Evidence map›Paper›PMID 33659073›Full record

ArticleBiomedical optics express2021

Hyperspectral microscopy combined with DAPI staining for the identification of hepatic carcinoma cells.

Kunxing Liu, Sifan Lin, Siqi Zhu, Yao Chen, Hao Yin, Zhen Li, Zhenqiang Chen

Abstract read
In one paragraph

Article in Biomedical optics express, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. AI-Powered Spectral Imaging for Virtual Pathology Staining.Bioengineering (Basel, Switzerland) · 2025
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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

7 authors.

Kunxing LiuGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.
Sifan LinGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.
Siqi ZhuGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.
Yao ChenGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.
Hao YinGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.
Zhen LiGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.
Zhenqiang ChenGuangdong Provincial Engineering Research Center of Crystal and Laser Technology, Guangzhou 510632, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, the DAPI staining is firstly reported for use in the identification of hepatic carcinoma cells based on hyperspectral microscopy. Nuclei in cancer cells usually contain more aneuploidies than that in normal cells, leading to the change of DNA content. Here, we stain hepatic carcinoma tissues and normal hepatic tissues with 4',6-diamidino-2-phenylindole (DAPI) which is sensitive to the DNA content as a fluorochrome binds to DNA. Consequently, the difference in DNA content between hepatic carcinoma cells and normal hepatic cells can be identified by the fluorescent spectral characteristics. Harnessing the hyperspectral microscopy, we find that the fluorescent properties of these two kinds of cells are different not only in the intensity but also in the spectral shape. These properties are exploited to train a support vector machine (SVM) model for classifying cells. The results show that the sensitivity and specificity for the identification of 1000 hepatic carcinoma samples are 99.3% and 99.1%, respectively.

Identifiers

PMID33659073
PMCPMC7899502

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.