Optimizing steroid administration in murine models of <i>Citrobacter rodentium</i>-induced acute phase colitis
Bibliographic record
Abstract
Abstract This study used Citrobacter rodentium-infected C57BL/6 mice as a model of acute colitis to assess treatment with steroids prednisolone (pred) and dexamethasone (dex). Both germ-free (GF) and normal flora (NF) adult mice were used to determine an optimal steroid dosing strategy to mitigate intestinal inflammation caused by C. rodentium attachment/effacement lesions. Male and female mice (6-weeks of age) were orally inoculated with C. rodentium (1.0 × 108 CFU/100 μL) for two consecutive days. Five days post-infection, mice were gavaged with pred (1 mg/kg/d or 3 mg/kg/d), dex (0.6 mg/kg/d or 1.5 mg/kg/d) or controls for five days (GF n = 3 and NF n = 4 for each treatment). GF mice had longer colons (base of cecum to anus) than NF mice (7.1 ± 0.1 cm vs. 6.2 ± 0.2 cm; P < 0.001) and lighter total liver weight than NF mice (1.0 ± 0 g vs. 1.1 ± 0.2 g; P = 0.03). Colonic cytokines were quantified by ELISA. Steroid treatment decreased concentrations of colonic KC (CXCL1; P < 0.001) in NF mice; and high-dose dex decreased KC more effectively than low-dose dex (3207.5 ± 583.5 pg/g vs. 9217.6 ± 1375.4 pg/g; P = 0.019). Although C. rodentium increased colonic concentrations of KC (P < 0.001), MIP-2 (CXCL2; P = 0.19), TNF-α (P = 0.031), IL-22 (P = 0.018) and myeloperoxidase (P < 0.001) in NF mice, no differences were attributable to steroids or sex in GF and NF mice. Colonic MIP-2 (P < 0.001), IL-10 (P = 0.032), TNF-α (P < 0.001), IL-17 (P < 0.001) and IL-22 (P = 0.005) were higher in NF than GF mice, likely due to underdeveloped gut-associated lymphoid tissue in GF mice. In summary, high-dose dex (1.5 mg/kg/d) but not high-dose pred (3 mg/kg/d) mitigated production of the potent neutrophil chemoattractant KC in a C. rodentium induced model of acute-phase 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 | 0.000 | 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 | 0.001 |
| 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".