Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers about a hierarchical semantic compositional model (HSCM) which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects including semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework

An Informatics Roadmap Toward a FAIR Understanding of Mitochondrial Biology and Rare Mitochondrial Disease

Mitochondrial biology is integral to our fundamental understanding of human health and many diseases. They exist in every human cell type except for red blood cells and have critical functions in metabolism, oxidative phosphorylation, oxidation-reduction, and as signaling hubs responsible for mediating protective mechanisms. Rare mitochondrial diseases (RMDs) are devastating and complex, affect multiple organ systems, and disproportionately impact young children. Despite copious existing knowledge and increased public interest, the knowledge is fragmented and difficult to access. Clinical case reports (CCRs) on RMDs contain valuable clinical insights, but they are scarce and lack the metadata necessary to facilitate their discovery among the two million CCRs on PubMed. The unstructured text data of CCRs is also ill-suited to computational approaches, limiting our ability to derive the knowledge contained within.To address these issues, I assembled all available informatics tools and resources with mitochondrial components and used them to contribute to Gene Wiki pages that enable easy access to mitochondrial knowledge for researchers, students, clinicians, and patients. Through these efforts, I made mitochondrial gene, protein, and disease knowledge widely accessible with contributions of over 4MB of content across 541 Gene Wiki pages. Concurrently, I used Gene Wiki as an educational platform to train over 50 students in the biosciences and pre-medical studies in mitochondrial biology and disease, as well as instilling effective research and writing methods in biomedicine.To impose structure on CCRs and render them FAIR (Findable, Accessible, Interoperable, Reusable), I developed and applied a standardized metadata template to RMD CCRs and codified patient symptomology with the International Statistical Classification of Disease and Related Health Problems (ICD) system. I created the open-source, cloud-based MitoCases RMD Knowledge Platform (http://mitocases.org/) to house data on 384 RMD CCRs, including 4,561 instances of 952 unique ICD codes. Supplementing CCRs with structured metadata amplifies machine-readable information content and provides a distinct improvement in searching for CCRs as compared to indexing by title and abstract. Finally, I employed these resources to conduct a thorough review of Barth syndrome and characterized the diversity of presentations, range of genetic etiologies, and treatment paradigms

An Informatics Roadmap Toward a FAIR Understanding of Mitochondrial Biology and Rare Mitochondrial Disease

Mitochondrial biology is integral to our fundamental understanding of human health and many diseases. They exist in every human cell type except for red blood cells and have critical functions in metabolism, oxidative phosphorylation, oxidation-reduction, and as signaling hubs responsible for mediating protective mechanisms. Rare mitochondrial diseases (RMDs) are devastating and complex, affect multiple organ systems, and disproportionately impact young children. Despite copious existing knowledge and increased public interest, the knowledge is fragmented and difficult to access. Clinical case reports (CCRs) on RMDs contain valuable clinical insights, but they are scarce and lack the metadata necessary to facilitate their discovery among the two million CCRs on PubMed. The unstructured text data of CCRs is also ill-suited to computational approaches, limiting our ability to derive the knowledge contained within.To address these issues, I assembled all available informatics tools and resources with mitochondrial components and used them to contribute to Gene Wiki pages that enable easy access to mitochondrial knowledge for researchers, students, clinicians, and patients. Through these efforts, I made mitochondrial gene, protein, and disease knowledge widely accessible with contributions of over 4MB of content across 541 Gene Wiki pages. Concurrently, I used Gene Wiki as an educational platform to train over 50 students in the biosciences and pre-medical studies in mitochondrial biology and disease, as well as instilling effective research and writing methods in biomedicine.To impose structure on CCRs and render them FAIR (Findable, Accessible, Interoperable, Reusable), I developed and applied a standardized metadata template to RMD CCRs and codified patient symptomology with the International Statistical Classification of Disease and Related Health Problems (ICD) system. I created the open-source, cloud-based MitoCases RMD Knowledge Platform (http://mitocases.org/) to house data on 384 RMD CCRs, including 4,561 instances of 952 unique ICD codes. Supplementing CCRs with structured metadata amplifies machine-readable information content and provides a distinct improvement in searching for CCRs as compared to indexing by title and abstract. Finally, I employed these resources to conduct a thorough review of Barth syndrome and characterized the diversity of presentations, range of genetic etiologies, and treatment paradigms

Mitochondrial Reactive Oxygen Species (ROS): Which ROS is Responsible for Cardioprotective Signaling?

Mitochondria are the major effectors of cardioprotection by procedures that open the mitochondrial ATP-sensitive potassium channel (mitoKATP), including ischemic and pharmacological preconditioning. MitoKATP opening leads to increased reactive oxygen species (ROS), which then activate a mitoKATP-associated PKCε, which phosphorylates mitoKATP and leaves it in a persistent open state (Costa, ADT and Garlid, KD. Am J Physiol 295, H874-82, 2008). Superoxide (O2•-), hydrogen peroxide (H2O2), and hydroxyl radical (HO•) have each been proposed as the signaling ROS but the identity of the ROS responsible for this feedback effect is not known. Superoxide was excluded in earlier work on the basis that it does not activate PKCε and does not induce mitoKATP opening.To further examine the identity of the signaling ROS, respiring rat heart mitochondria were preincubated with ATP and diazoxide to induce the phosphorylation-dependent open state, together with agents that may interrupt feedback activation of mitoKATP by ROS scavenging or by blocking ROS transformations. Swelling assays of the preincubated mitochondria revealed that dimethylsulfoxide (DMSO), dimethylformamide (DMF), deferoxamine, trolox, and bromoenol lactone (BEL) each blocked the ROS-dependent open state but catalase did not interfere with this step. The lack of a catalase effect and the inhibitory effects of agents acting downstream of HO• excludes H2O2 as the endogenous signaling ROS and focuses attention on HO•. In support of the hypothesis that HO• is required, we also found that HO•-scavenging by DMF blocked cardioprotection by both ischemic preconditioning and diazoxide in the Langendorff perfused rat heart. HO• itself cannot act as a signaling molecule, because its lifetime is too short and it reacts immediately with nearest neighbor phospholipids and proteins. Therefore, these findings point to a product of phospholipid peroxidation, such as hydroperoxy-fatty acids. Indeed, this hypothesis was supported by the finding that hydroperoxylinoleic acid (LAOOH) opens the ATP-inhibited mitoKATP in isolated mitochondria. This effect was blocked by the specific PKCε inhibitor peptide εV1-2, showing that LAOOH activates the mitoKATP-associated PKCε. During ischemia, catabolism of mitochondrial phospholipids is accelerated, causing accumulation of plasmalogens and free fatty acids (FA) in the heart by the action of calcium independent phospholipases A2 (iPLA2). We first assessed the role of FAs and hydroxy FAs on mitoKATP opening and cardioprotection. Swelling assays of isolated rat heart mitochondria showed that naturally formed free FAs inhibit mitoKATP opening and that they are more potent inhibitors of the pharmacological open state of mitoKATP than the phosphorylation-dependent open state. That is, sustained mitoKATP opening induced by the phosphorylation-dependent feedback loop is more resistant to FA inhibition than direct mitoKATP opening by a potassium channel opener. Moreover, rat hearts perfused with micromolar concentrations of FA were resistant to cardioprotection by diazoxide or ischemic preconditioning. Racemic bromoenol lactone (BEL), a selective inhibitor of iPLA2, confers protection to otherwise untreated Langendorff perfused hearts by preventing ischemic FA release. To bring this story full circle, BEL blocks protection afforded by preconditioning and postconditioning by preventing the iPLA2-mediated release of FAOOH generated in the conditioned heart. HO• resulting from mitoKATP opening oxidizes polyunsaturated fatty acid components of the membrane phospholipids, resulting in a peroxidized side chain. FAOOH must be released in order to act on the mitochondrial PKCε, and this is achieved by the action of iPLA2. iPLA2 is essential for most modes of cardioprotection because it catalyzes the release of FAOOH. This fully supports the hypothesis that the second messenger of cardioprotective ROS-mediated signaling is hydroperoxy fatty acid (FAOOH), a downstream oxidation product of HO•

Design considerations for a hierarchical semantic compositional framework for medical natural language understanding.

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts.

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data