Evidence map›Paper›PMID 37367497›Full record

ArticleJournal of xenobiotics2023

Development of a Dynamic Network Model to Identify Temporal Patterns of Structural Malformations in Zebrafish Embryos Exposed to a Model Toxicant, Tris(4-chlorophenyl)methanol.

Ashley V Schwartz, Karilyn E Sant, Uduak Z George

Open access · goldAbstract read
In one paragraph

Article in Journal of xenobiotics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 32% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Integrating network analysis and machine learning to elucidate chemical-induced pancreatic toxicity in zebrafish embryos.Toxicological sciences : an official journal of the Society of Toxicology · 2025
    Article
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

3 authors at 1 institution in 1 country.

Ashley V SchwartzComputational Science Research Center, San Diego State University, San Diego, CA 92182, USA.ORCID 0000-0003-2199-1352
Karilyn E SantSchool of Public Health, Division of Environmental Health, San Diego State University, San Diego, CA 92182, USA.ORCID 0000-0001-8565-2072
Uduak Z GeorgeComputational Science Research Center, San Diego State University, San Diego, CA 92182, USA.ORCID 0000-0003-2208-3671
San Diego State University · US

Funding

Integrating Computational and Experimental Models to Predict Toxicity of the PancreasR21DK134931 · NIDDK · SAN DIEGO STATE UNIVERSITY · PI GEORGE, UDUAK ZENAS · 2023 to 2025
$540k
Elucidating the role of PPAR signaling in pancreatic organogenesis and juvenile metabolic syndromeK01ES031640 · NIEHS · SAN DIEGO STATE UNIVERSITY · PI SANT, KARILYN ELIZABETH · 2020 to 2022
$502k
NIDDK NIH HHS R21 DK134931NIDDK NIH HHS R21DK134931NIEHS NIH HHS K01 ES031640NIEHS NIH HHS K01ES031640
6 · The paper itself

Abstract

Embryogenesis is a well-coordinated process relying on precise cues and environmental signals that direct spatiotemporal embryonic patterning. Quite often, when one error in this process occurs, others tend to co-occur. We posit that investigating the co-occurrence of these abnormalities over time would yield additional information about the mode of toxicity for chemicals. Here, we use the environmental contaminant tris(4-chlorophenyl)methanol (TCPMOH) as a model toxicant to assess the relationship between exposures and co-occurrence of developmental abnormalities in zebrafish embryos. We propose a dynamic network modeling approach to study the co-occurrence of abnormalities, including pericardial edema, yolk sac edema, cranial malformation, spinal deformity, delayed/failed swim bladder inflation, and mortality induced by TCPMOH exposure. TCPMOH-exposed samples revealed increased abnormality co-occurrence when compared to controls. The abnormalities were represented as nodes in the dynamic network model. Abnormalities with high co-occurrence over time were identified using network centrality scores. We found that the temporal patterns of abnormality co-occurrence varied between exposure groups. In particular, the high TCPMOH exposure group experienced abnormality co-occurrence earlier than the low exposure group. The network model also revealed that pericardial and yolk sac edema are the most common critical nodes among all TCPMOH exposure levels, preceding further abnormalities. Overall, this study introduces a dynamic network model as a tool for assessing developmental toxicology, integrating structural and temporal features with a concentration response.

Indexed as

dynamic network modelembryonic developmentembryonic morphologytoxicantzebrafish

Identifiers

PMID37367497
PMCPMC10301205
OpenAlexW4380987627

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.