Begum Sunesen (nicpillow8)

ncer patients. Increased risk of progression from latent tuberculosis infection (LTBI) to tuberculosis (TB) disease among people living with HIV (PLWH) prioritizes them for LTBI testing and treatment. Studies comparing the performance of interferon gamma release assays (IGRAs) and the tuberculin skin test (TST) among PLWH are lacking. We used Bayesian latent class analysis to estimate the prevalence of LTBI and diagnostic characteristics of the TST, QuantiFERON Gold In-Tube (QFT), and T.SPOT-TB (TSPOT) among a prospective, multicenter cohort of U.S.-born PLWH ≥5 years old with valid results for all three LTBI tests using standard U.S. cutoffs (≥5mm TST, ≥0.35 IU/mL QFT, ≥8 spots TSPOT). We also explored the performance of varying LTBI test cutoffs. Among 1510 PLWH (median CD4+ count 532 cells/mm3), estimated LTBI prevalence was 4.7%. TSPOT was significantly more specific (99.7%) and had a significantly higher positive predictive value (90.0%, PPV) than QFT (96.5% specificity, 50.7% PPV) and TST (96.8% specificity, 45.4% PPV). QFT was significantly more sensitive (72.2%) than TST (54.2%) and TSPOT (51.9%); negative predictive value of all tests was high (TST 97.7%, QFT 98.6%, TSPOT 97.6%). Even at the highest cutoffs evaluated (15mm TST, ≥1.00 IU/mL QFT, ≥8 spots TSPOT), TST and QFT specificity was significantly lower than TSPOT. LTBI prevalence among this cohort of U.S.-born PLWH was low compared to non-U.S. born persons. TSPOT's higher PPV may make it preferable for testing U.S.-born PLWH at low risk for TB exposure and with high CD4+ counts. LTBI prevalence among this cohort of U.S.-born PLWH was low compared to non-U.S. born persons. TSPOT's higher PPV may make it preferable for testing U.S.-born PLWH at low risk for TB exposure and with high CD4+ counts. Single protein residue mutations may reshape the binding affinity of protein-protein interactions. Therefore, predicting its effects is of great interest in biotechnology and biomedicine. Unfortunately, the availability of experimental data on binding affinity changes upon mutation is limited, which hampers the development of new and more precise algorithms. Here, we propose UEP, a classifier for predicting beneficial and detrimental mutations in protein-protein complexes trained on interactome data. Regardless of the simplicity of the UEP algorithm, which is based on a simple three-body contact potential derived from interactome data, we report competitive results with the gold standard methods in this field with the advantage of being faster in terms of computational time. Moreover, we propose a consensus selection procedure by involving the combination of three predictors that showed higher classification accuracy in our benchmark UEP, pyDock and EvoEF1/FoldX. Overall, we demonstrate that the analysis of interactome data allows predicting the impact of protein-protein mutations by using UEP, a fast and reliable open-source code. UEP algorithm can be found at https//github.com/pepamengual/UEP. Supplementary data are available at Bioinformatics online. Supplementary data are available at Bioinformatics online.Gibberellins (GAs) are a class of tetracyclic diterpenoid phytohormones that regulate many aspects of plant development, including seed germination, stem elongation, leaf expansion, pollen maturation, and the development of flowers, fruits and seeds. During the past decades, the primary objective of crop breeding programs has been to increase productivity or yields. 'Green Revolution' genes that can produce semidwarf, high-yielding crops were identified as GA synthesis or response genes, confirming the value of research on GAs in improving crop productivity. The manipulation of GA status either by genetic alteration or by exogenous application of GA or GA biosynthesis inhibitors is often used to optimize plant growth and yields. In this review, we summarize the roles of GA