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Record W2613127712 · doi:10.1139/cjm-2017-0179

<i>Brucella melitensis</i> M5-90Δbp26 as a potential live vaccine that allows for the distinction between natural infection and immunization

2017· article· en· W2613127712 on OpenAlexvenueno aff
Tiansen Li, Zhixia Tong, Meiling Huang, Liyan Tang, Hui Zhang, Chuangfu Chen

Bibliographic record

VenueCanadian Journal of Microbiology · 2017
Typearticle
Languageen
FieldVeterinary
TopicBrucella: diagnosis, epidemiology, treatment
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsBrucellaVirulenceBrucella melitensisAttenuated vaccineBiologyVirologyImmunizationBrucellosisMicrobiologyMutantPathogenBrucellaceaeAntibodyImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Brucella is Gram-negative intracellular bacterial pathogen that infects humans and animals and contributes to great economic losses in developing countries. Presently, live attenuated Brucella vaccines (Brucella melitensis M5-90) are the most effective means of brucellosis control and prevention in animals. However, these vaccines have several drawbacks, such as an inability to distinguish between a natural infection and immunization and an association with abortions in pregnant animals. Therefore, this study constructed a Brucella M5-90Δbp26 mutant and evaluated its virulence. The survival of the M5-90Δbp26 mutant was attenuated in human placenta trophoblastic 8 cells (HPT-8 cells) and in BALB/c mice, with a high immunoprotectivity noted in mice. Furthermore, safety tests showed that the M5-90Δbp26 mutant was less virulent than the M5-90 vaccine strain. Additionally, an indirect enzyme-linked immunosorbent assay (ELISA) screening was shown to detect the presence of Brucella protein 26 (BP26) with high sensitivity, with M5-90Δbp26 inoculation accompanied with a lack of BP26 expression, and was further confirmed by western blotting. Together, the M5-90Δbp26 mutant and the indirect ELISA can be employed to distinguish vaccinated livestock from infected animals.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.111
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.043
GPT teacher head0.303
Teacher spread0.260 · 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 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

Citations21
Published2017
Admission routes1
Has abstractyes

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