Evidence map›Paper›PMID 37138821›Full record

ArticlePeerJ2023

An improved method for preparing stained ground teeth sections.

Qizhong Qin, Yueheng Li, Yujian Zhou

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

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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

3 authors at 2 institutions in 1 country.

Qizhong Qin *Experimental Teaching and Management Center, Chongqing Medical University, Chongqing, Yuzhong district, China.
Yueheng Li *Stomatological Hospital of Chongqing Medical University; College of Stomatology, Chongqing Medical University; Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences; Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing Medical University, Chongqing, Yubei District, China.
Yujian ZhouExperimental Teaching and Management Center, Chongqing Medical University, Chongqing, Yuzhong district, China.
Chongqing Medical University · CNChongqing Emergency Medical Center · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: In oral histopathology teaching and research, there is a need for high-quality undemineralized tooth sections that are easy to handle, have controlled thickness, allow the observation of intact microstructures, and can be preserved for long periods of time. Methods: Teeth were collected under non-demineralizing conditions. Tooth sections (15-25 µm) were prepared using a diamond knife, then randomly divided into three groups: (1) stained with rosin, (2) stained with hematoxylin and eosin, or (3) not stained. The prepared tooth sections were evaluated by microscopy for clarity and microstructure visibility. Results: The use of a diamond knife in the sectioning and grinding process yielded high-quality ground sections of teeth. Rosin-stained ground sections allowed better identification of microstructures within the teeth, compared with unstained or hematoxylin and eosin-stained ground sections. Conclusion: The best results were obtained in the ground sections of teeth that were stained with rosin. Ground sections of teeth prepared using this staining method could be useful in oral histopathology teaching and research.

Indexed as

ToothTooth DiscolorationEosine Yellowish-(YS)HematoxylinHumansMicroscopyStaining and LabelingEosine Yellowish-(YS)HematoxylinGround sections of teethRosinUn-demineralization

Identifiers

PMID37138821
PMCPMC10150715
OpenAlexW4367316060

What OpenQuestion holds

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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.