The performance of human papillomavirus high-risk DNA testing in the screening and diagnostic settings.

OBJECTIVE: We sought to evaluate the performance of the human papillomavirus high-risk DNA test in patients 30 years and older. MATERIALS AND METHODS: Screening (n=835) and diagnosis (n=518) groups were defined based on prior Papanicolaou smear results as part of a clinical trial for cervical cancer detection. We compared the Hybrid Capture II (HCII) test result with the worst histologic report. We used cervical intraepithelial neoplasia (CIN) 2/3 or worse as the reference of disease. We calculated sensitivities, specificities, positive and negative likelihood ratios (LR+ and LR-), receiver operating characteristic (ROC) curves, and areas under the ROC curves for the HCII test. We also considered alternative strategies, including Papanicolaou smear, a combination of Papanicolaou smear and the HCII test, a sequence of Papanicolaou smear followed by the HCII test, and a sequence of the HCII test followed by Papanicolaou smear. RESULTS: For the screening group, the sensitivity was 0.69 and the specificity was 0.93; the area under the ROC curve was 0.81. The LR+ and LR- were 10.24 and 0.34, respectively. For the diagnosis group, the sensitivity was 0.88 and the specificity was 0.78; the area under the ROC curve was 0.83. The LR+ and LR- were 4.06 and 0.14, respectively. Sequential testing showed little or no improvement over the combination testing. CONCLUSIONS: The HCII test in the screening group had a greater LR+ for the detection of CIN 2/3 or worse. HCII testing may be an additional screening tool for cervical cancer in women 30 years and older.

POLG DNA testing as an emerging standard of care before instituting valproic acid therapy for pediatric seizure disorders.

PURPOSE: To review our clinical experience and determine if there are appropriate signs and symptoms to consider POLG sequencing prior to valproic acid (VPA) dosing in patients with seizures. METHODS: Four patients who developed VPA-induced hepatotoxicity were examined for POLG sequence variations. A subsequent chart review was used to describe clinical course prior to and after VPA dosing. RESULTS: Four patients of multiple different ethnicities, age 3-18 years, developed VPA-induced hepatotoxicity. All were given VPA due to intractable partial seizures. Three of the patients had developed epilepsia partialis continua. The time from VPA exposure to liver failure was between 2 and 3 months. Liver failure was reversible in one patient. Molecular studies revealed homozygous p.R597W or p.A467T mutations in two patients. The other two patients showed compound heterozygous mutations, p.A467T/p.Q68X and p.L83P/p.G888S. Clinical findings and POLG mutations were diagnostic of Alpers-Huttenlocher syndrome. CONCLUSION: Our cases underscore several important findings: POLG mutations have been observed in every ethnic group studied to date; early predominance of epileptiform discharges over the occipital region is common in POLG-induced epilepsy; the EEG and MRI findings varying between patients and stages of the disease; and VPA dosing at any stage of Alpers-Huttenlocher syndrome can precipitate liver failure. Our data support an emerging proposal that POLG gene testing should be considered in any child or adolescent who presents or develops intractable seizures with or without status epilepticus or epilepsia partialis continua, particularly when there is a history of psychomotor regression.

Phenotypic and genotypic analysis of mouse hepatitis virus (JHM) complementation group A temperature-sensitive mutants

Temperature sensitive (ts) mutant viruses have helped elucidate replication processes in many viral systems. Several panels of replication-defective ts mutants in which viral RNA synthesis is abolished at the nonpermissive temperature (RNA$\sp{-})$ have been isolated for Mouse Hepatitis Virus, MHV (Robb et al., 1979; Koolen et al., 1983; Martin et al., 1988; Schaad et al., 1990). However, no one had investigated genetic or phenotypic relationships between these different mutant panels. In order to determine how the panel of MHV-JHM RNA$\sp{-}$ ts mutants (Robb et al., 1979) were genetically related to other described MHV RNA$\sp{-}$ ts mutants, the MHV-JHM mutants were tested for complementation with representatives from two different sets of MHV-A59 ts mutants (Koolen et al., 1983; Schaad et al., 1990). The three ts mutant panels together were found to comprise eight genetically distinct complementation groups. Of these eight complementation groups, three complementation classes are unique to their particular mutant panel; genetically equivalent mutants were not observed within the other two mutant panels. Two complementation groups were common to all three mutant panels. The three remaining complementation groups overlapped two of the three mutant sets. Mutants MHV-JHM tsA204 and MHV-A59 ts261 were shown to be within one of these overlapping complementation groups. The phenotype of the MHV-JHM mutants within this complementation class has been previously characterized (Leibowitz et al., 1982; Leibowitz et al, 1990). When these mutants were grown at the permissive temperature, then shifted up to the nonpermissive temperature at the start of RNA synthesis, genome-length RNA and leader RNA fragments accumulated, but no subgenomic mRNA was synthesized. MHV-A59 ts261 produced leader RNA fragments identical to those observed with MHV-JHM tsA204. Thus, these two MHV RNA$\sp{-}$ ts mutants that were genetically equivalent by complementation testing were phenotypically similar as well. Recombination frequencies obtained from crosses of MHV-A59 ts261 with several of the gene 1 MHV-A59 mutants indicated that the causal mutation(s) of MHV-A59 ts261 was located near the overlapping junction of ORF1a and ORF1b, in the 3$\sp\prime$ end of ORF1a, or the 5$\sp\prime$ end of ORF1b. Sequence analysis of this junction and 1400 nucleotides into the 5$\sp\prime$ end of ORF1b of MHV-A59 ts261 revealed one nucleotide change from the wildtype MHV-A59. This substitution at nucleotide 13,598 (A to G) was a silent mutation in the ORF1a reading frame, but resulted in an amino acid change in ORF1b gene product (I to V). This amino acid change would be expressed only in the readthrough translation product produced upon successful ribosome frameshifting. A revertant of MHV-A59 ts261 (R2) also retained this guanidine residue, but had a second substitution at nucleotide 14,475 in ORF1b. This mutation results in the substitution of valine for an isoleucine.^ The data presented here suggest that the mutation in MHV-A59 ts261 (nucleotide 13,598) would be responsible for the MHV-JHM complementation group A phenotype. A second-site reversion at nucleotide 14,475 may correct this defect in the revertant. Sequencing of gene 1 immediately upstream of nucleotide 13,296 and downstream of nucleotide 15,010 must be conducted to test this hypothesis. ^

Robust effect sizes and their confidence intervals for group difference between trajectories in hierarchical linear growth model

Hierarchical linear growth model (HLGM), as a flexible and powerful analytic method, has played an increased important role in psychology, public health and medical sciences in recent decades. Mostly, researchers who conduct HLGM are interested in the treatment effect on individual trajectories, which can be indicated by the cross-level interaction effects. However, the statistical hypothesis test for the effect of cross-level interaction in HLGM only show us whether there is a significant group difference in the average rate of change, rate of acceleration or higher polynomial effect; it fails to convey information about the magnitude of the difference between the group trajectories at specific time point. Thus, reporting and interpreting effect sizes have been increased emphases in HLGM in recent years, due to the limitations and increased criticisms for statistical hypothesis testing. However, most researchers fail to report these model-implied effect sizes for group trajectories comparison and their corresponding confidence intervals in HLGM analysis, since lack of appropriate and standard functions to estimate effect sizes associated with the model-implied difference between grouping trajectories in HLGM, and also lack of computing packages in the popular statistical software to automatically calculate them. ^ The present project is the first to establish the appropriate computing functions to assess the standard difference between grouping trajectories in HLGM. We proposed the two functions to estimate effect sizes on model-based grouping trajectories difference at specific time, we also suggested the robust effect sizes to reduce the bias of estimated effect sizes. Then, we applied the proposed functions to estimate the population effect sizes (d ) and robust effect sizes (du) on the cross-level interaction in HLGM by using the three simulated datasets, and also we compared the three methods of constructing confidence intervals around d and du recommended the best one for application. At the end, we constructed 95% confidence intervals with the suitable method for the effect sizes what we obtained with the three simulated datasets. ^ The effect sizes between grouping trajectories for the three simulated longitudinal datasets indicated that even though the statistical hypothesis test shows no significant difference between grouping trajectories, effect sizes between these grouping trajectories can still be large at some time points. Therefore, effect sizes between grouping trajectories in HLGM analysis provide us additional and meaningful information to assess group effect on individual trajectories. In addition, we also compared the three methods to construct 95% confident intervals around corresponding effect sizes in this project, which handled with the uncertainty of effect sizes to population parameter. We suggested the noncentral t-distribution based method when the assumptions held, and the bootstrap bias-corrected and accelerated method when the assumptions are not met.^