Bibliographic record
Abstract
The human immune system is highly complex and well-equipped to combat most bacteria. However, in some instances bacteria can cause life-threatening infections and the increase of antibiotic-resistant infections highlights a clear demand for the development of alternative treatment strategies. Activating the body’s own immune system with antibodies to clear infections has been deemed a promising strategy. To effectively employ this, a more in-depth understanding about the mechanisms by which antibodies effectively stimulate bacterial killing by the immune system is needed. A potent way to eradicate bacteria by antibodies, is to activate the human complement system as this leads to potent recognition by immune phagocytic cells, as well as direct killing of Gram-negative bacteria. Of the five human antibody isotypes, only IgG and IgM can activate complement. Although it has been established that IgM is more potent in anti-bacterial immunity than IgG, the mechanisms by which human IgM can achieve this remains elusive. Using monoclonal human IgM targeting several bacteria, we show that: 1) anti-bacterial IgM is generally better at activating the complement system than IgG; 2) although the sequential pathway in which IgG and IgM activate complement is similar, the activation mechanism differs; and 3) IgMs could provide broader protection than IgG against bacteria via their superior cross-reactivity. Altogether, the gained knowledge provides fundamental insights into the mechanisms of IgM in human immunity against bacteria and accelerates the development of antibody therapies against problematic infections.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".