Virologic failure was less likely in the efavirenz group than in the lopinavir-ritonavir group. The virologic efficacy of the NRTI-sparing regimen was similar to that of the efavirenz regimen but was more likely to be associated with drug resistance. (ClinicalTrials.gov number, NCT00050895 [ClinicalTrials.gov].).

Background The metabolic effects of initial therapy for HIV-1 infection are important determinants of regimen selection. Methods Open-label study in 753 subjects randomized equally to: efavirenz or lopinavir/ritonavir(r) plus two NRTI versus the NRTI-sparing regimen of lopinavir/r plus efavirenz. Zidovudine, stavudine, or tenofovir with lamivudine was selected prior to randomization. Metabolic outcomes through 96-weeks were lipoatrophy, defined as ≥20% loss of extremity fat, and fasting serum lipids. Results Lipoatrophy by DEXA at week 96 occurred in 32% (95% confidence interval 25%, 39%) of subjects in the efavirenz plus two NRTI arm, 17% (12%,24%) in the lopinavir/r plus two NRTI arm, and 9% (5,14%) in the NRTI-sparing arm (p≤0.023 for all comparisons). Varying the definition of lipoatrophy (≥10% to ≥40% fat loss) and correction for baseline risk factors did not affect the significant difference in lipoatrophy between the NRTI-containing regimens. Lipoatrophy was most frequent with stavudine-containing regimens and least frequent with tenofovir-containing regimens (p<0.001), which were not significantly different from the NRTI-sparing regimen. Total cholesterol increases at week 96 were greatest in the NRTI-sparing arm (median +57 mg/dL) compared to the other two arms (+32-33 mg/dL, p<.001). Use of lipid lowering agents was more common (25% versus 11-13%) in the NRTI-sparing arm. Conclusion Lipoatrophy was more frequent with efavirenz than lopinavir/r when combined with stavudine or zidovudine, and less frequent when either drug was combined with tenofovir. Lipoatrophy was least frequent with the NRTI-sparing regimen, but this benefit was offset by greater cholesterol elevations and the need for lipid lowering agents.

To control the antibiotic resistance epidemic, it is necessary to understand the distribution of genetic material encoding antibiotic resistance in the environment and how anthropogenic inputs, such as wastewater, affect this distribution. Approximately two-thirds of antibiotics administered to humans are ␤-lactams, for which the predominant bacterial resistance mechanism is hydrolysis by ␤-lactamases. Of the ␤-lactamases, the TEM family is of overriding significance with regard to diversity, prevalence, and distribution. This paper describes the design of DNA probes universal for all known TEM ␤-lactamase genes and the application of a quantitative PCR assay (also known as Taqman) to quantify these genes in environmental samples. The primer set was used to study whether sewage, both treated and untreated, contributes to the spread of these genes in receiving waters. It was found that while modern sewage treatment technologies reduce the concentrations of these antibiotic resistance genes, the ratio of bla TEM genes to 16S rRNA genes increases with treatment, suggesting that bacteria harboring bla TEM are more likely to survive the treatment process. Thus, ␤-lactamase genes are being introduced into the environment in significantly higher concentrations than occur naturally, creating reservoirs of increased resistance potential.

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We examine the asymptotic and small sample properties of model-based and robust tests of the null hypothesis of no randomized treatment effect based on the partial likelihood arising from an arbitrarily misspeci®ed Cox proportional hazards model. When the distribution of the censoring variable is either conditionally independent of the treatment group given covariates or conditionally independent of covariates given the treatment group, the numerators of the partial likelihood treatment score and Wald tests have asymptotic mean equal to 0 under the null hypothesis, regardless of whether or how the Cox model is misspeci®ed. We show that the modelbased variance estimators used in the calculation of the model-based tests are not, in general, consistent under model misspeci®cation, yet using analytic considerations and simulations we show that their true sizes can be as close to the nominal value as tests calculated with robust variance estimators. As a special case, we show that the model-based log-rank test is asymptotically valid. When the Cox model is misspeci®ed and the distribution of censoring depends on both treatment group and covariates, the asymptotic distributions of the resulting partial likelihood treatment score statistic and maximum partial likelihood estimator do not, in general, have a zero mean under the null hypothesis. Here neither the fully model-based tests, including the log-rank test, nor the robust tests will be asymptotically valid, and we show through simulations that the distortion to test size can be substantial.