Evidence map›Paper›PMID 25469113›Full record

ArticleJournal of biomedical research2014

Chemo/Dietary prevention of cancer: perspectives in China.

Chung S Yang, Qing Feng

Open access · hybridAbstract read
In one paragraph

Article in Journal of biomedical research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.9field-weighted citation impact, top 14% 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

8 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

2 authors at 2 institutions in 2 countries.

Chung S YangDepartment of Chemical Biology, Center for Cancer Prevention Research and Rutgers Cancer Institute of New Jersey, Rutgers University, Piscataway, NJ 08854-8020, USA.
Qing FengDepartment of Nutrition and Food Hygiene, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 210029, China.
Nanjing Medical University · CNRutgers, The State University of New Jersey · US

Funding

Translational Research Support CoreP30ES005022 · NIEHS · UNIV OF MED/DENT NJ-R W JOHNSON MED SCH · PI PANOS G. GEORGOPOULOS · 1988 to 2026
$47.4M
NIEHS NIH HHS P30 ES005022
6 · The paper itself

Abstract

Cancer is a major disease worldwide and different approaches are needed for its prevention. Previous laboratory and clinical studies suggest that cancer can be prevented by chemicals, including those from the diet. Furthermore, epidemiological studies have suggested that deficiencies in certain nutrients can increase the risk of some cancers. In this article on chemo/dietary prevention, examples will be given to illustrate the effectiveness of chemopreventive agents in the prevention of breast, colon and prostate cancers in high-risk populations and the possible side effects of these agents. The potential usefulness of dietary approaches in cancer prevention and the reasons for some of the failed trials will be discussed. Lessons learned from these studies can be used to design more relevant research projects and develop effective measures for cancer prevention in the future. The development of effective chemopreventive agents, the use of nutrient supplements in deficient or carcinogen-exposed populations, and the importance of cohort studies will be discussed in the context of the current socioeconomic situation in China. More discussions are needed on how we can influence society to pay more attention to cancer prevention research and measures.

Indexed as

cancer chemopreventiondietaryintervention

Identifiers

PMID25469113
PMCPMC4250523
OpenAlexW1596707835

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.