An IL28B Genotype-Based Clinical Prediction Model for Treatment of Chronic Hepatitis C

BACKGROUND:Genetic variation in IL28B and other factors are associated with sustained virological response (SVR) after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC). Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables. METHODS:HALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR) and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC). RESULTS:Among 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p<.0001); the model also included HCV RNA (log10 IU/ml), AST:ALT ratio, Ishak fibrosis score and prior ribavirin treatment. For this model AUC was 78.5%, compared to 73.0% for a model restricted to the four clinical predictors and 60.0% for a model restricted to IL28B genotype (p<0.001). Subjects with a predicted probability of SVR <10% had an observed SVR rate of 3.8%; subjects with a predicted probability >10% (43.3% of subjects) had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study. CONCLUSION:A clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC

Marginal tests with sliced average variance estimation

We present a new computationally feasible test for the dimension of the central subspace in a regression problem based on sliced average variance estimation. We also provide a marginal coordinate test. Under the null hypothesis, both the test of dimension and the marginal coordinate test involve test statistics that asymptotically have chi-squared distributions given normally distributed predictors, and have a distribution that is a linear combination of chi-squared distributions in general. Copyright 2007, Oxford University Press.

Comparison of diagnostic performance of AFP, DCP and two diagnostic models in hepatocellular carcinoma: a retrospective study

ABSTRACT: Introduction and Objectives: Hepatocellular carcinoma (HCC) may be diagnosed using the GAAP and ASAP models; our goal was to verify and evaluate their diagnostic effectiveness compared to alpha-fetoprotein (AFP), des-gamma-carboxy prothrombin (DCP), and AFP &amp; DCP for both HCC and HCC caused by the hepatitis B virus (HBV). Patients and Methods: GAAP and ASAP models were validated and compared using a retrospective investigation of 938 patients from our hospital between July 2020 and July 2021. Results: Both the GAAP and ASAP models had better diagnostic efficacy than AFP, DCP, AFP &amp; DCP. The GAAP model achieved better performance in section A for the detection of HCC and in section C for the detection of HBV-HCC than the ASAP model. The Hosmer-Lemeshow test showed that the GAAP and ASAP models were well-calibrated for the diagnoses of these two groups. To be more specific, the area under curve (AUC) of the GAAP model for HCC detection in section A was 0.862 [95% confidence interval (CI): 0.838-0.883], and that of the ASAP model was 0.850 [95% CI: 0.826-0.872]. The AUC of the GAAP model for HBV-HCC detection in section C was 0.897 [95% CI: 0.872-0.918], and that of the ASAP model was 0.878 [95% CI: 0.852-0.902]. Conclusions: The GAAP model was more accurate and reliable than the AFP, DCP, AFP and DCP, as well as the ASAP model in section A for the detection of HCC and in section C for the detection of HBV-HCC

An IL28B Genotype-Based Clinical Prediction Model for Treatment of Chronic Hepatitis C

BackgroundGenetic variation in IL28B and other factors are associated with sustained virological response (SVR) after pegylated-interferon/ribavirin treatment for chronic hepatitis C (CHC). Using data from the HALT-C Trial, we developed a model to predict a patient's probability of SVR based on IL28B genotype and clinical variables.MethodsHALT-C enrolled patients with advanced CHC who had failed previous interferon-based treatment. Subjects were re-treated with pegylated-interferon/ribavirin during trial lead-in. We used step-wise logistic regression to calculate adjusted odds ratios (aOR) and create the predictive model. Leave-one-out cross-validation was used to predict a priori probabilities of SVR and determine area under the receiver operator characteristics curve (AUC).ResultsAmong 646 HCV genotype 1-infected European American patients, 14.2% achieved SVR. IL28B rs12979860-CC genotype was the strongest predictor of SVR (aOR, 7.56; p&lt;.0001); the model also included HCV RNA (log10 IU/ml), AST∶ALT ratio, Ishak fibrosis score and prior ribavirin treatment. For this model AUC was 78.5%, compared to 73.0% for a model restricted to the four clinical predictors and 60.0% for a model restricted to IL28B genotype (p&lt;0.001). Subjects with a predicted probability of SVR &lt;10% had an observed SVR rate of 3.8%; subjects with a predicted probability &gt;10% (43.3% of subjects) had an SVR rate of 27.9% and accounted for 84.8% of subjects actually achieving SVR. To verify that consideration of both IL28B genotype and clinical variables is required for treatment decisions, we calculated AUC values from published data for the IDEAL Study.ConclusionA clinical prediction model based on IL28B genotype and clinical variables can yield useful individualized predictions of the probability of treatment success that could increase SVR rates and decrease the frequency of futile treatment among patients with CHC

Ultrathin Two-Dimensional Covalent Organic Framework Nanosheets: Preparation and Application in Highly Sensitive and Selective DNA Detection

The ability to prepare ultrathin two-dimensional (2D) covalent organic framework (COF) nanosheets (NSs) in high yield is of great importance for the further exploration of their unique properties and potential applications. Herein, by elaborately designing and choosing two flexible molecules with <i>C</i>_3<i>v</i> molecular symmetry as building units, a novel imine-linked COF, namely, TPA-COF, with a hexagonal layered structure and sheet-like morphology, is synthesized. Since the flexible building units are integrated into the COF skeletons, the interlayer stacking becomes weak, resulting in the easy exfoliation of TPA-COF into ultrathin 2D NSs. Impressively, for the first time, the detailed structural information, i.e., the pore channels and individual building units in the NSs, is clearly visualized by using the recently developed low-dose imaging technique of transmission electron microscopy (TEM). As a proof-of-concept application, the obtained ultrathin COF NSs are used as a novel fluorescence sensing platform for the highly sensitive and selective detection of DNA