Evidence map›Paper›PMID 39772690›Full record

ArticleScience advances2025

Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.

Çağatay Işıl, Hatice Ceylan Koydemir, Merve Eryilmaz, Kevin de Haan, Nir Pillar, Koray Mentesoglu, Aras Firat Unal, Yair Rivenson, Sukantha Chandrasekaran, Omai B Garner and 1 more

Abstract read
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In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
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  3. Review
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  6. 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

11 authors.

Çağatay IşılElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0003-3367-1858
Hatice Ceylan KoydemirDepartment of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA.ORCID 0000-0002-8612-5167
Merve EryilmazElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0003-3034-7665
Kevin de HaanElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0002-7802-3857
Nir PillarElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0003-4979-1440
Koray MentesogluElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Aras Firat UnalElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
Yair RivensonElectrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.ORCID 0000-0003-1132-0715
Sukantha ChandrasekaranDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
Omai B GarnerDepartment 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.ORCID 0000-0002-0717-683X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained neural network that digitally transforms dark-field images of unstained bacteria into their Gram-stained equivalents matching bright-field image contrast. After a one-time training, the virtual Gram staining model processes an axial stack of dark-field microscopy images of label-free bacteria (never seen before) to rapidly generate Gram staining, bypassing several chemical steps involved in the conventional staining process. We demonstrated the success of virtual Gram staining on label-free bacteria samples containing

Indexed as

Deep LearningGentian VioletMicroscopyStaining and LabelingBacteriaEscherichia coliImage Processing, Computer-AssistedListeriaNeural Networks, ComputerPhenazinesGentian VioletGram's stainPhenazines

Identifiers

PMID39772690
PMCPMC11803577

What OpenQuestion holds

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LicenceCC BY-NC
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.