Evidence map›Paper›PMID 37508119›Full record

ArticleAnimals : an open access journal from MDPI2023

Fecal Proteome Profile in Dogs Suffering from Different Hepatobiliary Disorders and Comparison with Controls.

Matteo Cerquetella, Sara Mangiaterra, Francesco Pinnella, Giacomo Rossi, Andrea Marchegiani, Alessandra Gavazza, Evelina Serri, Alessandro Di Cerbo, Carlotta Marini, Daniela Cecconi and 3 more

Open access · goldAbstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.5field-weighted citation impact, top 27% 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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Article
  3. Understanding the diversity and roles of the canine gut microbiome.Journal of animal science and biotechnology · 2025
    Review
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

13 authors at 3 institutions in 1 country.

Matteo CerquetellaSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.ORCID 0000-0001-9471-0756
Sara MangiaterraFuturavet Veterinary Referral Center, 62029 Tolentino, MC, Italy.ORCID 0000-0003-1735-3569
Francesco PinnellaSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.
Giacomo RossiSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.ORCID 0000-0001-8617-5984
Andrea MarchegianiSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.ORCID 0000-0002-3629-0391
Alessandra GavazzaSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.ORCID 0000-0002-2070-8028
Evelina SerriSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.
Alessandro Di CerboSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.ORCID 0000-0001-8122-9170
Carlotta MariniSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.
Daniela CecconiDepartment of Biotechnology, University of Verona, Strada le Grazie 15, 37134 Verona, VR, Italy.ORCID 0000-0002-7314-8941
Daniela SorioCentre for Technological Platforms (CPT), University of Verona, Piazzale L.A. Scuro 10, 37134 Verona, VR, Italy.
Veronica MarchettiDepartment of Veterinary Sciences, University of Pisa, Via Livornese, San Piero a Grado, 56122 Pisa, PI, Italy.ORCID 0000-0001-6099-6223
Silvia VincenzettiSchool of Biosciences and Veterinary Medicine, University of Camerino, Via Circonvallazione 93/95, 62024 Matelica, MC, Italy.ORCID 0000-0002-6386-5437
Università di Camerino · ITUniversity of Verona · ITUniversity of Pisa · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present study, the fecal proteomes of clinically healthy dogs (HD = n. 10), of dogs showing clinical, ultrasonographic, and/or laboratory evidence of different hepatobiliary dysfunction (DHD = n. 10), and of dogs suffering from chronic hepatitis (CHD = n. 10) were investigated with an Ultimate 3000 nanoUPLC system, coupled to an Orbitrap Fusion Lumos Tribrid mass spectrometer. Fifty-two different proteins of canine origin were identified qualitatively in the three study groups, and quantitative differences were found in 55 proteins when comparing groups. Quantitatively, a total of 41 and 36 proteins were found differentially abundant in the DHD and CHD groups compared to the control HD, and 38 proteins resulted dysregulated in the CHD group as compared to the DHD group. Among the various proteins, differently abundant fecal fibronectin and haptoglobin were more present in the feces of healthy and DHD dogs than in chronic ones, leading us to hypothesize its possible diagnostic/monitoring role in canine chronic hepatitis. On the other hand, the trefoil factor 2 was increased in DHD dogs. Our results show that the analysis of the fecal proteome is a very promising field of study, and in the case of dogs suffering from different hepatobiliary disorders, it was able to highlight both qualitative and quantitative differences among the three groups included. Results need to be confirmed with western blotting and in further studies.

Indexed as

biomarkerdiagnosisdogfecal proteomicsliver diseasemonitoring

Identifiers

PMID37508119
PMCPMC10376375
OpenAlexW4384825505

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.