Evidence map›Paper›PMID 35273689›Full record

ArticleAmerican journal of translational research2022

Effect of

Zi Wang, Huimin Hou, Haomin Zhang, Xingwu Duan, Lingling Li, Lingfeng Meng

Open access · greenAbstract read
In one paragraph

Article in American journal of translational research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

14 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. [MUC16: The Novel Target for Tumor Therapy].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2022
    Review
  13. Article
  14. 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

6 authors at 2 institutions in 1 country.

Zi WangDepartment of Dermatology, Dong Zhimen Hospital Affiliated to Beijing University of Chinese Medicine Beijing 100700, China.
Huimin HouDepartment of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences Beijing 100730, China.
Haomin ZhangDepartment of Dermatology, Dong Zhimen Hospital Affiliated to Beijing University of Chinese Medicine Beijing 100700, China.
Xingwu DuanDepartment of Dermatology, Dong Zhimen Hospital Affiliated to Beijing University of Chinese Medicine Beijing 100700, China.
Lingling LiDong Zhimen Hospital Affiliated to Beijing University of Chinese Medicine Beijing 100700, China.
Lingfeng MengDepartment of Urology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences Beijing 100730, China.
Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine · CNChinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectives

methodsMelanoma data were downloaded from the Cancer Genome Atlas and the International Cancer Genome Consortium database, and the "GenVisR" package was used to visualize the gene mutation types and frequencies. Intersections of the top 30 genes with the highest mutation frequencies were determined. Thereafter, we investigated the effects of

resultsTwenty-two genes with high mutation frequencies were identified in both datasets.

conclusion

Indexed as

Cutaneous melanomaimmuneMUC16prognosistumor mutation burden

Identifiers

PMID35273689
PMCPMC8902552
OpenAlexW4226330203

What OpenQuestion holds

Textmetadata
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.