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Record W3161944459 · doi:10.3168/jds.2020-20042

Virulence profiles of Klebsiella pneumoniae isolated from 2 large dairy farms in China

2021· article· en· W3161944459 on OpenAlexaff
Jia Cheng, Man Zhou, Diego B. Nóbrega, Zhijun Cao, Jingyue Yang, Chunyan Zhu, Bo Han, Jian Gao

Bibliographic record

VenueJournal of Dairy Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsVirulenceKlebsiella pneumoniaeUdderBiologyMicrobiologyAerobactinMastitisVeterinary medicinePolymerase chain reactionPathogenGenetic diversityEnterobacteriaceaeGeneEscherichia coliGeneticsMedicinePopulation

Abstract

fetched live from OpenAlex

We recently reported on the diversity of Klebsiella pneumoniae isolated from dairy herds in China. In our previous work, isolates from subclinical mastitis (SCM) had lower indices of diversity when compared with bacteria from other sources, possibly due to a contagious-like spread of udder adapted strains. Here we explored the virulence profile and capsular types of K. pneumoniae isolated from different sources on 2 dairy farms in China. Our overarching goal was to gain insights on the role of virulence genes toward the severity of mastitis caused by K. pneumoniae. A total of 1,484 samples were collected from clinical mastitis (CM; n = 355), SCM (n = 561), bulk tank milk (BTM; n = 130), and environmental and extramammary (EE) sites (n = 438). From those, 431 K. pneumoniae isolates were obtained, including 129, 77, 66, and 159 isolates from CM, SCM, BTM, and EE samples, respectively. Polymerase chain reactions were used to determine the capsular types and to detect potential virulence genes in all isolates. No significant farm effects were observed when comparing the distribution of most virulence genes in K. pneumoniae isolated from each source. K57 was the most prevalent capsular type in K. pneumoniae from all sources, but with increased detection rate in isolates from CM. entB, kfu, fimH1, mrkD, and β-d-lacZ were frequently detected in K. pneumoniae from all sources. β-d-lacZ, entB, and ituA were more prevalent in isolates from CM, whereas kfu, allS, and nif were more frequently detected in isolates from SCM. ybtS, aerobactin, and rpmA had increased prevalence in K. pneumoniae from BTM when compared with bacteria from other sources. No association was detected between virulence genes and the severity of CM. K57 and the nif gene had the highest discriminatory power to classify isolates from CM and SCM, respectively. Based on our findings, it is likely that K57 is the dominant capsular type in K. pneumoniae causing CM in large Chinese dairy herds. Likewise, we demonstrated that β-d-lacZ is disseminated in K. pneumoniae isolated from large Chinese dairy farms, irrespectively of the source of bacteria. Our results also suggest a low contribution of the virulence profile of K. pneumoniae toward CM severity. Finally, the role of nif in increasing the adaptability to the udder and promoting a contagious-like spread of K. pneumoniae warrants further investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.251
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations24
Published2021
Admission routes1
Has abstractyes

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