Evidence map›Paper›PMID 35486595›Full record

ArticlePloS one2022

Identifying luminal and basal mammary cell specific genes and their expression patterns during pregnancy.

Zhan Dong Li, Xiangtian Yu, Zi Mei, Tao Zeng, Lei Chen, Xian Ling Xu, Hao Li, Tao Huang, Yu-Dong Cai

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. 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
0.1field-weighted citation impact, top 63% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

9 authors at 6 institutions in 1 country.

Zhan Dong LiCollege of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
Xiangtian YuShanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
Zi MeiShanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China.
Tao ZengBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Lei ChenCollege of Information Engineering, Shanghai Maritime University, Shanghai, China.
Xian Ling XuGuangdong AIB Polytechnic College, Guangzhou, China.
Hao LiCollege of Biological and Food Engineering, Jilin Engineering Normal University, Changchun, China.
Tao HuangBio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
Yu-Dong CaiSchool of Life Sciences, Shanghai University, Shanghai, China.ORCID 0000-0001-5664-7979
Shanghai Institute of Nutrition and Health · CNJilin Engineering Normal University · CNGuangdong Polytechnic Normal University · CNShanghai Jiao Tong University · CNShanghai Maritime University · CNShanghai University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mammary gland is present in all mammals and usually functions in producing milk to feed the young offspring. Mammogenesis refers to the growth and development of mammary gland, which begins at puberty and ends after lactation. Pregnancy is regulated by various cytokines, which further contributes to mammary gland development. Epithelial cells, including basal and luminal cells, are one of the major components of mammary gland cells. The development of basal and luminal cells has been observed to significantly differ at different stages. However, the underlying mechanisms for differences between basal and luminal cells have not been fully studied. To explore the mechanisms underlying the differentiation of mammary progenitors or their offspring into luminal and myoepithelial cells, the single-cell sequencing data on mammary epithelia cells of virgin and pregnant mouse was deeply investigated in this work. We evaluated features by using Monte Carlo feature selection and plotted the incremental feature selection curve with support vector machine or RIPPER to find the optimal gene features and rules that can divide epithelial cells into four clusters with different cell subtypes like basal and luminal cells and different phases like pregnancy and virginity. As representations, the feature genes Cldn7, Gjb6, Sparc, Cldn3, Cited1, Krt17, Spp1, Cldn4, Gjb2 and Cldn19 might play an important role in classifying the epithelial mammary cells. Notably, seven most important rules based on the combination of cell-specific and tissue-specific expressions of feature genes effectively classify the epithelial mammary cells in a quantitative and interpretable manner.

Indexed as

Mammary Glands, AnimalSexual MaturationAnimalsCell DifferentiationEpithelial CellsFemaleLactationMammalsMicePregnancy

Identifiers

PMID35486595
PMCPMC9053804
OpenAlexW4225165275

What Socratic holds

Textmetadata
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 Socratic graph.