Ford Bass (yarnstew8)

In assessing prediction accuracy of multivariable prediction models, optimism corrections are essential for preventing biased results. However, in most published papers of clinical prediction models, the point estimates of the prediction accuracy measures are corrected by adequate bootstrap-based correction methods, but their confidence intervals are not corrected, for example, the DeLong's confidence interval is usually used for assessing the C-statistic. These naïve methods do not adjust for the optimism bias and do not account for statistical variability in the estimation of parameters in the prediction models. Therefore, their coverage probabilities of the true value of the prediction accuracy measure can be seriously below the nominal level (eg, 95%). In this article, we provide two generic bootstrap methods, namely, (1) location-shifted bootstrap confidence intervals and (2) two-stage bootstrap confidence intervals, that can be generally applied to the bootstrap-based optimism correction methods, that is, the Harrell's bias correction, 0.632, and 0.632+ methods. In addition, they can be widely applied to various methods for prediction model development involving modern shrinkage methods such as the ridge and lasso regressions. Through numerical evaluations by simulations, the proposed confidence intervals showed favorable coverage performances. Besides, the current standard practices based on the optimism-uncorrected methods showed serious undercoverage properties. To avoid erroneous results, the optimism-uncorrected confidence intervals should not be used in practice, and the adjusted methods are recommended instead. We also developed the R package predboot for implementing these methods ( https//github.com/nomahi/predboot). The effectiveness of the proposed methods are illustrated via applications to the GUSTO-I clinical trial.Monoclonal anti-SARS-CoV-2 immunoglobulins represent a treatment option for COVID-19. However, their production in mammalian cells is not scalable to meet the global demand. Single-domain (VHH) antibodies (also called nanobodies) provide an alternative suitable for microbial production. this website Using alpaca immune libraries against the receptor-binding domain (RBD) of the SARS-CoV-2 Spike protein, we isolated 45 infection-blocking VHH antibodies. These include nanobodies that can withstand 95°C. The most effective VHH antibody neutralizes SARS-CoV-2 at 17-50 pM concentration (0.2-0.7 µg per liter), binds the open and closed states of the Spike, and shows a tight RBD interaction in the X-ray and cryo-EM structures. The best VHH trimers neutralize even at 40 ng per liter. We constructed nanobody tandems and identified nanobody monomers that tolerate the K417N/T, E484K, N501Y, and L452R immune-escape mutations found in the Alpha, Beta, Gamma, Epsilon, Iota, and Delta/Kappa lineages. We also demonstrate neutralization of the Beta strain at low-picomolar VHH concentrations. We further discovered VHH antibodies that enforce native folding of the RBD in the E. coli cytosol, where its folding normally fails. Such "fold-promoting" nanobodies may allow for simplified production of vaccines and their adaptation to viral escape-mutations. <br> Numerous empirical studies and theoretical models posit that shame is a common experience among individuals across the eating disorder spectrum. In this study we aim to investigate the association between shame and eating disorders symptoms using a meta-analytical approach. <br> <br> In this meta-analysis, we synthesized findings from 195 studies to examine the proposed association between shame and eating disorders symptoms. We looked at the associations with both general eating disorders symptoms and with specific eating disorders symptoms (i.e., anorexic, bulimic, and binge-eating symptoms). Moderation analyses testing for the effect of type of shame, type of eating symptoms, clinical status, quality of the study, age, and gender were conducted. <br> <