Novel Solutions for Vaccines and Diagnostics To Combat Brucellosis
Bibliographic record
Abstract
Abstract Brucellosis is diagnosed by detection of antibodies in the blood of animals and humans that are specific for two carbohydrate antigens, termed A and M, which are present concurrently in a single cell wall O-polysaccharide. Animal brucellosis vaccines contain these antigenic determinants, and consequently infected and vaccinated animals cannot be differentiated as both groups produce A and M specific antibodies. We hypothesized that chemical synthesis of a pure A vaccine would offer unique identification of infected animals by a synthetic M diagnostic antigen that would not react with antibodies generated by this vaccine. Two forms of the A antigen, a hexasaccharide and a heptasaccharide conjugated to tetanus toxoid via reducing and nonreducing terminal sugars, were synthesized and used as lead vaccine candidates. Mouse antibody profiles to these immunogens showed that to avoid reaction with diagnostic M antigen it was essential to maximize the induction of anti-A antibodies that bind internal oligosaccharide sequences and minimize production of antibodies directed toward the terminal nonreducing monosaccharide. This objective was achieved by conjugation of Brucella O-polysaccharide to tetanus toxoid via its periodate oxidized terminal nonreducing monosaccharide, thereby destroying terminal epitopes and focusing the antibody response on internal A epitopes. This establishes the method to resolve the decades-long challenge of how to create effective brucellosis vaccines without compromising diagnosis of infected animals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".