5 resultados para least absolute deviation

em DigitalCommons@The Texas Medical Center


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BACKGROUND: Few reports of the utilization of an accurate, cost-effective means for measuring HPV oncogene transcripts have been published. Several papers have reported the use of relative quantitation or more expensive Taqman methods. Here, we report a method of absolute quantitative real-time PCR utilizing SYBR-green fluorescence for the measurement of HPV E7 expression in cervical cytobrush specimens. RESULTS: The construction of a standard curve based on the serial dilution of an E7-containing plasmid was the key for being able to accurately compare measurements between cervical samples. The assay was highly reproducible with an overall coefficient of variation of 10.4%. CONCLUSION: The use of highly reproducible and accurate SYBR-based real-time polymerase chain reaction (PCR) assays instead of performing Taqman-type assays allows low-cost, high-throughput analysis of viral mRNA expression. The development of such assays will help in refining the current screening programs for HPV-related carcinomas.

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The RPC developed a new phantom to ensure comparable and consistent radiation administration in spinal radiosurgery clinical trials. This study assessed the phantom’s dosimetric and anatomic utility. The ‘spine phantom’ is a water filled thorax with anatomy encountered in spinal radiosurgery: target volume, vertebral column, spinal canal, esophagus, heart, and lungs. The dose to the target volume was measured with axial and sagittal planes of radiochromic film and thermoluminescent dosimeters (TLD). The dose distributions were measured with the radiochromic film calibrated to the absolute dose measured by the TLD. Four irradiations were administered: a four angle box plan, a seven angle conformal plan, a seven angle IMRT plan, and a nine angle IMRT plan (denoted as IMRT plan #1 and plan #2, respectively). In each plan, at least 95% of the defined tumor volume received 8 Gy. For each irradiation the planned and administered dose distributions were registered via pinpricks, and compared using point dose measurements, dose profiles, isodose distributions, and gamma analyses. Based on previous experience at the RPC, a gamma analysis was considering passing if greater than 95% of pixels passed the criteria of 5% dose difference and 3 mm distance-to-agreement. Each irradiation showed acceptable agreement in the qualitative assessments and exceeded the 95% passing rate at the 5% / 3 mm criteria, except IMRT plan #1, which was determined to have been poorly localized during treatment administration. The measured and planned dose distributions demonstrated acceptable agreement at the 5% / 3 mm criteria, and the spine phantom was determined to be a useful tool for the remote assessment of an institution’s treatment planning and dose delivery regimen.

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Class I major histocompatibility complex (MHC) molecules induce either accelerated rejection or prolonged survival of allografts, presumably because of the presence of immunogenic or tolerogenic epitopes, respectively. To explore the molecular basis of this phenomenon, three chimeric class I molecules were constructed by substituting the rat class I RT1.A$\sp{\rm a}$ sequences with the N-terminus of HLA-A2.1 (N$\sp{\rm HLA-A2.1}$-RT1.A$\sp{\rm a}$), the $\alpha\sb1$ helix (h) with $\rm\alpha\sb{1h}\sp{u}$ sequences ( ($\rm\alpha\sb{1h}\sp{u}$) -RT1.A$\sp{\rm a}$) or the entire $\alpha\sb2$ domain (d) with $\rm\alpha\sb{2d}\sp{u}$ sequences ( ($\rm\alpha\sb{2d}\sp{u}$) -RT1.A$\sp{\rm a}$). Wild type (WT) and chimeric cDNAs were sequenced prior to transfection into Buffalo (BUF; RT1$\sp{\rm b}$) hepatoma cells. Stable transfectants were injected subcutaneously (s.c.) into different hosts 7 days prior to challenge with a heart allograft. In BUF hosts, chimeric ($\rm\alpha\sb{1h}\sp{u}$) -RT1.A$\sp{\rm a}$ accelerated the rejection of Wistar Furth (WF; RT1$\sp{\rm u}$) heart allografts, but had no effect on the survival of ACI (RT1$\sp{\rm a}$) grafts. In contrast, the ($\rm\alpha\sb{2d}\sp{u}$) -RT1.A$\sp{\rm a}$ (containing $\rm\alpha\sb{1d}\sp{a}$ sequences) immunized BUF recipients toward RT1$\sp{\rm a}$ grafts. In WF hosts, WT-RT1.A$\sp{\rm a}$ was a potent immunogen and accelerated ACI graft rejection, N$\sp{\rm HLA-A2.1}$-RT1.A$\sp{\rm a}$ was less effective and ($\rm\alpha\sb{\rm 1h}\sp{u}\rbrack$-RT1.A$\sp{\rm a}$ was not immunogenic. Thus, dominant and subdominant epitopes inducing in vivo sensitization to cardiac allografts are present in the $\alpha\sb1$ helix and the N-terminus, respectively. The failure of ($\rm\alpha\sb{2d}\sp{u}$) -RT1.A$\sp{\rm a}$ transfectants (containing recipient-type $\alpha\sb{\rm 2d}$ sequences) to sensitize WF hosts toward ACI (RT1$\sp{\rm a}$) grafts, despite the presence of donor-type immunogenic $\alpha\sb{\rm 1d}\sp{\rm a}$, suggests that "self-$\alpha\sb2$" sequences displayed on chimeric antigens interfere with immunogenicity. The ($\rm\alpha\sb{1h}\sp{u}$) -RT1.A$\sp{\rm a}$ transfectants injected s.c. prolonged the survival of WF (RT1$\sp{\rm u}$) hearts in ACI (RT1$\sp{\rm a}$) recipients. Furthermore, intra-portal injection of extracts from ($\rm\alpha\sb{1h}\sp{u}$) -RT1.A$\sp{\rm a}$, but not WT-RT1.A$\sp{\rm a}$ or RT1.A$\sp{\rm u}$, in conjunction with a brief cyclosporine course rendered ACI hosts permanently and specifically tolerant to donor-type WF cardiac allografts. Thus, immunodominant allodeterminants are present in the $\alpha\sb1$, but not the $\alpha\sb2$, domain of rat class I MHC molecules. Furthermore, the $\rm\alpha\sb{1h}\sp{u}$ immunogenic epitopes trigger tolerogenic responses when flanked by host-type N-terminal$\sp{\rm a}$ and $\rm\alpha\sb{2d}\sp{a}$ sequences. ^

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Environmental data sets of pollutant concentrations in air, water, and soil frequently include unquantified sample values reported only as being below the analytical method detection limit. These values, referred to as censored values, should be considered in the estimation of distribution parameters as each represents some value of pollutant concentration between zero and the detection limit. Most of the currently accepted methods for estimating the population parameters of environmental data sets containing censored values rely upon the assumption of an underlying normal (or transformed normal) distribution. This assumption can result in unacceptable levels of error in parameter estimation due to the unbounded left tail of the normal distribution. With the beta distribution, which is bounded by the same range of a distribution of concentrations, $\rm\lbrack0\le x\le1\rbrack,$ parameter estimation errors resulting from improper distribution bounds are avoided. This work developed a method that uses the beta distribution to estimate population parameters from censored environmental data sets and evaluated its performance in comparison to currently accepted methods that rely upon an underlying normal (or transformed normal) distribution. Data sets were generated assuming typical values encountered in environmental pollutant evaluation for mean, standard deviation, and number of variates. For each set of model values, data sets were generated assuming that the data was distributed either normally, lognormally, or according to a beta distribution. For varying levels of censoring, two established methods of parameter estimation, regression on normal ordered statistics, and regression on lognormal ordered statistics, were used to estimate the known mean and standard deviation of each data set. The method developed for this study, employing a beta distribution assumption, was also used to estimate parameters and the relative accuracy of all three methods were compared. For data sets of all three distribution types, and for censoring levels up to 50%, the performance of the new method equaled, if not exceeded, the performance of the two established methods. Because of its robustness in parameter estimation regardless of distribution type or censoring level, the method employing the beta distribution should be considered for full development in estimating parameters for censored environmental data sets. ^

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The performance of the Hosmer-Lemeshow global goodness-of-fit statistic for logistic regression models was explored in a wide variety of conditions not previously fully investigated. Computer simulations, each consisting of 500 regression models, were run to assess the statistic in 23 different situations. The items which varied among the situations included the number of observations used in each regression, the number of covariates, the degree of dependence among the covariates, the combinations of continuous and discrete variables, and the generation of the values of the dependent variable for model fit or lack of fit.^ The study found that the $\rm\ C$g* statistic was adequate in tests of significance for most situations. However, when testing data which deviate from a logistic model, the statistic has low power to detect such deviation. Although grouping of the estimated probabilities into quantiles from 8 to 30 was studied, the deciles of risk approach was generally sufficient. Subdividing the estimated probabilities into more than 10 quantiles when there are many covariates in the model is not necessary, despite theoretical reasons which suggest otherwise. Because it does not follow a X$\sp2$ distribution, the statistic is not recommended for use in models containing only categorical variables with a limited number of covariate patterns.^ The statistic performed adequately when there were at least 10 observations per quantile. Large numbers of observations per quantile did not lead to incorrect conclusions that the model did not fit the data when it actually did. However, the statistic failed to detect lack of fit when it existed and should be supplemented with further tests for the influence of individual observations. Careful examination of the parameter estimates is also essential since the statistic did not perform as desired when there was moderate to severe collinearity among covariates.^ Two methods studied for handling tied values of the estimated probabilities made only a slight difference in conclusions about model fit. Neither method split observations with identical probabilities into different quantiles. Approaches which create equal size groups by separating ties should be avoided. ^