Gut Decontamination Prevents Bronchoscopy-Induced Bacterial Translocation
Bibliographic record
Abstract
BACKGROUND: Selective gut decontamination is suggested to suppress the gram-negative bacterial overgrowth in the intestine and consequently to reduce bacterial translocation. OBJECTIVE: The purpose of the present study is to examine the effects of gut decontamination on bronchoscopy-induced bacterial translocation, and intestinal mucosal injury. METHODS: Forty-five rats were assigned into three groups. Group 1 served as control (n=15). Group 2 (n=15) and group 3 (n=15) underwent bronchoscopy. In addition, group 3 underwent gut decontamination. Gut decontamination was performed two days prior to bronchoscopy with erythromycin and neomycin. Twenty-four hours after bronchoscopy, blood, mesenteric lymph nodes, spleen, liver, ileum and cecum were harvested for bacterial determination. The ileum was also assessed and graded histologically according to Chiu's injury scale. RESULTS: In the bronchoscopy group, bacterial translocation to the mesenteric lymph nodes was found in 7/15 rats (46.7%), compared to none of the controls (p=0.01). These rats also showed significant evidence of intestinal injury, compared to the controls (mean ranks, 32.7 or 8.5, p<0.0001). On the other hand, gut decontamination prevented bacterial translocation, compared to the bronchoscopy group (p=0.011). However, gut decontamination provided no beneficial effect on the intestinal mucosal injury, compared to the bronchoscopy group. These animals also revealed significant intestinal injury, compared to the controls (mean ranks, 27.8 or 8.5, p<0.0001). CONCLUSIONS: Our data shows that despite no amelioration in bronchoscopy-induced intestinal mucosal injury, gut decontamination has a preventive role for bronchoscopy-induced bacterial translocation.
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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) | 0.000 | 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 | 0.001 |
| 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".