Toxicity, inflammation, and anti-human immunodeficiency virus type 1 activity following exposure to chemical moieties of C31G.

C31G, which has potent activity against the human immunodeficiency virus type 1 (HIV-1) and an established record of safety in animal studies and human trials, is a microbicidal agent comprised of a buffered equimolar mixture of two amphoteric, surface-active agents: an alkyl amine oxide (C14AO) and an alkyl betaine (C16B). Studies of long-term in vitro exposure to C31G and its constituents have suggested that the components of C31G may contribute differentially to its toxicity and efficacy. In the present studies, in vitro assays of cytotoxicity and anti-HIV-1 activity demonstrated that C16B was slightly less cytotoxic compared to either C31G or C14AO, whereas the anti-HIV-1 activities of C31G and its individual constituents were similar. In the murine model of cervicovaginal microbicide toxicity, in vivo exposure to C14AO resulted in severe cervical inflammation followed by a delayed disruption of the columnar epithelium. In contrast, exposure to C16B caused severe cervical epithelial disruption and a secondary, less intense inflammatory response. These results demonstrate that (i) there are both mechanistic and temporal differences in toxicity associated with the components of C31G not necessarily predicted by in vitro assessments of cytotoxicity and (ii) contributions of each component to the anti-HIV-1 activity of C31G appear to be equal. In addition, these findings indicate that direct and indirect mechanisms of in vivo toxicity can be observed as separate but interrelated events. These results provide further insight into the activity of C31G, as well as mechanisms potentially associated with microbicide toxicity.     

Statistical clues to postoperative blood loss: Moving averages applied to medical data

With the advent of computerized databases, medical data has become easy to accumulate; however, effective use of this data continues to pose significant problems. In other circumstances, smoothing algorithms have been used to uncover non-obvious correlations, trends and relationships in noisy data. We have applied four such algorithms to a large dataset of postoperative blood replacement in cardiopulmonary bypass patients. When applied to this dataset, one of the algorithms proved surprisingly effective. It confirmed several previously observed correlations, and also provided an additional series of counterintuitive and apparently unrelated associations. These associations have been explored in an accompanying paper.