Explore the vast range of titles available.

Chromatographic protein separations, immunoassays, and biosensing all typically involve the adsorption of proteins to surfaces decorated with charged, hydrophobic, or affinity ligands. Despite increasingly widespread use throughout the pharmaceutical industry, mechanistic detail about the interactions of proteins with individual chromatographic adsorbent sites is available only via inference from ensemble measurements such as binding isotherms, calorimetry, and chromatography. In this work, we present the direct superresolution mapping and kinetic characterization of functional sites on ion-exchange ligands based on agarose, a support matrix routinely used in protein chromatography. By quantifying the interactions of single proteins with individual charged ligands, we demonstrate that clusters of charges are necessary to create detectable adsorption sites and that even chemically identical ligands create adsorption sites of varying kinetic properties that depend on steric availability at the interface. Additionally, we relate experimental results to the stochastic theory of chromatography. Simulated elution profiles calculated from the molecular-scale data suggest that, if it were possible to engineer uniform optimal interactions into ion-exchange systems, separation efficiencies could be improved by as much as a factor of five by deliberately exploiting clustered interactions that currently dominate the ion-exchange process only accidentally.

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Mental disorders can have a major impact on brain development. Peripheral blood concentrations of brain-derived neurotrophic factor (BDNF) are lower in adult psychiatric disorders. Serum BDNF concentrations and BDNF genotype have been associated with cortical maturation in children and adolescents. In 2 large independent samples, this study tests associations between serum BDNF concentrations, brain structure, and psychopathology, and the effects of BDNF genotype on BDNF serum concentrations in late childhood and early adolescence. Children and adolescents (7-14 years old) from 2 cities (n = 267 in Porto Alegre; n = 273 in São Paulo) were evaluated as part of the Brazilian high-risk cohort (HRC) study. Serum BDNF concentrations were quantified by sandwich ELISA. Genotyping was conducted from blood or saliva samples using the SNParray Infinium HumanCore Array BeadChip. Subcortical volumes and cortical thickness were quantified using FreeSurfer. The Development and Well-Being Behavior Assessment was used to identify the presence of a psychiatric disorder. Serum BDNF concentrations were not associated with subcortical volumes or with cortical thickness. Serum BDNF concentration did not differ between participants with and without mental disorders, or between Val homozygotes and Met carriers. No evidence was found to support serum BDNF concentrations as a useful marker of developmental differences in brain and behavior in early life. Negative findings were replicated in 2 of the largest independent samples investigated to date.