Prophylactic ciprofloxacin treatment prevented high mortality, and modified systemic and intestinal immune function in tumour-bearing rats receiving dose-intensive CPT-11 chemotherapy
Bibliographic record
Abstract
Infectious complications are a major cause of morbidity and mortality from dose-intensive cancer chemotherapy. In spite of the importance of intestinal bacteria translocation in these infections, information about the effect of high-dose chemotherapy on gut mucosal immunity is minimal. We studied prophylactic ciprofloxacin (Cipro) treatment on irinotecan (CPT-11) toxicity and host immunity in rats bearing Ward colon tumour. Cipro abolished chemotherapy-related mortality, which was 45% in animals that were not treated with Cipro. Although Cipro reduced body weight loss and muscle wasting, it was unable to prevent severe late-onset diarrhoea. Seven days after CPT-11, splenocytes were unable to proliferate (stimulation index=0.10+/-0.02) and produce proliferative and inflammatory cytokines (i.e., Interleukin (IL)-2, interferon-gamma (IFN-gamma), tumour necrosis factor-alpha (TNF-alpha) IL-1beta, IL-6) on mitogen stimulation in vitro (P<0.05 vs controls), whereas mesenteric lymph node (MLN) cells showed a hyper-proliferative response and a hyper-production of pro-inflammatory cytokines on mitogen stimulation. This suggests compartmentalised effects by CPT-11 chemotherapy on systemic and intestinal immunity. Cipro normalised the hyper-responsiveness of MLN cells, and in the spleen, it partially restored the proliferative response and normalised depressed production of IL-1beta and IL-6. Taken together, Cipro prevented infectious challenges associated with immune hypo-responsiveness in systemic immune compartments, and it may also alleviate excessive pro-inflammatory responses mediating local gut injury.
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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".