Integrating inflammatory biomarker analysis and artificial-intelligence-enabled image-based profiling to identify drug targets for intestinal fibrosis.

Intestinal fibrosis, often caused by inflammatory bowel disease, can lead to intestinal stenosis and obstruction, but there are no approved treatments. Drug discovery has been hindered by the lack of screenable cellular phenotypes. To address this, we used a scalable image-based morphology assay called Cell Painting, augmented with machine learning algorithms, to identify small molecules that could reverse the activated fibrotic phenotype of intestinal myofibroblasts. We then conducted a high-throughput small molecule chemogenomics screen of approximately 5,000 compounds with known targets or mechanisms, which have achieved clinical stage or approval by the FDA. By integrating morphological analyses and AI using pathologically relevant cells and disease-relevant stimuli, we identified several compounds and target classes that are potentially able to treat intestinal fibrosis. This phenotypic screening platform offers significant improvements over conventional methods for identifying a wide range of drug targets.

Number lines can be more effective at facilitating adults' performance on health-related ratio problems than risk ladders and icon arrays.

Visual displays, such as icon arrays and risk ladders, are often used to communicate numerical health information. Number lines improve reasoning with rational numbers but are seldom used in health contexts. College students solved ratio problems related to COVID-19 (e.g., number of deaths and number of cases) in one of four randomly assigned conditions: icon arrays, risk ladders, number lines, or no accompanying visual display. As predicted, number lines facilitated performance on these problems-the number line condition outperformed the other visual display conditions, which did not perform any better than the no visual display condition. In addition, higher performance on the health-related ratio problems was associated with higher COVID-19 worry for oneself and others, higher perceptions of COVID-19 severity, and higher endorsement of intentions to engage in preventive health behaviors, even when controlling for baseline math skills. These findings have important implications for effectively presenting health statistics. (PsycInfo Database Record (c) 2023 APA, all rights reserved).