Evidence map›Paper›PMID 35237979›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Use of Rabbit Models to Study Atherosclerosis.

Jianglin Fan, Manabu Niimi, Yajie Chen, Ritsuko Suzuki, Enqi Liu

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
4.7field-weighted citation impact, top 5% 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

5 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
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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

5 authors at 3 institutions in 2 countries.

Jianglin FanDepartment of Molecular Pathology, Faculty of Medicine, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Japan. jianglin@yamanashi.ac.jp.
Manabu NiimiDepartment of Molecular Pathology, Faculty of Medicine, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Japan.
Yajie ChenDepartment of Molecular Pathology, Faculty of Medicine, Interdisciplinary Graduate School of Medicine, University of Yamanashi, Chuo, Japan.
Ritsuko SuzukiAdmission Center, University of Yamanashi, Kofu, Japan.
Enqi LiuResearch Institute of Atherosclerotic Disease and Laboratory Animal Center, Xi'an Jiaotong University School of Medicine, Xi'an, China.
University of Yamanashi · JPWuyi University · CNXi'an Jiaotong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rabbits are a useful animal model for examining human hyperlipidemia and atherosclerosis because they have unique features of lipoprotein metabolism that are similar to those in humans. Feeding rabbits a cholesterol-rich diet is a simple means to induce experimental atherosclerosis. Indeed, cholesterol-fed rabbits were first applied to address the relationship between dietary cholesterol and atherosclerosis more than 100 years ago. However, the methods for investigating atherosclerosis using cholesterol-fed rabbits have not been well formulated. In this chapter, we attempt to provide readers with the essential methods to use cholesterol-fed rabbits in the examination of atherosclerosis from cholesterol diet preparation to lesion analysis. These protocols are compiled for both experienced and young researchers who intend to use rabbits for the investigation of lipoprotein metabolism and atherosclerosis.

Indexed as

AtherosclerosisHypercholesterolemiaAnimalsCholesterolCholesterol, DietaryDisease Models, AnimalRabbitsCholesterolCholesterol, DietaryAnimal modelsAtherosclerosisHypercholesterolemiaPlaquesRabbits

Identifiers

PMID35237979
OpenAlexW4214859896

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.