Doxycycline impairs neutrophil migration to the airspaces of the lung in mice exposed to intratracheal lipopolysaccharide
Bibliographic record
Abstract
BACKGROUND: Tetracyclines are broad-spectrum antibiotics that are also used to induce gene expression using the reverse tetracycline transactivator / tetracycline operator system (rtTA/tetO system). The system assumes that tetracyclines have no effects on mammals. However, a number of studies suggest that tetracyclines may have powerful anti-inflammatory effects. We report that the tetracycline, doxycycline, inhibits neutrophil (PMN) influx into the lungs of mice treated with bacterial endotoxin (LPS). METHODS: Mice were challenged with intratracheal LPS in the presence or absence of doxycyline. bronchoalveolar lavage cell counts and differential, total bronchoalveolar lavage protein, lung homogenate caspase-3 and tissue imaging were used to assess lung injury. In addition, PMN chemotaxis was measured in vitro and syndecan-1 was measured in bronchoalveolar lavage fluid. RESULTS: The administration of doxycycline resulted in a significant decrease in the number of bronchoalveolar lavage PMNs in LPS-treated mice. Doxycycline had no effect on other markers of lung injury such as total bronchoalveolar lavage protein and whole lung caspase-3 activity. However, doxycycline resulted in a decrease in shed syndecan-1 in bronchoalveolar lavage fluid. CONCLUSION: We conclude that doxycycline has an important anti-inflammatory effect that can potentially confound the experiments in which the rtTA/tetO system is being used to study the immune response.
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 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) | 0.001 | 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 | 0.001 | 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".