Human pluripotent stem cell-based in vitro models that reflect human physiology have the potential to reduce the number of drug failures in clinical trials and offer a cost-effective approach for assessing chemical safety. Here, human embryonic stem (ES) cell-derived neural progenitor cells, endothelial cells, mesenchymal stem cells, and microglia/ macrophage precursors were combined on chemically defined polyethylene glycol hydrogels and cultured in serum-free medium to model cellular interactions within the developing brain. The precursors self-assembled into 3D neural constructs with diverse neuronal and glial populations, interconnected vascular networks, and ramified microglia. Replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed neurogenesis, vasculature development, and microglia genes. Linear support vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constructs treated with 34 toxic and 26 nontoxic chemicals. The predictive model was evaluated using two standard hold-out testing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an unbiased blinded trial using a single holdout set of 10 additional chemicals. The linear support vector produced an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 of 10 chemicals in the blinded trial.organoid | machine learning | tissue engineering | differentiation | toxicology T here is a pressing need for improved methods to assess the safety of drugs and other compounds (1-5). Success rates for drug approval are declining despite higher research and development spending (6), and clinical trials often fail due to toxicities that were not identified through animal testing (7). In addition, most of the chemicals in commerce have not been rigorously assessed for safety despite growing concerns over the potential impact of industrial and environmental exposures on human health (2-5). Animal models are costly, time consuming, and fail to recapitulate many aspects of human physiology, which has motivated agencies such as the National Institutes of Health (NIH) and the US Environmental Protection Agency (EPA) to initiate programs that emphasize human cellular approaches for assessing the safety of drugs (1) and environmental chemicals (2, 3). In vitro cellular models that accurately reflect human physiology have the potential to improve the prediction of drug toxicity early in the development pipeline (1) and would provide a cost-effective approach for testing other sources of chemical exposure, including food additives, cosmetics, pesticides, and industrial chemicals (2-5).The human brain is particularly sensitive to toxic insults during development and early childhood (8), and there is growing concern that exposure to environmental chemicals may be linked to the rising incidence of neurodevelopmental disorders worldwide (4). Human brain development is mediated by highly coordinated cellular interactions between functionally ...
Abstract. The area under the precision-recall curve (AUCPR) is a single number summary of the information in the precision-recall (PR) curve. Similar to the receiver operating characteristic curve, the PR curve has its own unique properties that make estimating its enclosed area challenging. Besides a point estimate of the area, an interval estimate is often required to express magnitude and uncertainty. In this paper we perform a computational analysis of common AUCPR estimators and their confidence intervals. We find both satisfactory estimates and invalid procedures and we recommend two simple intervals that are robust to a variety of assumptions.
BACKGROUND Prediction of subsequent school-age asthma during the preschool years has proven challenging. OBJECTIVE To confirm in a post hoc analysis the predictive ability of the modified Asthma Predictive Index (mAPI) in a high-risk cohort and a theoretical unselected population. We also tested a potential mAPI modification with a 2-wheezing episode requirement (m2API) in the same populations. METHODS Subjects (n = 289) with a family history of allergy and/or asthma were used to predict asthma at age 6, 8, and 11 years with the use of characteristics collected during the first 3 years of life. The mAPI and the m2API were tested for predictive value. RESULTS For the mAPI and m2API, school-age asthma prediction improved from 1 to 3 years of age. The mAPI had high predictive value after a positive test (positive likelihood ratio ranging from 4.9 to 55) for asthma development at years 6, 8, and 11. Lowering the number of wheezing episodes to 2 (m2API) lowered the predictive value after a positive test (positive likelihood ratio ranging from 1.91 to 13.1) without meaningfully improving the predictive value of a negative test. Posttest probabilities for a positive mAPI reached 72% and 90% in unselected and high-risk populations, respectively. CONCLUSIONS In a high-risk cohort, a positive mAPI greatly increased future asthma probability (eg, 30% pretest probability to 90% posttest probability) and is a preferred predictive test to the m2API. With its more favorable positive posttest probability, the mAPI can aid clinical decision making in assessing future asthma risk for preschool-age children.
In the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.