Functional differences exist in contribution of VH and VL from polyspecific IgM and monospecific IgG antibodies in antigen recognition and virus neutralization functions.
Bibliographic record
Abstract
Abstract We analyzed role of individual variable heavy (FdVH) and variable light (FdVL) domains in comparison with VH+VL pair (scFv) originating from a polyspecific bovine IgM, with an exceptionally long CDR3H (61 amino acids), and a monospecific IgG1 antibody in antigen (Ag) recognition and virus neutralization functions. To this end, recombinant FdVH, FdVL and scFv were constructed and expressed in Pichia pastoris from bovine polyspecific IgM and IgG1 encoding cDNA. The scFv1H12 showed polyspecific antigen recognition similar to parent IgM antibody with minor differences. Unlike variable light domain FdVL1H12, variable heavy domain FdVH1H12 recognized multiple antigens that differed from scFv1H12 and the parent IgM antibody. Nevertheless, role of FdVL1H12 in providing structural support to FdVH in antigen recognition is noted. By contrast, the individual FdVH073 and FdVL074, originating from induced BoHV-1 neutralizing IgG1 antibody, recognized target epitope on BoHV-1 relatively weakly when compared to VH+VL pair as scFv3-18L. Both VH and VL domains of induced IgG antibody are required to achieve BoHV-1 neutralization function. To conclude, there exist subtle functional differences in relative contribution of VH and VL from polyspecific IgM and monospecific IgG antibodies in antigen recognition and virus neutralization functions. [Supportd by NSERC Canada]
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".