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Molecular Characterization of Pasterulla Multocida in Fattening Bovine

2024· article· en· W4396724614 on OpenAlexaboutno aff
Ahmed G. Radwan, Islam Zakria, Rania AboSakya, Faysal Arnaout, Abdelfattah Selim

Bibliographic record

VenueEgyptian Journal of Veterinary Science · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolism and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsPasteurella multocidaCharacterization (materials science)BiologyChemistryMaterials scienceNanotechnologyGenetics

Abstract

fetched live from OpenAlex

Pasteurella multocida infection has serious complications for both human and animal health. In bovines, it is the predominant cause of fatal pneumonia, with a mortality rate up to 100%, especially in severe undiagnosed cases. Therefore, this study was conducted for molecular identification and characterization of P. multocida in nasal swabs and pneumonic lung tissue in cattle calves less than one year suffered from pneumonia and respiratory disorders. The detection of P. multocida was higher in pneumonic lung tissue (40%) than in nasal swabs. Only 37 out of 109 collected samples were positive for P. multocida using bacteriological culture and those samples were confirmed by PCR targeting Kmt1 gene. The phylogenetic analysis of local P. multocida isolate revealed close relation with other P. multocida strains from bovine from Egypt, China, USA and Canada. Consequently, establishing a potent epidemiological surveillance program is necessary to decrease the spreading of disease and the economic losses among fattening calves.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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