, comprised of few levels of granularity, and require continuous manual work, making it hard to follow the rapidly evolving landscape of science as new research topics emerge. Based on our previous work of scinobo, which incorporates metadata and graph-based publication bibliometric information to assign Field-of-Science fields to scientific publications, we propose a novel hybrid approach by further employing Neural Topic Modeling and Community Detection techniques to dynamically construct a Field-of-Science taxonomy used as the backbone in automatic publication-level Field-of-Science classifiers. Our proposed Field-of-Science taxonomy is based on the OECD fields of research and development (FORD) classification, developed in the framework of the Frascati Manual containing knowledge domains in broad (first level(L1), one-digit) and narrower (second level(L2), two-digit) levels. We create a 3-level hierarchical taxonomy by manually linking Field-of-Science fields of the sciencemetrix Journal classification to the OECD/FORD level-2 fields. To facilitate a more fine-grained analysis, we extend the aforementioned Field-of-Science taxonomy to level-4 and level-5 fields by employing a pipeline of AI techniques. We evaluate the coherence and the coverage of the Field-of-Science fields for the two additional levels based on synthesis scientific publications in two case studies, in the knowledge domains of Energy and Artificial Intelligence. Our results showcase that the proposed automatically generated Field-of-Science taxonomy captures the dynamics of the two research areas encompassing the underlying structure and the emerging scientific developments.
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Project description:Contemporary research in compositional, truth-conditional semantics often takes judgments of the relative unacceptability of certain phrasal combinations as evidence for lexical semantics. For example, observing that completely full sounds perfectly natural whereas completely tall does not has been used to motivate a distinction whereby the lexical entry for full but not for tall specifies a scalar endpoint. So far, such inferences seem unobjectionable. In general, however, applying this methodology can lead to dubious conclusions. For example, observing that slightly bent is natural but slightly cheap is not (that is, not without a "too cheap" interpretation) leads researchers to suggest that the interpretation of bent involves a scalar minimum but cheap does not, contra intuition-after all, one would think that what is minimally cheap is (just) free. Such claims, found in sufficient abundance, raise the question of how we can support semantic theories that posit properties of entities that those entities appear to lack. This paper argues, using theories of adjectival scale structure as a test case, that the (un)acceptability data recruited in semantic explanations reveals properties of a two-stage system of semantic interpretation that can support divergences between our semantic and metaphysical intuitions.
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Project description:In patients with immune-associated disorders of the gray central nervous system matter (including recurrent seizures), antibodies against intracellular antigens have been discovered since the 1980s/1990s. In recent years, new antibodies against surface antigens have also been discovered. In two respects, these antibodies are even more interesting than the ones to intracellular antigens as, first, they promise a better response to immunotherapy; and, second, these antibodies contribute greatly to the understanding of the disease mechanisms. Whereas in encephalitides with antibodies against intracellular antigens, a cytotoxic T-cell-mediated response seems to be responsible for neuronal cell loss, in encephalitides with autoantibodies against surface antigens these antibodies are probably the relevant pathogenic agents in the associated disease conditions. On the one hand, antibodies to the NR1 subunit of N-methyl-D-aspartate receptors have been suggested to cause internalization and loss of these receptors without any cell destruction. This mechanism can explain the reversible functional effects caused by these antibodies. On the other hand, antibody- and complement-mediated destructive, and the irreversible effects of antibodies against the voltage-gated potassium channel antigens have been noted. These emerging findings make it plausible that immunological therapies, preferably early after characterization of the antibodies, offer opportunities to restore the health of affected patients.
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