Triclosan Inhibits Uropathogenic <i>Escherichia coli</i> -Stimulated Tumor Necrosis Factor- <i>α</i> Secretion in T24 Bladder Cells <i>in Vitro</i>
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Triclosan is an antimicrobial agent commonly used in consumer and medical products that inhibits bacterial fatty acid synthesis. In addition to its bactericidal effects, sublethal concentrations of triclosan reduce local inflammation, inhibit the growth of bacterial uropathogens, induce membrane stress, and inhibit P-fimbrial expression in uropathogenic Escherichia coli (UPEC). We tested whether sublethal concentrations of triclosan could reduce the adherence of UPEC to bladder and kidney cells and reduce the amount of the pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-alpha) produced by these cells during bacterial challenge in vitro. MATERIALS AND METHODS: Assays of bacterial growth, adhesion, and intracellularization were performed using UPEC GR12 incubated for 4 hours on monolayers of human T24 bladder cells or A498 kidney cells with various sublethal concentrations of triclosan. The expression profile of TNF-alpha from bladder cells was evaluated using ELISA. RESULTS: No significant decreases were observed in the adherence or invasion percentages of UPEC GR12 with either cell line when treated with sublethal amounts of triclosan. However, treatment with triclosan 0.5 microg/mL led to a significant decrease in the total number of UPEC GR12 recovered from T24 monolayers (P < 0.05). Importantly, a reduction in the expression of TNF-alpha by T24 cells was shown when UPEC GR12 was treated with triclosan (P < 0.05). CONCLUSIONS: Sublethal concentrations of triclosan did not inhibit the adhesion or intracellularization of UPEC into kidney or bladder cell lines but did significantly reduce the amount of TNF-alpha secreted by bladder cells. Therefore, the use of triclosan on ureteral stents may prove clinically beneficial, not only by inhibiting bacterial survival and growth within the urinary tract, but by reducing local inflammation as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 teacher head, 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".