Long-term viral suppression requires consistent and high percent adherence accompanied by optimal interdose intervals. Efforts to improve viral outcomes should address not only missed doses but excessive variation in dose timing and prevention of adherence decline over time. Preventing the development and transmission of resistant variants is also critically important.

The receiver operating characteristic (ROC) curve is widely used for diagnosing as well as for judging the discrimination ability of different statistical models. Although theories about ROC curves have been established and computation methods and computer software are available for cross-sectional design, limited research for estimating ROC curves and their summary statistics has been done for repeated measure designs, which are useful in many applications, such as biological, medical and health services research. Furthermore, there is no published statistical software available that can generate ROC curves and calculate summary statistics of the area under a ROC curve for data from a repeated measures design. Using generalized linear mixed model (GLMM), we estimate the predicted probabilities of the positivity of a disease or condition, and the estimated probability is then used as a bio-marker for constructing the ROC curve and computing the area under the curve. The area under a ROC curve is calculated using the Wilcoxon non-parametric approach by comparing the predicted probability of all discordant pairs of observations. The ROC curve is constructed by plotting a series of pairs of true positive rate (sensitivity) and false positive rate (1-specificity) calculated from varying cuts of positivity escalated by increments of 0.005 in predicted probability. The computation software is written in SAS/IML/MACRO v8 and can be executed in any computer that has a working SAS v8 system with SAS/IML/MACRO.

Summary Minichromosomal maintenance (MCM) proteins are participants of DNA replication and may represent more accurate markers in determining the proliferative fraction within a tumor than proliferative marker Ki-67. Our study investigated the correlation between MCM4 and MCM7 expression and Ki-67, Bmi1, and cyclin E expression in esophageal adenocarcinoma, squamous cell carcinoma, and precancerous lesions. MCM4 and MCM7 expression had similar distribution as Ki-67 and Bmi1 expression in esophageal carcinoma and pre-cancerous lesions. The mean percentage of MCM4, MCM7, and Ki-67 expression increased from squamous epithelium (5.5%, 7.3%, and 5.9%, respectively), to columnar cell metaplasia (11.2, 13.5%, and 3.4%), Barrett's esophagus (27.7%, 35.3%, and 8.3%), low-grade dysplasia (42.6%, 52.2%, and 12.9%), high-grade dysplasia (63.2%, 77.7%, and 29.6%), adenocarcinoma (61.3%, 75.5%, and 24.5%), and squamous cell carcinoma (74.1, 85.4%, and 36.3%). The percentages of MCM4 and MCM7 expression were significantly higher than Ki-67 expression. Using univariate analysis we found a high percentage of MCM4 expression (>70%) to be significantly associated with lymph node metastasis and shorter survival in the adenocarcinoma group. We also demonstrated the percentage of MCM4 and MCM7 expression to be significantly correlated with Ki-67, Bmi1, and cyclin E expression in esophageal carcinoma and precancerous lesions. MCM4 and MCM7 may serve as more sensitive proliferative markers for the evaluation of esophageal lesions.

Tumor necrosis factor-alpha receptor knockout (TNF-alphaRKO) mice have homozygous deletions of the genes that code for both the 55- and 75-kD receptors. The mice are protected from the fibrogenic effects of bleomycin, silica, and inhaled asbestos. The asbestos-exposed animals exhibit reduced expression of other peptide growth factors such as transforming growth factor (TGF)-alpha, platelet-derived growth factors, and TGF-beta. In normal animals, these and other cytokines are elaborated at high levels during the development of fibroproliferative lung disease, but there is little information available that has allowed investigators to establish the role of the individual growth factors in disease pathogenesis. Here, we show that overexpression of TGF-beta(1) by means of a replication-deficient adenovirus vector induces fibrogenesis in the lungs of the fibrogenic-resistant TNF-alphaRKO mice. The fibrogenic lesions developed in both the KO and background controls within 7 d, and both types of animals exhibited similar incorporation of bromodeoxyuridine. Interestingly, airway epithelial cell proliferation appeared to be suppressed, perhaps due to the presence of the TGF-beta(1), a well-known inhibitor of epithelial mitogenesis. Before these experiments, there was no information available that would provide a basis for predicting whether or not TGF-beta(1) expression induces fibroproliferative lung disease in fibrogenic-resistant TNF-alphaRKO mice, an increasingly popular animal model.