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Record W2969671448 · doi:10.1128/aem.01359-19

Development of Bacterial Therapeutics against the Bovine Respiratory Pathogen Mannheimia haemolytica

2019· article· en· W2969671448 on OpenAlexafffund
Samat Amat, Edouard Timsit, Danica Baines, Jay Yanke, Trevor W. Alexander

Bibliographic record

VenueApplied and Environmental Microbiology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicMicrobial infections and disease research
Canadian institutionsUniversity of CalgaryAlberta Health ServicesAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaAlberta Livestock and Meat AgencyGovernment of CanadaEuropean Food Safety Authority
KeywordsBovine respiratory diseaseMicrobiologyBiologyPathogenBacteriaFeedlotVirologyAnimal science

Abstract

fetched live from OpenAlex

Bovine respiratory disease (BRD) is a significant animal health issue impacting the beef industry. Current BRD prevention strategies rely mainly on metaphylactic use of antimicrobials when cattle enter feedlots. However, a recent increase in BRD-associated bacterial pathogens that are resistant to metaphylactic antimicrobials highlights a pressing need for the development of novel mitigation strategies. Based upon previous research showing the importance of respiratory commensal bacteria in protecting against bronchopneumonia, this study aimed to develop bacterial therapeutics that could be used to mitigate the BRD pathogen Mannheimia haemolytica . Bacteria isolated from the respiratory tracts of healthy cattle were characterized for their inhibitory, adhesive, and immunomodulatory properties. In total, 6 strains were identified as having the best properties for use as intranasal therapeutics to inhibit M. haemolytica . If successful in vivo , these strains offer an alternative to metaphylactic antimicrobial use in feedlot cattle for mitigating BRD.

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 categoriesInsufficient payload (model declined to judge)
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 score0.516
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.013
GPT teacher head0.215
Teacher spread0.202 · 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.

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

Citations53
Published2019
Admission routes2
Has abstractyes

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