Differential activation of human and guinea pig complement by pentameric and hexameric IgM
Bibliographic record
Abstract
Human and mouse IgM can be polymerized as a hexamer in addition to a pentamer. Our previous work with mouse IgM measured activation of guinea pig complement by highly enriched preparations of hexamer and pentamer and showed that hexamer is >100-fold more active than pentamer. In this report pentamer and hexamer were compared for their capacity to activate complement in a homogeneic system, i.e. chimeric mouse V/human Cmu IgM pentamer and hexamer were assayed separately for their capacity to activate human (and guinea pig) complement. In both the homogeneic and the xenogeneic systems hexamer was more active than pentamer, but the magnitude of the difference between hexamer and pentamer depended on the complement source. Whereas chimeric hexamer activated guinea pig complement >100-fold more efficiently than did chimeric pentamer, this hexamer was only 4-13-fold more active than pentamer when assayed with human complement. Similarly, mouse hexamer, which was >100-fold more active than mouse pentamer with guinea pig complement, was only approximately 2-fold more active than mouse pentamer with human complement. Mouse hexameric and pentameric IgM were each approximately 20-fold more active with human complement than were the corresponding chimeric isoforms of IgM.
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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".