Doxycycline Reduces Lipopolysaccharide‐Induced Inflammatory Mediator Secretion in Macrophage and Ex Vivo Human Whole Blood Models
Bibliographic record
Abstract
BACKGROUND: Tetracyclines have been extensively used as adjuncts in the treatment of some forms of periodontitis. The aim of this study was to evaluate the capacity of doxycycline to influence the secretion of inflammatory mediators in macrophage and ex vivo human whole blood models stimulated with periodontopathogen lipopolysaccharides (LPS). METHODS: Monocyte-derived macrophages were treated with various concentrations of doxycycline prior to being stimulated with Aggregatibacter actinomycetemcomitans (previously Actinobacillus actinomycetemcomitans) LPS. The capacity of doxycycline to mediate the inflammatory response was also tested in an ex vivo whole blood model (whole blood isolated from periodontitis patients and healthy subjects) stimulated with Porphyromonas gingivalis LPS. The secretion of interleukin (IL)-1beta, -6, and -8 and tumor necrosis factor-alpha (TNF-alpha) in both models was assessed by enzyme-linked immunosorbent assays (ELISA). Changes in phosphorylation state of kinases induced by A. actinomycetemcomitans LPS and doxycycline in the macrophage model were characterized by a multiplex ELISA analysis. RESULTS: The secretion of IL-1beta and -8 and TNF-alpha by macrophages decreased significantly (P <0.05) when they were pretreated with 2 microM doxycycline, whereas a concentration of 10 microM was required to significantly reduce IL-6 secretion. Pretreatment of macrophages with 10 microM doxycycline prior to A. actinomycetemcomitans LPS stimulation resulted in a marked decrease in the phosphorylation of extracellular signal-regulated kinase (ERK) 1/2 (-76%). In the whole blood model, doxycycline, more particularly at 10 microM, was also a potent inhibitor of the proinflammatory cytokine response. CONCLUSION: These two models provided clear evidence that some of the clinically proven benefits of doxycycline may be related to its ability to regulate inflammatory mediator release by host cells.
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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 | 0.000 |
| 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".