Evidence map›Paper›PMID 42058177›Full record

ArticleFrontiers in cellular and infection microbiology2026

A newly identified sequence type: genomic and pathogenic profiling of the highly virulent

Tian Wang, Xian Deng, Maolin Xu, Zimeng Zhu, Yuxin Liu, Herman W Barkema, Eduardo R Cobo, John P Kastelic, Jian Gao, Bo Han

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PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in cellular and infection microbiology, 2026. 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

10 authors.

Tian WangCollege of Veterinary Medicine, China Agricultural University, Beijing, China.
Xian DengCollege of Veterinary Medicine, China Agricultural University, Beijing, China.
Maolin XuCollege of Veterinary Medicine, China Agricultural University, Beijing, China.
Zimeng ZhuCollege of Veterinary Medicine, China Agricultural University, Beijing, China.
Yuxin LiuCollege of Veterinary Medicine, China Agricultural University, Beijing, China.
Herman W BarkemaFaculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.
Eduardo R CoboFaculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.
John P KastelicFaculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada.
Jian GaoCollege of Veterinary Medicine, China Agricultural University, Beijing, China.
Bo HanCollege of Veterinary Medicine, China Agricultural University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Bovine mastitis causes substantial economic losses worldwide. Methods: From 135 Results: The Conclusion: Phylogenetically, ST3475 belongs to clonal complex CC2 but represents a distinct, putative bovine-adapted lineage separate from major human clone CC1. Although currently susceptible to last-resort antimicrobials like carbapenems, carriage of high-risk resistance genes highlights livestock as a potential reservoir for clinically relevant resistance determinants. This study reveals ST3475 as a novel, virulent, and putative bovine-adapted lineage, underscoring the importance of One Health surveillance.

Indexed as

Acinetobacter baumanniiAcinetobacter InfectionsMastitis, BovineAnimalsAnti-Bacterial AgentsBacterial AdhesionBiofilmsCattleChinaFemaleGenome, BacterialGenomicsMicrobial Sensitivity TestsPlasmidsVirulenceVirulence FactorsAnti-Bacterial AgentsVirulence FactorsAcinetobacter baumannii ST3475antimicrobial resistancebovine mastitispathogenesiswhole genome sequencing

Identifiers

PMID42058177
PMCPMC13121252

What OpenQuestion holds

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Registered trials

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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.