ArticleAnalytical chemistry2018
Screening Estrogen Receptor Modulators in a Paper-Based Breast Cancer Model.
Article in Analytical chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed, 21 citations in OpenAlex.
- Opportunities and challenges for analytical chemists: organ-on-chip devices as new approach methods (NAMs) for identifying potential toxins.The Analyst · 2026Review
- A high-throughputActa pharmaceutica Sinica. B · 2023Article
- Generating linear oxygen gradients across 3D cell cultures with block-layered oxygen controlled chips (BLOCCs).Analytical methods : advancing methods and applications · 2020Article
- Developing a Drug Screening Platform: MALDI-Mass Spectrometry Imaging of Paper-Based Cultures.Analytical chemistry · 2019Article
- Tissue Papers: Leveraging Paper-Based Microfluidics for the Next Generation of 3D Tissue Models.Analytical chemistry · 2019Article
- Hypoxia differentially regulates estrogen receptor alpha in 2D and 3D culture formats.Archives of biochemistry and biophysics · 2019Article
- Paper-based Transwell assays: an inexpensive alternative to study cellular invasion.The Analyst · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
The health risks associated with acute and prolonged exposure to estrogen receptor (ER) modulators has led to a concerted effort to identify and prioritize potential disruptors present in the environment. ER agonists and antagonists are identified with end-point assays, quantifying changes in cellular proliferation or gene transactivation in monolayers of estrogen receptor alpha expressing (ER+) cells upon exposure. While these monolayer cultures can be prepared, dosed, and analyzed in a highly parallelized manner, they are unable to predict the potencies of ER modulators in vivo accurately. Physiologically relevant model systems that better predict tissue- or organ-level responses are needed. To address this need, we describe here a screening platform capable of quantitatively assessing ER modulators in 96 chemically isolated 3D cultures. These cultures are supported in wax-patterned paper scaffolds whose design has improved performance and throughput over previously described paper-based setups. To highlight the potential of paper-based cultures for toxicity screens, we measured the potency of known ER modulators with a luciferase-based reporter assay. We also quantified the proliferation and invasion of two ER+ cell lines in the presence of estradiol. Despite the inability of the current setup to better predict in vivo potencies of ER modulators than monolayer cultures, the results demonstrate the potential of this platform to support increasingly complex and physiologically relevant tissue-like structures for environmental chemical risk assessment.
Indexed as
Identifiers
What 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.