Incorporation of antigens from <i>Mannheimia haemolytica</i> culture supernatant, and recombinant bovine C3d into ISCOM matrix using neutravidin–biotin interaction
Bibliographic record
Abstract
The aim of this study was to incorporate antigens from Mannheimia haemolytica culture supernatant, and an immune modulatory molecule, recombinant bovine C3d (rBoC3d), into immune stimulating complexes (ISCOMs) using neutravidin-biotin interaction. Biotinylated ISCOM matrix was generated using a commercial kit. The biotinylated ISCOM matrix was incubated with neutravidin and then centrifuged in a sucrose density gradient. The rBoC3d was expressed as an in vivo biotinylated protein and with a c-Myc tag (EQKLISEEDL) engineered to facilitate detection. The neutravidin-coated ISCOM matrix was incubated with biotinylated antigens from M. haemolytica culture supernatants and rBoC3d. To test the association among the neutravidin-coated ISCOM matrix, biotinylated antigens and rBoC3d, an analytical sucrose density gradient (10-40%, w/w) was performed. The experimental formulations were run in SDS-PAGE gels under reducing conditions. For Western immunoblot analysis, polyclonal bovine antiavidin, monoclonal anti-c-Myc, monoclonal antileukotoxin, and anti-GS60 antibodies were used to detect the presence of neutravidin, rBoC3d, leukotoxin, and GS60 antigens, respectively. By taking advantage of the biotin-neutravidin interaction, not only leukotoxin but also the recombinant immunomodulatory molecule, rBoC3d, was incorporated into ISCOM particles.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".