Evidence map›Paper›PMID 38396004›Full record

ArticleNature communications2024

Virtual histological staining of unlabeled autopsy tissue.

Yuzhu Li, Nir Pillar, Jingxi Li, Tairan Liu, Di Wu, Songyu Sun, Guangdong Ma, Kevin de Haan, Luzhe Huang, Yijie Zhang and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing 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

35 citing papers in PubMed.

  1. Article
  2. Virtual Overlay Staining With Plasmonic Oligomer Metasurface.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Article
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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

16 authors.

Yuzhu Li *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Nir Pillar *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Jingxi Li *Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0001-6595-8680
Tairan LiuElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Di WuComputer Science Department, University of California, Los Angeles, CA, 90095, USA.
Songyu SunComputer Science Department, University of California, Los Angeles, CA, 90095, USA.
Guangdong MaElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Kevin de HaanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.ORCID 0000-0002-7802-3857
Luzhe HuangElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Yijie ZhangElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
Sepehr HamidiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
Anatoly UrismanDepartment of Pathology, University of California, San Francisco, CA, 94143, USA.ORCID 0000-0001-8364-5303
Tal Keidar HaranDepartment of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel.ORCID 0000-0003-1931-706X
William Dean WallaceDepartment of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA.ORCID 0000-0002-9850-9723
Jonathan E ZuckermanDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA.
Aydogan OzcanElectrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.ORCID 0000-0002-0717-683X

Funding

TRD3: Data Analytics and Intelligent Systems (AI-ML-DL-Visualization)P41EB032840 · NIBIB · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Griffith R. Harsh, Laura Marcu · 2022 to 2026
$6.7M
NIBIB NIH HHS P41 EB032840
6 · The paper itself

Abstract

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.

Indexed as

Neural Networks, ComputerEosine Yellowish-(YS)HematoxylinStaining and LabelingEosine Yellowish-(YS)Hematoxylin

Identifiers

PMID38396004
PMCPMC10891155

What OpenQuestion holds

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Registered trials

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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.