Effects of Azithromycin on Behavior, Pathologic Signs, and Changes in Cytokines, Chemokines, and Neutrophil Migration in C57BL/6 Mice Exposed to Dextran Sulfate Sodium
Bibliographic record
Abstract
Here we characterized the murine dextran sulfate sodium (DSS) model of acute colitis. Specifically, we evaluated azithromycin and metronidazole treatment regimens to assess their effects on animal wellbeing, pathologic changes, barrier function, cytokine and chemokine profiles, and neutrophil migration in colon tissue. Azithromycin treatment significantly reduced the severity of colitis, as assessed through body weight change, water consumption, macroscopic lesions, and animal behaviors (activity level, climbing, and grooming), but did not alter food consumption or feeding behavior. Mucosal barrier function (evaluated by using FITC-labeled dextran) was decreased after DSS exposure; azithromycin did not significantly alter barrier function in mice with colitis, whereas metronidazole exacerbated the colitis-related deficit in barrier function. In addition, metronidazole appeared to exacerbate disease as assessed through water consumption and animal behaviors (overall activity, climbing, grooming, and drinking) but had no effect on weight loss, macroscopic lesions, or eating behavior. Pathologic changes were typical for DSS treatment. Antibiotic treatment resulted in reduced levels of proinflammatory cytokines and chemokines and decreased neutrophil adhesion and emigration in DSS-exposed mice. The results highlight the importance of clinical and behavioral assessments in addition to laboratory evaluation as tools to evaluate animal welfare and therapeutic efficacy in disease models. Data from this study suggest that azithromycin may convey some benefits in the mouse DSS colitis model through modulation of the immune response, including neutrophil migration into tissues, whereas metronidazole may exacerbate colitis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".