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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".