The novel iron chelator, DIBI, attenuates inflammation and improves outcome in colon ascendens stent peritonitis-induced experimental sepsis
Bibliographic record
Abstract
BACKGROUND: Sepsis is the result of a dysregulated host immune response to an infection. An ideal therapy would target both the underlying infection and the dysregulated immune response. DIBI, a novel iron-binding polymer, was specifically developed as an antimicrobial agent and has also demonstrated in vivo anti-inflammatory properties. OBJECTIVE: This study aimed to further investigate the effects of DIBI with and without the antibiotic imipenem (IMI) in colon ascendens stent peritonitis (CASP)-induced experimental sepsis. METHODS: Vehicle, DIBI and/or IMI were administered in C57BL/6 mice after CASP surgery. Intestinal leukocyte activation and capillary perfusion was evaluated by intravital microscopy. Moreover, bacterial load in peritoneal lavage fluid and blood, and plasma cytokine levels were assessed. In a second series of experiments, surgery to repair the colon was performed at 5 hr and these mice were followed for long-term survival over 7 days. RESULTS: DIBI reduced leukocyte adhesion, improved capillary blood flow, and decreased key plasma cytokines levels. DIBI also improved survival of infected mice and greatly improved IMI efficacy. Survivors treated with IMI and DIBI were found to be free of systemic infection. CONCLUSIONS: DIBI has promising potential for sepsis treatment including its use as a sole or an adjunct therapeutic with antibiotics.
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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) | 0.001 | 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".