Evidence map›Paper›PMID 35630347›Full record

ReviewMicroorganisms2022

Microbiota Transplantation in an Antibiotic-Induced Bacterial Depletion Mouse Model: Reproducible Establishment, Analysis, and Application.

Lijun Shang, Jiayu Tu, Ziqi Dai, Xiangfang Zeng, Shiyan Qiao

Open access · goldAbstract readReview
In one paragraph

Review in Microorganisms, 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
0.6field-weighted citation impact, top 34% 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, 6 citations in OpenAlex.

  1. Frontiers in microbiology · 2026
    Article
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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 2 institutions in 1 country.

Lijun ShangState Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing 100193, China.
Jiayu TuState Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing 100193, China.
Ziqi DaiState Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing 100193, China.
Xiangfang ZengState Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing 100193, China.
Shiyan QiaoState Key Laboratory of Animal Nutrition, Ministry of Agriculture Feed Industry Centre, China Agricultural University, Beijing 100193, China.
China Agricultural University · CNFeed Research Institute · CN

Funding

Chongqing Rongchang Agricultural and Animal Husbandry High-tech Industry Research and Development Project cstc2019ngzx0019National Key Research and Development Program of China 32030105
6 · The paper itself

Abstract

The fecal bacteria transplantation (FMT) technique is indispensable when exploring the pathogenesis and potential treatments for microbiota-related diseases. For FMT clinical treatments, there are already systematic guidelines for donor selection, fecal bacterial separation, FMT frequency, and infusion methods. However, only a few studies have demonstrated the use of standardized FMT procedures for animal models used in theoretical research, creating difficulties for many new researchers in this field. In the present paper, we provide a brief overview of FMT and discuss its contribution to the current understanding of disease mechanisms that relate to microbiota. This protocol can be used to generate a commonly used FMT mouse model and provides a literature reference of customizable steps.

Indexed as

antibioticapplicationFMTmicrobiotaprotocol

Identifiers

PMID35630347
PMCPMC9146686
OpenAlexW4224577371

What OpenQuestion 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 OpenQuestion graph.