Evidence map›Paper›PMID 25199550›Full record

ArticleLasers in medical science2015

Optical diagnosis of gallbladder cancers via two-photon excited fluorescence imaging of unstained histological sections.

Zhipeng Hong, Youting Chen, Jing Chen, Hong Chen, Yahao Xu, Xiaoqin Zhu, Shuangmu Zhuo, Zheng Shi, Jianxin Chen

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Article in Lasers in medical science, 2015. 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
–field-weighted citation impact, top 95% 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

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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, 7 citations in OpenAlex.

  1. Article
4 · The record

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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

9 authors at 3 institutions in 2 countries.

Zhipeng HongInstitute of Laser and Optoelectronics Technology, Fujian Provincial Key Laboratory for Photonics Technology, Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, 350007, People's Republic of China.
Youting Chen
Jing Chen
Hong Chen
Yahao Xu
Xiaoqin Zhu
Shuangmu Zhuo
Zheng Shi
Jianxin Chen
Fujian Normal University · CNFirst Affiliated Hospital of Fujian Medical University · CNBeckman Laser Institute and Medical Clinic · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Two-photon excited fluorescence (TPEF) microscopy, based on signal from cells, can provide detailed information on tissue architecture and cellular morphology in unstained histological sections to generate subcellular-resolution images from tissue directly. In this paper, we used TPEF microscopy to image microstructure of human normal gallbladder and three types of differentiated carcinomas in order to investigate the morphological changes of tissue structure, cell, cytoplasm, and nucleus without hematoxylin and eosin (H&E) staining. It displayed that TPEF microscopy can well image the stratified normal gallbladder tissue, including the mucosa, the muscularis, and the serosa. The typical cancer cell, characterized by cellular and nuclear pleomorphism, enlarged nuclei, and augmented nucleolus, can be identified in histological sections without H-E staining as well. The quantitative results showed that the areas of the nucleus and the nucleolus in three types of cancerous cells were all significantly greater than those in normal gallbladder columnar epithelial cells derived from TPEF microscopic images. The studies demonstrated that TPEF microscopy has the ability to characterize tissue structures and cell morphology of gallbladder cancers differentiated from a normal gallbladder in a manner similar to traditional histological analysis. As a novel tool, it has the potential for future retrospective studies of tumor staging and migration by utilizing histological section specimens without H-E staining.

Indexed as

Cell NucleolusGallbladderGallbladder NeoplasmsHumansMicroscopy, Fluorescence, MultiphotonRetrospective StudiesSingle-Blind Method

Identifiers

PMID25199550
OpenAlexW2043104049

What OpenQuestion holds

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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.