What is the scope of teaching and training of undergraduate students and trainees in point of care testing in United Kingdom universities and hospital laboratories?

Point of care testing (POCT) is an analytical test performed by a healthcare professional outside of a conventional laboratory. The global POCT market was valued at US$ 23.16 billion in 2016 and is forecasted to grow to US$ 36.96 billion in 2021. This upward trend for POCT has increased workload for pathology departments who manage POCT. This research aims to characterize and analyse the teaching and training of POCT at United Kingdom (UK) universities on Institute of Biomedical Science (IBMS) accredited biomedical science degrees, and at UK hospital laboratories. A freedom of information (FOI) request was sent in 2018 to all 52 UK universities with an accredited IBMS Biomedical science degree to request information on teaching of POCT, with a 100% response rate. Further FOI requests were sent to all National Health Service (NHS) hospital pathology departments in the UK, regarding POCT training provided to trainee Biomedical scientists, with a 97% response rate. Twelve of the degrees contained no POCT teaching, with a further 9 having no specific POCT teaching. Sixty-six laboratories confirmed that there was no POCT training. The university teaching hours varied between 0 and 35 hours. The median time spent teaching POCT at university was 2 hours. The laboratory teaching hours varied between 0 and 450 hours The median time spent teaching POCT in hospital laboratories was 3 hours. A content analysis of the learning outcomes provided by 29 universities showed that only 61% (84/137) were measurable and 26% (36/137) of the learning outcomes used action verbs that have previously been listed to be avoided in learning outcome writing. Only 9% (13/137) of outcomes specifically described POCT, with 8 of these being measurable. The findings demonstrate that although this is a commonly required skill for biomedical scientists, there is a clear lack of POCT teaching and training in the UK. To meet the new Quality Assurance Agency for Higher Education (QAA) guidelines, but most importantly to ensure the workforce is fit for the needs of the current healthcare system, the quality and quantity of POCT teaching and training needs to improve.

Vaccination Data When the Outbreak Happens: A Qualitative Evaluation of Oregon's Rapid Response Tool.

OBJECTIVE: Immunization data are vital to support responses to vaccine-preventable disease outbreaks. The Oregon Immunization Program developed a unique prototype instrument-the Rapid Response Tool (RRT)-that provides population data to local responders within 2 hours of a request. Data outputs include vaccination coverage by age group and zip code; percentages of students with nonmedical exemptions to vaccination requirements, by school; and current, comprehensive lists of local vaccination providers. METHODS: The RRT was demonstrated to staff at 7 Oregon counties and feedback was solicited via comments and a structured survey. RESULTS: The RRT received strong support. Attendees identified several uses for RRT data, including outbreak response and ongoing intervention efforts, and they pointed to areas for further development. CONCLUSIONS: The success of the RRT demonstrations illustrates that a well-populated immunization information system can contribute to preparedness work well beyond current standards. (Disaster Med Public Health Preparedness. 2019;13:682-685).

Terminology Status APIs - Mapping Obsolete Codes to Current RxNorm, SNOMED CT, and LOINC Concepts.

We created the Terminology Status Application Programming Interface (API) to assist users in mapping obsolete codes to current RxNorm, SNOMED CT and LOINC concepts. Use cases include support for information retrieval, maintenance of value sets, and analytics of legacy clinical databases. Our terminology status APIs typically receive over 4 million calls per month on average.

Structure of ubiquitylated-Rpn10 provides insight into its autoregulation mechanism.

Ubiquitin receptors decode ubiquitin signals into many cellular responses. Ubiquitin receptors also undergo coupled monoubiquitylation, and rapid deubiquitylation has hampered the characterization of the ubiquitylated state. Using bacteria that express a ubiquitylation apparatus, we purified and determined the crystal structure of the proteasomal ubiquitin-receptor Rpn10 in its ubiquitylated state. The structure shows a novel ubiquitin-binding patch that directs K84 ubiquitylation. Superimposition of ubiquitylated-Rpn10 onto electron-microscopy models of proteasomes indicates that the Rpn10-conjugated ubiquitin clashes with Rpn9, suggesting that ubiquitylation might be involved in releasing Rpn10 from the proteasome. Indeed, ubiquitylation on immobilized proteasomes dissociates the modified Rpn10 from the complex, while unmodified Rpn10 mainly remains associated. In vivo experiments indicate that contrary to wild type, Rpn10-K84R is stably associated with the proteasomal subunit Rpn9. Similarly Rpn10, but not ubiquitylated-Rpn10, binds Rpn9 in vitro. Thus we suggest that ubiquitylation functions to dissociate modified ubiquitin receptors from their targets, a function that promotes cyclic activity of ubiquitin receptors.

Proteasome storage granules are transiently associated with the insoluble protein deposit in Saccharomyces cerevisiae.

Proteasome storage granules (PSGs) are created in yeast as part of an extensive and programmed reorganization of proteins into reversible assemblies upon carbon source depletion. Here, we demonstrate that cells distinguish dysfunctional proteasomes from PSGs on the cytosolic insoluble protein deposit (IPOD). Furthermore, we provide evidence that this is a general mechanism for the reorganization of additional proteins into reversible assemblies. Our study expands the roles of the IPOD, which might serve not only as the specific depository for amyloidogenic and misfolded proteins, but also as a potential hub from which proteins are directed to distinct cellular compartments. These findings therefore provide a framework for understanding how cells discriminate between intact and abnormal proteins under stress conditions to ensure that only structurally 'correct' proteins are deployed.

Evaluating drug-drug interaction information in NDF-RT and DrugBank.

BACKGROUND: There is limited consensus among drug information sources on what constitutes drug-drug interactions (DDIs). We investigate DDI information in two publicly available sources, NDF-RT and DrugBank. METHODS: We acquire drug-drug interactions from NDF-RT and DrugBank, and normalize the drugs to RxNorm. We compare interactions between NDF-RT and DrugBank and evaluate both sources against a reference list of 360 critical interactions. We compare the interactions detected with NDF-RT and DrugBank on a large prescription dataset. Finally, we contrast NDF-RT and DrugBank against a commercial source. RESULTS: DrugBank drug-drug interaction information has limited overlap with NDF-RT (24-30%). The coverage of the reference set by both sources is about 60%. Applied to a prescription dataset of 35.5M pairs of co-prescribed systemic clinical drugs, NDF-RT would have identified 808,285 interactions, while DrugBank would have identified 1,170,693. Of these, 382,833 are common. The commercial source Multum provides a more systematic coverage (91%) of the reference list. CONCLUSIONS: This investigation confirms the limited overlap of DDI information between NDF-RT and DrugBank. Additional research is required to determine which source is better, if any. Usage of any of these sources in clinical decision systems should disclose these limitations.

The protein quality control machinery regulates its misassembled proteasome subunits.

Cellular toxicity introduced by protein misfolding threatens cell fitness and viability. Failure to eliminate these polypeptides is associated with various aggregation diseases. In eukaryotes, the ubiquitin proteasome system (UPS) plays a vital role in protein quality control (PQC), by selectively targeting misfolded proteins for degradation. While the assembly of the proteasome can be naturally impaired by many factors, the regulatory pathways that mediate the sorting and elimination of misassembled proteasomal subunits are poorly understood. Here, we reveal how the dysfunctional proteasome is controlled by the PQC machinery. We found that among the multilayered quality control mechanisms, UPS mediated degradation of its own misassembled subunits is the favored pathway. We also demonstrated that the Hsp42 chaperone mediates an alternative pathway, the accumulation of these subunits in cytoprotective compartments. Thus, we show that proteasome homeostasis is controlled through probing the level of proteasome assembly, and the interplay between UPS mediated degradation or their sorting into distinct cellular compartments.

Approaches to Supporting the Analysis of Historical Medication Datasets with RxNorm.

OBJECTIVE: To investigate approaches to supporting the analysis of historical medication datasets with RxNorm. METHODS: We created two sets of National Drug Codes (NDCs). One is based on historical NDCs harvested from versions of RxNorm from 2007 to present. The other comprises all sources of NDCs in the current release of RxNorm, including proprietary sources. We evaluated these two resources against four sets of NDCs obtained from various sources. RESULTS: In two historical medication datasets, 14-19% of the NDCs were obsolete, but 91-96% of these obsolete NDCs could be recovered and mapped to active drug concepts. CONCLUSION: Adding historical data significantly increases NDC mapping to active RxNorm drugs. A service for mapping historical NDC datasets leveraging RxNorm was added to the RxNorm API and is available at https://rxnav.nlm.nih.gov/.

Enabling complex queries to drug information sources through functional composition.

Our objective was to enable an end-user to create complex queries to drug information sources through functional composition, by creating sequences of functions from application program interfaces (API) to drug terminologies. The development of a functional composition model seeks to link functions from two distinct APIs. An ontology was developed using Protégé to model the functions of the RxNorm and NDF-RT APIs by describing the semantics of their input and output. A set of rules were developed to define the interoperable conditions for functional composition. The operational definition of interoperability between function pairs is established by executing the rules on the ontology. We illustrate that the functional composition model supports common use cases, including checking interactions for RxNorm drugs and deploying allergy lists defined in reference to drug properties in NDF-RT. This model supports the RxMix application (http://mor.nlm.nih.gov/RxMix/), an application we developed for enabling complex queries to the RxNorm and NDF-RT APIs.

Formation and dissociation of proteasome storage granules are regulated by cytosolic pH.

The 26S proteasome is the major protein degradation machinery of the cell and is regulated at many levels. One mode of regulation involves accumulation of proteasomes in proteasome storage granules (PSGs) upon glucose depletion. Using a systematic robotic screening approach in yeast, we identify trans-acting proteins that regulate the accumulation of proteasomes in PSGs. Our dataset was enriched for subunits of the vacuolar adenosine triphosphatase (V-ATPase) complex, a proton pump required for vacuole acidification. We show that the impaired ability of V-ATPase mutants to properly govern intracellular pH affects the kinetics of PSG formation. We further show that formation of other protein aggregates upon carbon depletion also is triggered in mutants with impaired activity of the plasma membrane proton pump and the V-ATPase complex. We thus identify cytosolic pH as a specific cellular signal involved both in the glucose sensing that mediates PSG formation and in a more general mechanism for signaling carbon source exhaustion.

An approximate matching method for clinical drug names.

OBJECTIVE: To develop an approximate matching method for finding the closest drug names within existing RxNorm content for drug name variants found in local drug formularies. METHODS: We used a drug-centric algorithm to determine the closest strings between the RxNorm data set and local variants which failed the exact and normalized string matching searches. Aggressive measures such as token splitting, drug name expansion and spelling correction are used to try and resolve drug names. The algorithm is evaluated against three sets containing a total of 17,164 drug name variants. RESULTS: Mapping of the local variant drug names to the targeted concept descriptions ranged from 83.8% to 92.8% in three test sets. The algorithm identified the appropriate RxNorm concepts as the top candidate in 76.8%, 67.9% and 84.8% of the cases in the three test sets and among the top three candidates in 90-96% of the cases. CONCLUSION: Using a drug-centric token matching approach with aggressive measures to resolve unknown names provides effective mappings to clinical drug names and has the potential of facilitating the work of drug terminology experts in mapping local formularies to reference terminologies.

Comparing and evaluating terminology services application programming interfaces: RxNav, UMLSKS and LexBIG.

To facilitate the integration of terminologies into applications, various terminology services application programming interfaces (API) have been developed in the recent past. In this study, three publicly available terminology services API, RxNav, UMLSKS and LexBIG, are compared and functionally evaluated with respect to the retrieval of information from one biomedical terminology, RxNorm, to which all three services provide access. A list of queries is established covering a wide spectrum of terminology services functionalities such as finding RxNorm concepts by their name, or navigating different types of relationships. Test data were generated from the RxNorm dataset to evaluate the implementation of the functionalities in the three API. The results revealed issues with various aspects of the API implementation (eg, handling of obsolete terms by LexBIG) and documentation (eg, navigational paths used in RxNav) that were subsequently addressed by the development teams of the three API investigated. Knowledge about such discrepancies helps inform the choice of an API for a given use case.

Extracting Rx information from clinical narrative.

OBJECTIVE: The authors used the i2b2 Medication Extraction Challenge to evaluate their entity extraction methods, contribute to the generation of a publicly available collection of annotated clinical notes, and start developing methods for ontology-based reasoning using structured information generated from the unstructured clinical narrative. DESIGN: Extraction of salient features of medication orders from the text of de-identified hospital discharge summaries was addressed with a knowledge-based approach using simple rules and lookup lists. The entity recognition tool, MetaMap, was combined with dose, frequency, and duration modules specifically developed for the Challenge as well as a prototype module for reason identification. MEASUREMENTS: Evaluation metrics and corresponding results were provided by the Challenge organizers. RESULTS: The results indicate that robust rule-based tools achieve satisfactory results in extraction of simple elements of medication orders, but more sophisticated methods are needed for identification of reasons for the orders and durations. LIMITATIONS: Owing to the time constraints and nature of the Challenge, some obvious follow-on analysis has not been completed yet. CONCLUSIONS: The authors plan to integrate the new modules with MetaMap to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.

Methods for managing variation in clinical drug names.

OBJECTIVES: To develop normalization methods for managing the variation in clinical drug names. METHODS: Manual examination of drug names from RxNorm and local variants collected from formularies led to the identification of three types of drug-specific normalization rules: expansion of abbreviations (e.g., tab to tablet);reformatting of specific elements (e.g., space between number and unit); and removal of salt variants (e.g., succinate from metoprolol succinate). RESULTS: After drug-specific normalization, recall of 3397 previously non-matching names from formularies reaches 45% overall (70% of some subsets), compared to 10-20% after generic normalization. Ambiguity has not increased significantly in the RxNorm dataset. CONCLUSIONS: A limited number of drug-specific normalization operations provide significant improvement over general language normalization.

A graph-based approach to auditing RxNorm.

OBJECTIVES: RxNorm is a standardized nomenclature for clinical drug entities developed by the National Library of Medicine. In this paper, we audit relations in RxNorm for consistency and completeness through the systematic analysis of the graph of its concepts and relationships. METHODS: The representation of multi-ingredient drugs is normalized in order to make it compatible with that of single-ingredient drugs. All meaningful paths between two nodes in the type graph are computed and instantiated. Alternate paths are automatically compared and manually inspected in case of inconsistency. RESULTS: The 115 meaningful paths identified in the type graph can be grouped into 28 groups with respect to start and end nodes. Of the 19 groups of alternate paths (i.e., with two or more paths) between the start and end nodes, 9 (47%) exhibit inconsistencies. Overall, 28 (24%) of the 115 paths are inconsistent with other alternate paths. A total of 348 inconsistencies were identified in the April 2008 version of RxNorm and reported to the RxNorm team, of which 215 (62%) had been corrected in the January 2009 version of RxNorm. CONCLUSION: The inconsistencies identified involve missing nodes (93), missing links (17), extraneous links (237) and one case of mix-up between two ingredients. Our auditing method proved effective in identifying a limited number of errors that had defeated the quality assurance mechanisms currently in place in the RxNorm production system. Some recommendations for the development of RxNorm are provided.

Using the RxNorm web services API for quality assurance purposes.

Auditing large, rapidly evolving terminological systems is still a challenge. In the case of RxNorm, a standardized nomenclature for clinical drugs, we argue that quality assurance processes can benefit from the recently released application programming interface (API) provided by RxNav. We demonstrate the usefulness of the API by performing a systematic comparison of alternative paths in the RxNorm graph, over several thousands of drug entities. This study revealed potential errors in RxNorm, currently under review. The results also prompted us to modify the implementation of RxNav to navigate the RxNorm graph more accurately. The RxNav web services API used in this experiment is robust and fast.

Enhancing biomedical ontologies through alignment of semantic relationships: exploratory approaches.

OBJECTIVE: This paper investigates several methods for aligning Metathesaurus relationships with their counterparts in the UMLS Semantic Network. Unlike the categorization link defined between Metathesaurus concepts and Semantic Network types, no such correspondence exists between the relationships at these two levels of the UMLS. METHODS: The first approach attempts to elicit the semantics of Metathesaurus relationships through an examination of their relata at different levels: concept, high-level ancestors and semantic types. The second approach examines the frequency of association between a given Semantic Network relationship and the actual relationships observed in the Metathesaurus between the concepts categorized by these semantic types. RESULTS: A total of 139 relationships are present in the Metathesaurus. Using the methods described in this paper, 80 (58%) could be aligned with Semantic Network relationships. The remaining relationships are vocabulary internal, used, for example, for vocabulary management or to indicate strictly lexical relationships. The work reported here is a first step in the attempt to build a more comprehensive ontology of biomedical relationships.

The UMLS knowledge source server: an object model for delivering UMLS data.

The Unified Medical Language System (UMLS), a project of the National Library of Medicine (NLM), regularly distributes a set of knowledge sources to the research community. These data are made available over the Internet through the UMLS Knowledge Source Server (UMLSKS). The new version of the UMLSKS is a complete redesign of the original system using Java and the Extensible Markup Language (XML) technologies to implement a fast, reliable, flexible, and extensible UMLS data retrieval system that includes an Application Programmer's Interface (API) and an Object Model of each of the Knowledge Sources: the UMLS Metathesaurus, the Semantic Network, and the SPECIALIST Lexicon. In this paper we present the design of the new system, outline each of the system design goals, the UMLS Object Model, and statistics showing the usage of the new UMLSKS and associated data. We conclude with implications for future work.