Reversal of heavy metal-induced antibiotic resistance by dandelion root extracts and taraxasterol
Bibliographic record
Abstract
Introduction. Metal exposure is an important factor for inducing antibiotic resistance in bacteria. Dandelion extracts have been used for centuries in traditional Chinese and Native American medicine. Aim. We assessed the effects of dandelion water extracts and taraxasterol on heavy metal-induced antibiotic resistance in Escherichia coli as well as the underlying mechanisms. Methodology. Dandelion extracts were obtained through 4 h of boiling in distilled water. Bacterial growth was monitored with a spectrophotometer. Biochemical assays were performed to assess the activities and gene transcriptions of β-lactamase and acetyltransferase. Oxidative stress was determined using an oxidation-sensitive probe, H2DCFDA. Results. The present study demonstrated that higher concentrations of nickel (>5 µg ml−1), cadmium (>0.1 µg ml−1), arsenic (>0.1 µg ml−1) and copper (>5 µg ml−1) significantly inhibited the growth of E. coli . Lower concentrations of nickel (0.5 µg ml−1), cadmium (0.05 µg ml−1) and arsenic (0.05 µg ml−1) had no effect on bacterial growth, but helped the bacteria become resistant to two antibiotics, kanamycin and ampicillin. The addition of dandelion root extracts and taraxasterol significantly reversed the antibiotic resistance induced by these heavy metals. The supplements of antibiotics and cadmium generated synergistic effects on the activities of β-lactamase and acetyltransferase (two antibiotic resistance-related proteins), which were significantly blocked by either dandelion root extract or taraxasterol. In contrast, oxidative stress was not involved in the preventative roles of dandelion root extracts and taraxasterol in heavy metal-induced antibiotic resistance. Conclusion. This study suggests that heavy metals induce bacterial antibiotic resistance and dandelion root extracts and taraxasterol could be used to help reverse bacterial resistance to antibiotics.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".