Treatment with <scp>IgM</scp> ‐enriched intravenous immunoglobulins enhances clearance of stroke‐associated bacterial lung infection
Bibliographic record
Abstract
Post-stroke infection is a common complication of stroke that is associated with poor outcome. We previously reported that stroke induces an ablation of multiple sub-populations of B cells and reduces levels of immunoglobulin M (IgM) antibody, which coincides with the development of spontaneous bacterial pneumonia. The loss of IgM after stroke could be an important determinant of infection susceptibility and highlights this pathway as a target for intervention. We treated mice with a replacement dose of IgM-enriched intravenous immunoglobulin (IgM-IVIg) prior to and 24 h after middle cerebral artery occlusion (MCAO) and allowed them to recover for 2- or 5-day post-surgery. Treatment with IgM-IVIg enhanced bacterial clearance from the lung after MCAO and improved lung pathology but did not impact brain infarct volume. IgM-IVIg treatment induced immunomodulatory effects systemically, including rescue of splenic plasma B cell numbers and endogenous mouse IgM and IgA circulating immunoglobulin concentrations that were reduced by MCAO. Treatment attenuated MCAO-induced elevation of selected pro-inflammatory cytokines in the lung. IgM-IVIg treatment did not increase the number of lung mononuclear phagocytes or directly modulate macrophage phagocytic capacity but enhanced phagocytosis of Staphylococcus aureus bioparticles in vitro. Low-dose IgM-IVIg contributes to increased clearance of spontaneous lung bacteria after MCAO likely via increasing availability of antibody in the lung to enhance opsonophagocytic activity. Immunomodulatory effects of IgM-IVIg treatment may also contribute to reduced levels of damage in the lung after MCAO. IgM-IVIg shows promise as an antibacterial and immunomodulatory agent to use in the treatment of post-stroke infection.
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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) | 0.000 | 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 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".