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Record W2277262792 · doi:10.1099/vir.0.000206

Assessment of neutralizing and non-neutralizing antibody responses against Porcine circovirus 2 in vaccinated and non-vaccinated farmed pigs

2015· article· en· W2277262792 on OpenAlexaff
Cristina Solís Worsfold, Rkia Dardari, Sampson Law, Michael Eschbaumer, Narges Nourozieh, Frank Marshall, Markus Czub

Bibliographic record

VenueJournal of General Virology · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPorcine circovirusVirologyVaccinationBiologyAntibodyCircovirusNeutralizing antibodyVirusImmune systemImmunityCapsidImmunology

Abstract

fetched live from OpenAlex

Vaccination is the most efficacious procedure to curtail Porcine circovirus 2 (PCV2)-associated diseases (PCVAD). Experimental studies indicate that PCV2 vaccine-induced virus-neutralizing antibodies play a major role in protection from PCVAD. However, the immune response to PCV2 vaccination of pigs on farms is less clear. Analysing groups of age-matched vaccinated and non-vaccinated farmed pigs, we found significantly increased levels of virus-neutralizing antibodies only in vaccinated pigs belonging to the age group with the highest risk for developing PCVAD. Serum levels of PCV2 genomes were not different between corresponding age groups. Levels of antibodies directed against a linear peptide from the PCV2 capsid protein correlated with those of virus-neutralizing antibodies and reached the highest levels in older, non-vaccinated animals, pointing towards an intense interaction between PCV2-infected cells and the immune system. In conclusion, current PCV2 vaccines are in need of improvement to induce stronger and more rapid immunity to prevent PCV2 infection.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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

Citations11
Published2015
Admission routes1
Has abstractyes

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