Katholieke Universiteit Leuven

About: Katholieke Universiteit Leuven is a education organization based out in Leuven, Belgium. It is known for research contribution in the topics: Population & Transplantation. The organization has 61109 authors who have published 176584 publications receiving 6210872 citations.

Antimicrobial Peptides from Plants

Abstract: Peptides with antimicrobial properties are present in most if not all plant species All plant antimicrobial peptides isolated so far contain even numbers of cysteines (4, 6, or 8), which are all pairwise connected by disulfide bridges, thus providing high stability to the peptides Based on homologies at the primary structure level, plant antimicrobial peptides can be classified into distinct families including thionins, plant defensins, lipid transfer proteins, and he vein- and knottin-type antimicrobial peptides Detailed three-dimensional structure information has been obtained for one or more members of these peptide families All antimicrobial peptides studied thus far appear to exert their antimicrobial effect at the level of the plasma membrane of the target microorganism, but the different peptide types are likely to act via different mechanisms Antimicrobial peptides can occur in all plant organs In unstressed organs, antimicrobial peptides are usually most abundant in the outer cell

Calibration: the Achilles heel of predictive analytics

Abstract: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention. Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice. Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling.