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Record W2897766844 · doi:10.1111/asj.13093

Identification and frequency of the associated genes with virulence and antibiotic resistance of <i>Escherichia coli</i> isolated from cow's milk presenting mastitis pathology

2018· article· en· W2897766844 on OpenAlexaff
Hossein Jamali, Kateryna Krylova, Mohammed Aïder

Bibliographic record

VenueAnimal Science Journal · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsVirulenceEscherichia coliMastitisBiologyMicrobiologyTetracyclineAntibiotic resistanceAntimicrobialIntegronStreptomycinPhylogenetic treeGeneAntibioticsGenetics

Abstract

fetched live from OpenAlex

Antimicrobial resistance, genotype, and virulence gene content of Escherichia coli isolated from bovine clinical mastitis in Tehran Province, Iran, were studied. Two hundred seven (207) milk samples from individual cows presenting mastitis symptoms collected from different dairy farms were used to determine the presence of specific genes of E. coli responsible for this pathology. Multiplex PCR was used to differentiate E. coli isolates into different phylogenetic groups/subgroups and to detect their virulence and involved resistance genes. All the isolated strains were tested for the susceptibility to 21 antimicrobial agents. The results showed that E. coli was detected in 42 (20.3%) samples and 69% of them belonged to the phylogenetic groups A and B1. The phylogenetic subgroup A1 (31%) and subgroup B1 (28.6%) demonstrated the highest prevalence of virulence genes (f17c-A, and eae (n = 6), f17b-A, and f17d-A (n = 5), afaD-8, afaE-8, aucD, and bfpA (n = 4), clpG and VT (n = 2), and LT and ST genes (n = 1)). The highest antimicrobial resistance was observed for tetracycline (45.2%) followed by streptomycin (26.2%). The antimicrobial resistance genes tetB (31%), tetA (28.6%), and aadA (26.2%) were the most prevalent. Moreover, integron class 1 and 2 were found in 24 (57.1%) and 8 (19%) of the E. coli isolates.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.676

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.223
Teacher spread0.216 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations22
Published2018
Admission routes1
Has abstractyes

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