Evidence map›Paper›PMID 39919237›Full record

ReviewEnvironmental toxicology and chemistry2025

Using digital pathology to standardize and automate histological evaluations of environmental samples.

Philip Tanabe, Daniel Schlenk, Kristy L Forsgren, Daniela M Pampanin

Abstract readReview
In one paragraph

Review in Environmental toxicology and chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Review
  2. Review
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

4 authors.

Philip TanabeNational Ocean Service, National Centers for Coastal Ocean Science, National Oceanic and Atmospheric Administration, Charleston, SC, United States.
Daniel SchlenkDepartment of Environmental Sciences, University of California, Riverside, Riverside, CA, United States.
Kristy L ForsgrenDepartment of Biological Science, California State University, Fullerton, Fullerton, CA, United States.
Daniela M PampaninDepartment of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Histological evaluations of tissues are commonly used in environmental monitoring studies to assess the health and fitness status of populations or even whole ecosystems. Although traditional histology can be cost-effective, there is a shortage of proficient histopathologists and results can often be subjective between operators, leading to variance. Digital pathology is a powerful diagnostic tool that has already significantly transformed research in human health but has rarely been applied to environmental studies. Digital analyses of whole slide images introduce possibilities of highly standardized histopathological evaluations, as well as the use of artificial intelligence for novel analyses. Furthermore, incorporation of digital pathology into environmental monitoring studies using standardized bioindicator species or groups such as bivalves and fish can greatly improve the accuracy, reproducibility, and efficiency of the studies. This review aims to introduce readers to digital pathology and how it can be applied to environmental studies. This includes guidelines for sample preparation, potential sources of error, and comparisons to traditional histopathological analyses.

Indexed as

Environmental MonitoringImage Processing, Computer-AssistedAnimalsHumansautomationdigital pathologyenvironmental monitoringhistopathologymachine learning

Identifiers

PMID39919237
PMCPMC11816309

What OpenQuestion 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 OpenQuestion graph.