Evidence map›Paper›PMID 42719358›Full record

ArticleDose-response : a publication of International Hormesis Society

Shape-Restricted Detection of Natural Product Hormesis: A Re-Analysis of Complex Botanical Mixtures.

Ying Jin, Cynthia Rider, Ruili Huang, Menghang Xia, Helene Langevin, Shanshan Zhao, Daniel Zilber

Abstract read
In one paragraph

Article in Dose-response : a publication of International Hormesis Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ying JinNational Institutes of Health, National Institute for Environmental Health Sciences, Durham, NC, USA.ORCID https://orcid.org/0000-0002-0866-1634
Cynthia RiderDivision of Translational Toxicology, National Institutes of Health, Durham, NC, USA.
Ruili HuangNational Institutes of Health, National Center for Advancing Translational Scencies, Bethesda, MD, USA.
Menghang XiaNational Institutes of Health, National Center for Advancing Translational Scencies, Bethesda, MD, USA.
Helene LangevinOsher Center for Integrative Health, University of Vermont, Burlington, VT, USA.
Shanshan ZhaoNational Institutes of Health, National Institute for Environmental Health Sciences, Durham, NC, USA.
Daniel ZilberNational Institutes of Health, National Institute for Environmental Health Sciences, Durham, NC, USA.ORCID https://orcid.org/0000-0002-4387-0271

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Botanical supplements are consumed to improve health despite the mystery of their mechanisms. One possible effect is hormesis, defined as a beneficial and stimulatory health effect at a low dose and detrimental effect at a high dose, or vice versa. Hormesis is usually discovered by manually examining the shape of dose-response curves, which is not scalable to the large number of chemical-assay combinations found in high-throughput screening (HTS) results. Methods: We proposed a semi-automatic statistical testing procedure to quickly evaluate large-scale dose-response data, accompanied by an R package Results: We applied this method to a data set of 4860 chemical-assay pairs and identified 561 (12%) dose-response curves showing statistically significant hormesis. Conclusion: Our discovery of hormetic curves is in line with estimates of hormetic behaviors in dose-response repositories like Tox21. Our proposed method has been effective in screening for hormesis from large-scale dose-response data, and the visualization method effectively facilitates the interpretation of test results.

Indexed as

botanicalhormesismixtureshape constrained regressionstatistical testing

Identifiers

PMID42719358
PMCPMC13554521

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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.