Evidence map›Paper›PMID 40290137›Full record

ArticleResearch (Washington, D.C.)2025

Alanine Derived from

Fanlin Kong, Shuo Wang, Yijia Zhang, Chen Li, Dongwen Dai, Yajing Wang, Zhijun Cao, Hongjian Yang, Shengli Li, Wei Wang

Abstract read
In one paragraph

Article in Research (Washington, D.C.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. 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

10 authors.

Fanlin KongState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.ORCID https://orcid.org/0000-0001-8099-0398
Shuo WangState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Yijia ZhangLaboratory of Animal Neurobiology, Department of Basic Veterinary Medicine, College of Veterinary Medicine, Nanjing Agricultural University, Nanjing 210095, China.
Chen LiDepartment of Animal Nutrition and Feed Science, College of Animal Science, Xinjiang Agricultural University, Urumqi 830052, China.
Dongwen DaiDepartment of Animal Nutrition and Feed Science, College of Animal Science and Technology, Ningxia University, Yinchuan 750021, China.
Yajing WangState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Zhijun CaoState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Hongjian YangState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Shengli LiState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.
Wei WangState Key Laboratory of Animal Nutrition and Feeding, Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China.ORCID https://orcid.org/0009-0005-2002-5074

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Peripartum dairy cows commonly experience energy metabolism disorders, which lead to passive culling of postpartum cows and a decrease in milk quality. By using ketosis peripartum dairy cows as a model, this study aims to elucidate the metabolic mechanism of peripartum cows and provide a novel way for managing energy metabolic disorders. From a cohort of 211 cows, we integrated multi-omics data (metagenomics, metabolomics, and transcriptomics) to identify key microbes and then utilized an in vitro rumen fermentation simulation system and ketogenic hepatic cells to validate the potential mechanisms and the effects of postbiotics derived from key microbes. Postpartum cows with metabolic disorders compensate for glucose deficiency through mobilizing muscle proteins, which leads to marked decreases in milk protein content. Concurrently, these cows experience rumen microbiota disturbance, with marked decreases in the concentrations of volatile fatty acids and microbial protein, and the deficiency of alanine (Ala) in microbial protein is correlated with the metabolic disorder phenotype. Metagenomic binning and in vitro fermentation assays reveal that

Identifiers

PMID40290137
PMCPMC12022398

What Socratic 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 Socratic graph.