Evidence map›Paper›PMID 39873507›Full record

ArticleMicrobiology spectrum2025

Exploring the growing phase forest musk deer (

Ruiguang Gong, Xiaocao Di, Xinxin Ping, Haodong Han, Bing Song, Shuhui Wang, Xianggui Dong, Zhan Jun Ren

Abstract read
In one paragraph

Article in Microbiology spectrum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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

8 authors.

Ruiguang GongCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.ORCID 0009-0000-5587-0749
Xiaocao DiCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.
Xinxin PingCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.
Haodong HanCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.
Bing SongCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.
Shuhui WangCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.
Xianggui DongCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.
Zhan Jun RenCollege of Animal Science and Technology, Northwest A&F University, Yangling, China.ORCID 0000-0002-4525-304X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The forest musk deer (FMD) farming industry is currently experiencing rapid growth, yet the dietary energy requirements for FMD remain unclear. Therefore, we explored the optimal dietary digestible energy (DE) for growing phase FMD by providing three diets with different DE levels and analyzing changes in gut microbiota. A total of 20 six-month-old FMD were used in a 62-day trial. Animals were fed either traditional feed (CON, 10.38 MJ/kg DE) or pelleted diets with low (L, 8.87 MJ/kg), medium (M, 10.38 MJ/kg), or high (H, 11.86 MJ/kg) DE levels. The results showed that feeding pelleted diets significantly reduced the incidence of diarrhea ( IMPORTANCE: This study underscores the significance of identifying the optimal dietary digestible energy (DE) for growing forest musk deer (FMD). Pelleted diets with a DE level of 11.86 MJ/kg enhanced growth performance, nutrient digestibility, and gut health, while reducing diarrhea and enriching beneficial gut bacteria, offering valuable insights for improving FMD farming practices.

Indexed as

Animal FeedDeerDietGastrointestinal MicrobiomeAnimalsBacteriaDigestionFecesForests16S rRNAdigestible energyfecal microbiotagrowth performanceMoschus berezovskii

Identifiers

PMID39873507
PMCPMC11878060

What OpenQuestion holds

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LicenceCC BY
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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.