Co-administration of aqueous ginseng extract with tobramycin stimulates the pro-inflammatory response and promotes the killing of <i>Pseudomonas aeruginosa</i> in the lungs of infected rats
Bibliographic record
Abstract
North American ginseng is known to have immunomodulatory and antipseudomonal properties in vitro. In this study we investigated the effects of aqueous ginseng extract, either alone or in a combination with the antibiotic tobramycin, in an animal model of chronic Pseudomonas aeruginosa lung infection. The lungs of male rats (n = 5) were infected with P. aeruginosa (2 × 10(8) cfu/mL) in agar-beads by intratracheal instillation. Starting on day 7 post-infection, animals were treated daily for 3 consecutive days with saline, tobramycin (300 μg/kg body mass, intratracheal), and (or) ginseng (100 mg/kg body mass, subcutaneous); animals were sacrificed 24 h after the third drug treatment. Lung bacteria counts, cytokine levels in sera, and lung histopathology were examined. The treatment of infected animals with tobramycin [6.6 × 10(4) colony forming units (cfu)], ginseng (5.3 × 10(4) cfu), or tobramycin plus ginseng (2.0 × 10(3) cfu) lessened the lung infection compared with the control group (saline treated) (6.0 × 10(6) cfu). The levels of pro-inflammatory cytokines (IL-2, IL-4, IL-6, IL-12p70, IFN-γ, GM-CSF, TNF-α) in infected animals were significantly increased with co-treatment of ginseng plus tobramycin. These data suggest that co-administration of aqueous ginseng extract and tobramycin stimulated the pro-inflammatory response and promoted the killing of P. aeruginosa.
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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".