Evidence map›Paper›PMID 35498641›Full record

ArticleFrontiers in plant science2022

Deciphering Precise Gene Transcriptional Expression Using gwINTACT in Tomato.

Yiyang Chu, Jiachen Gong, Peiwen Wu, Ye Liu, Yinglin Du, Lili Ma, Daqi Fu, Hongliang Zhu, Guiqin Qu, Benzhong Zhu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 35% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

10 authors at 1 institution in 1 country.

Yiyang ChuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Jiachen GongCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Peiwen WuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Ye LiuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Yinglin DuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Lili MaCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Daqi FuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Hongliang ZhuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Guiqin QuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
Benzhong ZhuCollege of Food Science and Nutritional Engineering, China Agricultural University, Beijing, China.
China Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional gene transcription mainly occurs in the nucleus and has a significant role in plant physiology. The isolation of nuclei tagged in specific cell type (INTACT) technique provides an efficient and stable nucleus purification method to investigate the dynamic changes of nuclear gene transcriptional expression. However, the application of traditional INTACT in plants is still limited to seedlings or root cells because of severe chloroplast pollution. In this study, we proposed a newly designed and simplified INTACT based on mas-enhanced GFP (eGFP)-

Indexed as

gwINTACTnuclear RNAnuclei purificationtomatotranscriptional expression

Identifiers

PMID35498641
PMCPMC9048029
OpenAlexW4223999591

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.