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Initial isolation and characterization screens have been carried out on the Jamaican ball moss (Tillandsia recurvata L.) plant producing a novel complex gly-cosidic compound. This complex glycoside may be responsible for the promising anti-cancer and anti-inflammatory properties of this ball moss plant. A combination of supercritical fluid processing and chromatographic purification was used to produce the isolate. LC-MS analysis showed the presence of a compound with a molecular weight of ~800 and mass spectral (MS-MS and MS-MS-MS) data indicate that this compound may be a glycoside with a molecular mass of 1601.1 Da (negative ionization mode) and possibly contain up to two sialic acids, based on the doubly-charged state of the molecule at neutral pH conditions. 2D-TOCSY NMR spectrum lends evidence to the presence of at least one likely amide to aliphatic proton correlation, indicating the presence of a sialic acid moiety. The main structure of this therapeutic compound appears similar to a soyasaponin.

Use of terminology standards facilitates aggregating data from multiple sources for information retrieval, exchange and analysis. However, medical vocabularies are continuously updated and incorporating those changes consistently into clinical data warehouses requires rigorous methodology. To integrate pharmacy data from two hospital pharmacy information systems the Stanford Translational Research Integrated Database Environment (STRIDE) project mapped medication orders to RxNorm content using the RxNorm drug model. In order to keep the data relevant and up-to-date, we developed a strategy for updating to RxNorm, while preserving the original meaning and mapping of the legacy data. This case study discusses managing the vocabulary update by following the RxNorm content maintenance strategy and supplementing it with operations to retain access to its drug model information.

Event Stream Processing is a computational approach to the problem of how to infer the occurrence of an event from a data stream in real time without reference to a database. This paper describes how we implemented this technology on the STRIDE platform to address the challenge of real time notification of patients presenting in the Emergency Department (ED) who potentially meet eligibility criteria for a clinical study. The system was evaluated against a standalone legacy alerting system and found to perform adequately. While our initial use of this technology was focused on relatively simple alerts, the system is extensible and has the potential to provide enterprise-level research alerting services supporting more complex scenarios.

To address the problem of extracting structured information from pathology reports for research purposes in the STRIDE Clinical Data Warehouse, we adapted the ChartIndex Medical Language Processing system to automatically identify and map anatomic and diagnostic noun phrases found in full-text pathology reports to SNOMED CT concept descriptors. An evaluation of the system's performance showed a positive predictive value for anatomic concepts of 92.3% and positive predictive value for diagnostic concepts of 84.4%. The experiment also suggested strategies for improving ChartIndex's performance coding pathology reports.

The severity of diseases has often been assigned by direct observation of a patient and by pathological examination after symptoms have appeared. As we move into the genomic era, the ability to predict disease severity prior to manifestation has improved dramatically due to genomic sequencing and analysis of gene expression microarrays. However, as the severity of diseases can be exacerbated by non genetic factors, the ability to predict disease severity by examining gene expression alone may be inadequate. We propose the creation of a "clinarray" to examine phenotypic expression in the form of clinical laboratory measurements. We demonstrate that the clinarray can be used to distinguish between the severities of patients with cystic fibrosis and those with Crohn's disease by applying unsupervised clustering methods that have been previously applied to microarrays.

We describe the development of a classification scheme of negations based on syntactical categories of negative words, and patterns of negation. Each category contains a negative word or phrase and a pattern to locate negated terms. The classification was derived through literature study and manual inspection of 500 radiology reports of six common imaging modalities.