Iron Chelation in Murine Models of Systemic Inflammation Induced by Gram-Positive and Gram-Negative Toxins
Bibliographic record
Abstract
Iron is an essential element for various physiological processes, but its levels must remain tightly regulated to avoid cellular damage. Similarly, iron plays a dual role in systemic inflammation, such as with sepsis. Leukocytes utilize iron to produce reactive oxygen species (ROS) to kill bacteria, but pathologically increased iron-catalyzed ROS production in sepsis can lead to damage of host cells, multi-organ failure and death. Temporary reduction in bioavailable iron represents a potential therapeutic target in sepsis. This study investigates the effect of the novel iron chelator, DIBI, in murine models of systemic (hyper-)inflammation: C57BL/6 mice were challenged with toxins from Gram-positive (Staphylococcus aureus: lipoteichoic acid, peptidoglycan) and Gram-negative bacteria (Escherichia coli and Klebsiella pneumoniae: lipopolysaccharide). Intravital microscopy (IVM) was performed to assess immune cell activation and its impact on microvascular blood flow in vivo in the microcirculation of the gut. Plasma inflammatory mediators were measured via multiplex assay, and morphologic change in intestinal tissue was evaluated. DIBI treatment decreased leukocyte (hyper-)activation induced by Gram-positive and Gram-negative toxins. In some cases, it preserved capillary perfusion, reduced plasma inflammatory markers and attenuated tissue damage. These findings support the utility of DIBI as a novel treatment for systemic inflammation, e.g., sepsis.
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) | 0.000 | 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".