Alkaloids Modulate Motility, Biofilm Formation and Antibiotic Susceptibility of Uropathogenic Escherichia coli
Bibliographic record
Abstract
Alkaloid-containing natural compounds have shown promise in the treatment of microbial infections. However, practical application of many of these compounds is pending a mechanistic understanding of their mode of action. We investigated the effect of two alkaloids, piperine (found in black pepper) and reserpine (found in Indian snakeroot), on the ability of the uropathogenic bacterium Escherichia coli CFT073 to colonize abiotic surfaces. Sub-inhibitory concentrations of both compounds (0.5 to 10 µg/mL) decreased bacterial swarming and swimming motilities and increased biofilm formation. qRT-PCR revealed a decrease in the expression of the flagellar gene (fliC) and motility genes (motA and motB) along with an increased expression of adhesin genes (fimA, papA, uvrY). Interestingly, piperine increased penetration of the antibiotics ciprofloxacin and azithromycin into E. coli CFT073 biofilms and consequently enhanced the ability of these antibiotics to disperse pre-established biofilms. The findings suggest that these alkaloids can potentially affect bacterial colonization by hampering bacterial motility and may aid in the treatment of infection by increasing antibiotic penetration in biofilms.
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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) | 0.000 | 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 | 0.000 |
| 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".