Tumor taxonomy for the developmental lineage classification of neoplasms

BACKGROUND: The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. METHODS: The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. RESULTS: The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. CONCLUSIONS: This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and assigns each tumor to a single class within the tumor hierarchy. The entire classification and taxonomy are available as open access files (in XML and flat-file formats) with this article

Clinical narrative analytics challenges

Precision medicine or evidence based medicine is based on the extraction of knowledge from medical records to provide individuals with the appropriate treatment in the appropriate moment according to the patient features. Despite the efforts of using clinical narratives for clinical decision support, many challenges have to be faced still today such as multilinguarity, diversity of terms and formats in different services, acronyms, negation, to name but a few. The same problems exist when one wants to analyze narratives in literature whose analysis would provide physicians and researchers with highlights. In this talk we will analyze challenges, solutions and open problems and will analyze several frameworks and tools that are able to perform NLP over free text to extract medical entities by means of Named Entity Recognition process. We will also analyze a framework we have developed to extract and validate medical terms. In particular we present two uses cases: (i) medical entities extraction of a set of infectious diseases description texts provided by MedlinePlus and (ii) scales of stroke identification in clinical narratives written in Spanish

Discovering context-specific relationships from biological literature by using multi-level context terms

<p>Abstract</p> <p>Background</p> <p>The Swanson's ABC model is powerful to infer hidden relationships buried in biological literature. However, the model is inadequate to infer relations with context information. In addition, the model generates a very large amount of candidates from biological text, and it is a semi-automatic, labor-intensive technique requiring human expert's manual input. To tackle these problems, we incorporate context terms to infer relations between AB interactions and BC interactions.</p> <p>Methods</p> <p>We propose 3 steps to discover meaningful hidden relationships between drugs and diseases: 1) multi-level (gene, drug, disease, symptom) entity recognition, 2) interaction extraction (drug-gene, gene-disease) from literature, 3) context vector based similarity score calculation. Subsequently, we evaluate our hypothesis with the datasets of the "Alzheimer's disease" related 77,711 PubMed abstracts. As golden standards, PharmGKB and CTD databases are used. Evaluation is conducted in 2 ways: first, comparing precision of the proposed method and the previous method and second, analysing top 10 ranked results to examine whether highly ranked interactions are truly meaningful or not.</p> <p>Results</p> <p>The results indicate that context-based relation inference achieved better precision than the previous ABC model approach. The literature analysis also shows that interactions inferred by the context-based approach are more meaningful than interactions by the previous ABC model.</p> <p>Conclusions</p> <p>We propose a novel interaction inference technique that incorporates context term vectors into the ABC model to discover meaningful hidden relationships. By utilizing multi-level context terms, our model shows better performance than the previous ABC model.</p

Normalizing biomedical terms by minimizing ambiguity and variability

<p>Abstract</p> <p>Background</p> <p>One of the difficulties in mapping biomedical named entities, e.g. genes, proteins, chemicals and diseases, to their concept identifiers stems from the potential variability of the terms. Soft string matching is a possible solution to the problem, but its inherent heavy computational cost discourages its use when the dictionaries are large or when real time processing is required. A less computationally demanding approach is to normalize the terms by using heuristic rules, which enables us to look up a dictionary in a constant time regardless of its size. The development of good heuristic rules, however, requires extensive knowledge of the terminology in question and thus is the bottleneck of the normalization approach.</p> <p>Results</p> <p>We present a novel framework for discovering a list of normalization rules from a dictionary in a fully automated manner. The rules are discovered in such a way that they minimize the ambiguity and variability of the terms in the dictionary. We evaluated our algorithm using two large dictionaries: a human gene/protein name dictionary built from BioThesaurus and a disease name dictionary built from UMLS.</p> <p>Conclusions</p> <p>The experimental results showed that automatically discovered rules can perform comparably to carefully crafted heuristic rules in term mapping tasks, and the computational overhead of rule application is small enough that a very fast implementation is possible. This work will help improve the performance of term-concept mapping tasks in biomedical information extraction especially when good normalization heuristics for the target terminology are not fully known.</p

Selective depletion of mouse kidney proximal straight tubule cells causes acute kidney injury

The proximal straight tubule (S3 segment) of the kidney is highly susceptible to ischemia and toxic insults but has a remarkable capacity to repair its structure and function. In response to such injuries, complex processes take place to regenerate the epithelial cells of the S3 segment; however, the precise molecular mechanisms of this regeneration are still being investigated. By applying the “toxin receptor mediated cell knockout” method under the control of the S3 segment-specific promoter/enhancer, Gsl5, which drives core 2 β-1,6-N-acetylglucosaminyltransferase gene expression, we established a transgenic mouse line expressing the human diphtheria toxin (DT) receptor only in the S3 segment. The administration of DT to these transgenic mice caused the selective ablation of S3 segment cells in a dose-dependent manner, and transgenic mice exhibited polyuria containing serum albumin and subsequently developed oliguria. An increase in the concentration of blood urea nitrogen was also observed, and the peak BUN levels occurred 3–7 days after DT administration. Histological analysis revealed that the most severe injury occurred in the S3 segments of the proximal tubule, in which tubular cells were exfoliated into the tubular lumen. In addition, aquaporin 7, which is localized exclusively to the S3 segment, was diminished. These results indicate that this transgenic mouse can suffer acute kidney injury (AKI) caused by S3 segment-specific damage after DT administration. This transgenic line offers an excellent model to uncover the mechanisms of AKI and its rapid recovery

Addressing the migration of health professionals: the role of working conditions and educational placements

This article provides a brief overview of the global health-worker shortage, which could undermine the Millennium Development Goal to halt and begin to reverse the spread of HIV/AIDS. The current situation suggests that long-term solutions to shortages can only be found by addressing the problem from a global perspective; that is, to eliminate shortages through substantial investments in training and retaining health workers in developed and developing countries, and not through policies that do not work towards solving this underlying problem, such as ones that restrict migration