PubMed · 7903751
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C Warlow. 1994-01-08. Unlocking datasets.. https://pubmed.ncbi.nlm.nih.gov/7903751/
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INTRODUCTION: Sustainable control of malaria in sub-Saharan Africa is jeopardized by dwindling public health resources resulting from competing health priorities that include an overwhelming acquired immunodeficiency syndrome (AIDS) epidemic. In Mpumalanga province, South Africa, rational planning has historically been hampered by a case surveillance system for malaria that only provided estimates of risk at the magisterial district level (a subdivision of a province). METHODS: To better map control programme activities to their geographical location, the malaria notification system was overhauled and a geographical information system implemented. The introduction of a simplified notification form used only for malaria and a carefully monitored notification system provided the good quality data necessary to support an effective geographical information system. RESULTS: The geographical information system displays data on malaria cases at a village or town level and has proved valuable in stratifying malaria risk within those magisterial districts at highest risk, Barberton and Nkomazi. The conspicuous west-to-east gradient, in which the risk rises sharply towards the Mozambican border (relative risk = 4.12, 95% confidence interval = 3.88-4.46 when the malaria risk within 5 km of the border was compared with the remaining areas in these two districts), allowed development of a targeted approach to control. DISCUSSION: The geographical information system for malaria was enormously valuable in enabling malaria risk at town and village level to be shown. Matching malaria control measures to specific strata of endemic malaria has provided the opportunity for more efficient malaria control in Mpumalanga province.
In computational structure-based drug design, the scoring functions are the cornerstones to the success of design/discovery. Many approaches have been explored to improve their reliability and accuracy, leading to three families of scoring functions: force-field-based, knowledge-based, and empirical. The last family is the most widely used in association with docking algorithms because of its speed, even though such empirical scoring functions produce far too many false positives to be fully reliable. In this work, we describe a World Wide Web accessible database that gathers the structural information from known complexes of the PDB with experimental binding data. This database, the Ligand-Protein DataBase (LPDB), is designed to allow the selection of complexes based on various properties of receptors and ligands for the design and parametrization of new scoring functions or to assess and improve existing ones. Moreover, for each complex, a continuum of ligand positions ranging from the crystallographic position to points on the surface of the protein receptor allows an assessment of the energetic behavior of particular scoring functions.