Evidence map›Paper›PMID 40096321›Full record

ArticleSensors (Basel, Switzerland)2025

Non-Invasive Monitoring and Differentiation of Aging Mice Treated with Goat Whey Powder by an Electronic Nose Coupled with Chemometric Methods.

Guilong Zhu, Yahe Yang, Fumei Zhang, Jia Wei, Xiaojing Tian, Lixia Liu, Zuolin Ma, Guoheng Zhang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Guilong ZhuSchool of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China.ORCID 0009-0001-9513-2457
Yahe YangGansu Engineering Research Center of Ecological Environment Intelligent Networking, College of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China.
Fumei ZhangGansu Tech Innovation Center of Animal Cell, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China.
Jia WeiSchool of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China.
Xiaojing TianSchool of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China.ORCID 0000-0002-8959-6984
Lixia LiuSchool of Life Sciences and Engineering, Northwest Minzu University, Lanzhou 730124, China.
Zuolin MaGansu Tech Innovation Center of Animal Cell, Biomedical Research Center, Northwest Minzu University, Lanzhou 730030, China.
Guoheng ZhangGansu Engineering Research Center of Ecological Environment Intelligent Networking, College of Electrical Engineering, Northwest Minzu University, Lanzhou 730030, China.

Funding

Fundamental Research Funds for the Central Universities 31920240106Gannan Research Institute of Yak Milk XBMU-2020-BC-30The Science and Technology Project of Lanzhou 2024-8-27
6 · The paper itself

Abstract

For the evaluation of food efficacy, in vitro experiments and cell and animal models are heavily relied on, with a need for quick and non-invasive monitoring methods. In this study, the fecal odor of aging mice supplemented with goat whey powder was obtained by an E-nose, and the correlation between odor information and the antioxidant indexes, serum antibody, cytokine, and intestinal bacteria were analyzed, aiming to establish a non-invasive method for monitoring and differentiating the effect of goat whey powder. As a result, the fecal odor differed with intervention groups and intervention time, and most of the sensor responses were significantly correlated with weight gain rate, SOD activity, and MDA content. For serum antibodies, cytokines, IL-2, and IL-6 were negatively correlated with the responses of sensor S7. A strong correlation was found between the E-nose sensor responses and the dominant intestinal bacteria. The E-nose could differentiate aging mice of different intervention times and intervention groups with canonical discriminate analysis (CDA). The effective predictive model was built by multiple linear regression (MLR) and a multilayer perceptron neural network (MLP) for SOD, MDA, and weight gain rate, with R

Indexed as

AgingChemometricsElectronic NoseWheyAnimalsAntioxidantsCytokinesFecesGoatsMiceOdorantsPowdersAntioxidantsCytokinesPowdersaging mice modelcanonical correlation analysiscanonical discriminant analysiselectronic nosegoat whey powder

Identifiers

PMID40096321
PMCPMC11902438

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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.