Tetracyclines tigecycline and doxycycline inhibit LPS-induced nitric oxide production by RAW 264.7 murine macrophages. (101.3)
Bibliographic record
Abstract
Abstract We previously reported that minocycline and doxycycline strongly suppress serum IgE and decrease IgE production by PBMC from patients with asthma (Smith-Norowitz et al, Ann Allergy, Asthma, & Immunol 2002; 89:172–9; Durkin et al, AAAAI Annual Meeting San Diego, Feb 23–27, 2007). We have now studied the effects of doxycycline and tigecycline on LPS-induced nitric oxide (NO) production by murine macrophages (RAW 264.7). Cell cultures were incubated for 1 – 5 days with or without LPS (200ng/mL, phenol extracted from Salmonella typhimurium) and doxycycline or tigecycline (1 – 10 μg/ml). Accumulation of nitrite, stable metabolite of NO, was determined using a modified colorimetric Griess reaction. Both doxycycline, and to a lesser extent tigecycline, consistently suppressed LPS-induced nitric oxide production in dose-dependent manner (doxycycline: 23–30%, tigecycline: 10–23%). The tetracycline mediated suppression of NO production by macrophages in culture is independent of antimicrobial activities, but the underlying mechanism of this effect remains to be elucidated. Since nitric oxide is an important mediator of inflammatory responses, our results suggest that the anti-inflammatory effects of tetracyclines may be utilized in the treatment of IgE mediated allergic disorders and may decrease exhaled breath nitric oxide, an emerging biomarker of allergic airway disease.
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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 | 0.001 |
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".