Impact of antibiotics on the microcirculation in local and systemic inflammation
Bibliographic record
Abstract
The main function of antibiotics is related to their capacity to eliminate a microorganism. In addition to the antimicrobial function of antibiotics, they are known to have anti-inflammatory and vasomodulatory effects on the microcirculation. The ability of non-antimicrobial derivatives of antibiotics to control inflammation illustrates the distinct anti-microbial and anti-inflammatory roles of antibiotics. In this review, we discuss the impact of antibiotics on leukocyte recruitment and the state of the microcirculation. Literature reporting the effect of antibiotics in non-infectious inflammatory conditions is reviewed as well as the studies demonstrating the anti-inflammatory effects of antibiotics in animal models of infection. In addition, the effect of the antibiotics on the immune system is summarized in this review, in order to postulate some mechanisms of action for the proand anti-inflammatory contribution of antibiotics. Literature reported the effect of antibiotics on the production of cytokines, chemotaxis and recruitment of leukocytes, production of reactive oxygen species, process of phagocytosis and autophagy, and apoptosis of leukocytes. Yet, all antibiotics may not necessarily exert an anti-inflammatory effect on the microcirculation. Thus, we suggest a model for spectrum of anti-inflammatory and vasomodulatory effects of antibiotics in the microcirculation of animals in local and systemic inflammation. Although the literature suggests the ability of antibiotics to modulate leukocyte recruitment and microperfusion, the process and the mechanism of action are not fully characterized. Studying this process will expand the knowledge base that is required for the selection of antibiotic treatment based on its anti-inflammatory functions, which might be particularly important for critically ill patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".