The Text-mining based PubChem Bioassay neighboring analysis.

BACKGROUND: In recent years, the number of High Throughput Screening (HTS) assays deposited in PubChem has grown quickly. As a result, the volume of both the structured information (i.e. molecular structure, bioactivities) and the unstructured information (such as descriptions of bioassay experiments), has been increasing exponentially. As a result, it has become even more demanding and challenging to efficiently assemble the bioactivity data by mining the huge amount of information to identify and interpret the relationships among the diversified bioassay experiments. In this work, we propose a text-mining based approach for bioassay neighboring analysis from the unstructured text descriptions contained in the PubChem BioAssay database. RESULTS: The neighboring analysis is achieved by evaluating the cosine scores of each bioassay pair and fraction of overlaps among the human-curated neighbors. Our results from the cosine score distribution analysis and assay neighbor clustering analysis on all PubChem bioassays suggest that strong correlations among the bioassays can be identified from their conceptual relevance. A comparison with other existing assay neighboring methods suggests that the text-mining based bioassay neighboring approach provides meaningful linkages among the PubChem bioassays, and complements the existing methods by identifying additional relationships among the bioassay entries. CONCLUSIONS: The text-mining based bioassay neighboring analysis is efficient for correlating bioassays and studying different aspects of a biological process, which are otherwise difficult to achieve by existing neighboring procedures due to the lack of specific annotations and structured information. It is suggested that the text-mining based bioassay neighboring analysis can be used as a standalone or as a complementary tool for the PubChem bioassay neighboring process to enable efficient integration of assay results and generate hypotheses for the discovery of bioactivities of the tested reagents.

Vacuum-assisted closure as a treatment modality for infections after cardiac surgery.

OBJECTIVE: Wound infections after cardiac surgery carry high morbidity and mortality. A plethora of management strategies have been used to treat such infections. We assessed the impact of vacuum-assisted closure on the management of sternal wound infections in terms of wound healing, duration of vacuum-assisted closure, and cost of treatment. METHODS: Between November 1998 and June 2001, a total of 27 mediastinal infections were managed with vacuum-assisted closure. Group A (n = 14) had vacuum-assisted closure as the final treatment modality, whereas in group B (n = 13) vacuum-assisted closure was followed by either a myocutaneous flap (n = 8) or primary (n = 5) wound closure. The choice of additional treatment modality was based on wound size. RESULTS: In group A, 4 patients died and a satisfactorily healed scar was achieved in 64% of cases. Median durations of vacuum-assisted closure and hospital stay in group A were 13.5 days (interquartile range 8.8-32.2 days) and 20 days (interquartile range 16.7-25.2 days), respectively. Mortality was 7.7% in group B, with a treatment failure rate of 15%. Median duration of vacuum-assisted closure in group B was 8 days (interquartile range 5.5-18 days), and median hospital stay was 29 days (interquartile range 25.8-38.2 days). During the year before institution of vacuum-assisted closure, poststernotomy infection (n = 13) was managed with rewiring and closed irrigation system. Treatment during this year failed in 30.7% of cases (n = 4/13), and mortality was also 30.7%. The total cost (hospitalization and treatment) per patient for vacuum-assisted closure was 16,400 dollars, compared with 20,000 dollars for the closed irrigation system treatment. CONCLUSION: Vacuum-assisted closure, used alone or before other surgical treatment strategies, is an acceptable treatment modality for infections in cardiac surgery with reasonable morbidity, mortality, and cost.

Minimum information about a bioactive entity (MIABE).

Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data - both on licensed and commercially available compounds, and also on those that fail during development - is crucial for understanding how improved molecules could be developed. For example, computational analysis of aggregated data on molecules that are investigated in drug discovery programmes has led to a greater understanding of the properties of successful drugs. However, the information required to perform these analyses is rarely published, and when it is made available it is often missing crucial data or is in a format that is inappropriate for efficient data-mining. Here, we propose a solution: the definition of reporting guidelines for bioactive entities - the Minimum Information About a Bioactive Entity (MIABE) - which has been developed by representatives of pharmaceutical companies, data resource providers and academic groups.