Interleukin-4 Programmed Macrophages Suppress Colitis and Do Not Enhance Infectious-Colitis, Inflammation-Associated Colon Cancer or Airway Hypersensitivity
Bibliographic record
Abstract
The murine interleukin-4 treated macrophage (MIL4) exerts anti-inflammatory and pro-healing effects and has been shown to reduce the severity of chemical-induced colitis. Positing M(IL4) transfer as an anti-inflammatory therapy, the possibility of side-effects must be considered. Consequently, bone marrow-derived M(IL4)s were administered via intraperitoneal injection to mice concomitant with Citrobacter rodentium infection (infections colitis), azoxymethane/dextran sodium sulphate (AOM/DSS) treatment [a model of colorectal cancer (CRC)], or ovalbumin sensitization (airway inflammation). The impact of M(IL4) treatment on C. rodentium infectivity, colon histopathology, tumor number and size and tissue-specific inflammation was examined in these models. The anti-colitic effect of the M(IL4)s were confirmed in the di-nitrobenzene sulphonic acid model of colitis and the lumen-to-blood movement of 4kDa FITC-dextran and bacterial translocation to the spleen and liver was also improved by M(IL4) treatment. Analysis of the other models of disease, that represent comorbidities that can occur in human inflammatory bowel disease (IBD), revealed that M(IL4) treatment did not exaggerate the severity of any of the conditions. Rather, there was reduction in the size (but not number) of polyps in the colon of AOM/DSS-mice and reduced infectivity and inflammation in C. rodentium-infected mice in M(IL4)-treated mice. Thus, while any new therapy can have unforeseen side effects, our data confirm and extend the anti-colitic capacity of murine M(IL4)s and indicate that systemic delivery of one million M(IL4)s did not exaggerate disease in models of colonic or airways inflammation or colonic tumorigenesis.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".