Evidence mapPaperPMID 38526618Full record

ReviewDigestive diseases and sciences2024

Review of Animal Models of Colorectal Cancer in Different Carcinogenesis Pathways.

Xue Chen, Yirong Ding, Yun Yi, Zhishan Chen, Jiaping Fu, Ying Chang

Abstract readReview
PubMed Publisher
In one paragraph

Review in Digestive diseases and sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
  3. Active fraction of ground cherry (Biomedical reports · 2024
    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 3 institutions in 1 country.

Xue ChenDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Yirong DingDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Yun YiDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Zhishan ChenDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Jiaping FuDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Ying ChangDepartment of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China. changying@whu.edu.cn.
Wuhan University · CNZhongnan Hospital of Wuhan University · CNHubei Provincial Center for Disease Control and Prevention · CN

Funding

National Natural Science Foundation of China 82172983
6 · The paper itself

Abstract

Colorectal cancer (CRC) is a common malignant tumor of the gastrointestinal tract with increasing morbidity and mortality. Exploring the factors affecting colorectal carcinogenesis and controlling its occurrence at its root is as important as studying post-cancer treatment and management. Establishing ideal animal models of CRC is crucial, which can occur through various pathways, such as adenoma-carcinoma sequence, inflammation-induced carcinogenesis, serrated polyp pathway and de-novo pathway. This article aims to categorize the existing well-established CRC animal models based on different carcinogenesis pathways, and to describe their mechanisms, methods, advantages and limitations using domestic and international literature sources. This will provide suggestions for the selection of animal models in early-stage CRC research.

Indexed as

CarcinogenesisColorectal NeoplasmsDisease Models, AnimalAdenomaAnimalsHumansMiceAnimal modelsCarcinogenesis pathwaysColorectal cancer

Identifiers

PMID38526618
OpenAlexW4393144008

What Socratic 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 Socratic graph.