Evidence map›Paper›PMID 38934693›Full record

ArticleJournal of food and drug analysis2024

Fluorescent carbon nanodot-based rapid cell nucleus staining and application for diagnosis.

Daiming Zhong, Huamin Yuan, Qiuyan Tian, Gongping Deng, Yanhong Ouyang, Yi Chen, Yang Wang, Zhiqin Yuan

Abstract readEvaluation Study
In one paragraph

Article in Journal of food and drug analysis, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

8 authors.

Daiming ZhongLaboratory Medicine Department, Hunan Prevention and Treatment Institute for Occupational Diseases, Changsha 410000, Hunan, China.
Huamin YuanLaboratory Medicine Department, Hunan Prevention and Treatment Institute for Occupational Diseases, Changsha 410000, Hunan, China.
Qiuyan TianLaboratory Medicine Department, Hunan Prevention and Treatment Institute for Occupational Diseases, Changsha 410000, Hunan, China.
Gongping DengDepartment of Emergency, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, Hainan, China.
Yanhong OuyangDepartment of Emergency, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, Hainan, China.
Yi ChenHunan Zhixiangweilai Biotechnology Co., Ltd, Changsha 410125, Hunan, China.
Yang WangDepartment of Emergency, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou 570311, Hainan, China.
Zhiqin YuanCollege of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cell nucleus status decides the activities of corresponding cells, making its rapid and effective staining important for revealing the actual condition of biological environment in life science and related fields. In this study, fast staining of cell nucleus is realized by fluorescent carbon nanodots (CDs). The staining mechanism is due to the positively charged CD surface-induced cell membrane penetration, which facilitates the CD-nucleus binding via electrostatic attraction. The size of cell nucleus is easily measured with fluorescence imaging technique. In addition, the CD-based cell nucleus stain is applied for discriminating the normal and cancer cells by determining the cell-to-nucleus ratio with fluorescence images.

Indexed as

CarbonCell NucleusStaining and LabelingFluorescenceFluorescent DyesHumansNanoparticlesOptical ImagingQuantum DotsCarbonFluorescent Dyes

Identifiers

PMID38934693
PMCPMC11210473

What OpenQuestion holds

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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.