Evidence map›Paper›PMID 38488831›Full record

ArticleeLife2024

Open-top Bessel beam two-photon light sheet microscopy for three-dimensional pathology.

Won Yeong Park, Jieun Yun, Jinho Shin, Byung Ho Oh, Gilsuk Yoon, Seung-Mo Hong, Ki Hean Kim

Open access · goldAbstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 18% 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

4 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Prototype-based AI triage for 3D pathology.bioRxiv : the preprint server for biology · 2026
    Article
  3. Article
  4. 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

7 authors at 4 institutions in 2 countries.

Won Yeong ParkDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Jieun YunDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Jinho ShinDepartment of Medicine, University of Ulsan College of Medicine, Seoul, Seoul, Republic of Korea.
Byung Ho OhDepartment of Dermatology, College of Medicine, Yonsei University, Seoul, Republic of Korea.
Gilsuk YoonDepartment of Pathology, School of Medicine, Kyungpook National University, Daegu, Republic of Korea.ORCID https://orcid.org/0000-0002-9941-024X
Seung-Mo HongDepartment of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Ki Hean KimDepartment of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.ORCID https://orcid.org/0000-0003-3410-7706
Pohang University of Science and Technology · KRUlsan College · KRKyungpook National University · KRYonsei University · KR

Funding

Korea Medical Device Development Fund 9991006749Korea Medical Device Development Fund KMDF_PR_20200901_0076National Research Foundation of Korea NRF-2020R1A2C3009309
6 · The paper itself

Abstract

Nondestructive pathology based on three-dimensional (3D) optical microscopy holds promise as a complement to traditional destructive hematoxylin and eosin (H&E) stained slide-based pathology by providing cellular information in high throughput manner. However, conventional techniques provided superficial information only due to shallow imaging depths. Herein, we developed open-top two-photon light sheet microscopy (OT-TP-LSM) for intraoperative 3D pathology. An extended depth of field two-photon excitation light sheet was generated by scanning a nondiffractive Bessel beam, and selective planar imaging was conducted with cameras at 400 frames/s max during the lateral translation of tissue specimens. Intrinsic second harmonic generation was collected for additional extracellular matrix (ECM) visualization. OT-TP-LSM was tested in various human cancer specimens including skin, pancreas, and prostate. High imaging depths were achieved owing to long excitation wavelengths and long wavelength fluorophores. 3D visualization of both cells and ECM enhanced the ability of cancer detection. Furthermore, an unsupervised deep learning network was employed for the style transfer of OT-TP-LSM images to virtual H&E images. The virtual H&E images exhibited comparable histological characteristics to real ones. OT-TP-LSM may have the potential for histopathological examination in surgical and biopsy applications by rapidly providing 3D information.

Indexed as

MicroscopyNeoplasmsEosine Yellowish-(YS)Fluorescent DyesHumansImaging, Three-DimensionalMaleSkinEosine Yellowish-(YS)Fluorescent Dyes3D imaging3D pathologybessel beam illuminationcancer biologyhumanmedicinenondestructive pathologytwo-photon light sheet microscopyunsupervised deep learning

Identifiers

PMID38488831
PMCPMC10942781
OpenAlexW4389172230

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.