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Record W2155682692 · doi:10.1186/1297-9716-45-54

From mouth to macrophage: mechanisms of innate immune subversion by Mycobacterium avium subsp. paratuberculosis

2014· review· en· W2155682692 on OpenAlexaff
Ryan J. Arsenault, Pekka Määttänen, Joanna Daigle, Andrew Potter, Philip Griebel, Scott Napper

Bibliographic record

VenueVeterinary Research · 2014
Typereview
Languageen
FieldMedicine
TopicMycobacterium research and diagnosis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsParatuberculosisBiologyInnate immune systemMycobacteriumImmune systemImmunologyMycobacterium avium subspecies paratuberculosisDiseaseMycobacterium avium subsp. paratuberculosisMicrobiologyVirologyBacteriaMedicineGenetics

Abstract

fetched live from OpenAlex

Johne's disease (JD) is a chronic enteric infection of cattle caused by Mycobacterium avium subsp. paratuberculosis (MAP). The high economic cost and potential zoonotic threat of JD have driven efforts to develop tools and approaches to effectively manage this disease within livestock herds. Efforts to control JD through traditional animal management practices are complicated by MAP's ability to cause long-term environmental contamination as well as difficulties associated with diagnosis of JD in the pre-clinical stages. As such, there is particular emphasis on the development of an effective vaccine. This is a daunting challenge, in large part due to MAP's ability to subvert protective host immune responses. Accordingly, there is a priority to understand MAP's interaction with the bovine host: this may inform rational targets and approaches for therapeutic intervention. Here we review the early host defenses encountered by MAP and the strategies employed by the pathogen to avert or subvert these responses, during the critical period between ingestion and the establishment of persistent infection in macrophages.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.113
GPT teacher head0.435
Teacher spread0.322 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations131
Published2014
Admission routes1
Has abstractyes

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