ArticleCell stem cell2016
What Can hiPSC-Cardiomyocytes Teach Us about Modeling Complex Human Disease Phenotypes?
Article in Cell stem cell, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Traction force microscopy of engineered cardiac tissues.PloS one · 2018Article
- Human iPSC-derived myocardium-on-chip with capillary-like flow for personalized medicine.Biomicrofluidics · 2017Article
Corrections and comments
- Comment on
Authors and funding
1 author.
Funding
Abstract
Using induced pluripotent stem cell-derived cardiomyocytes to predict patient-specific responses to drug treatments requires that they maintain inter-individual variability for RNA expression and recapitulate phenotypic responses in vitro. In this issue of Cell Stem Cell, Matsa et al. (2016) show that expression differences between hiPSC-cardiomyocytes provide a personalized drug testing platform to evaluate potential efficacy and cardiotoxicity.
Indexed as
Identifiers
27588742What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.