Totarol exhibits antibacterial effects through antibiofilm and combined interaction against vancomycin-resistant <i>Enterococcus faecalis</i>
Bibliographic record
Abstract
The rise of vancomycin-resistant enterococci (VRE) due to antibiotic overuse poses a significant threat to long-term care patients and those with impaired immune systems. Therefore, it is imperative to seek alternatives to overcome multidrug resistance. This study aimed to evaluate totarol, a natural compound derived from Podocarpus totara, for its antibacterial activity against vancomycin-resistant Enterococcus faecalis (VREF). Totarol exhibited potent antibacterial activity at a very low concentration of 0.25 µg/mL and demonstrated antibiofilm effects through biofilm inhibitory concentration and biofilm eradication concentration assays. Confocal laser scanning microscopy confirmed that totarol inhibited not only biofilm mass but also bacterial cell viability. The combinatorial use of sublethal concentrations of totarol and vancomycin showed antibacterial activity, as observed in the time-kill assay. Quantitative polymerase chain reaction assays revealed a concentration-dependent downregulation of key virulence genes ( vanA, ace, asa, efaA, and esp) in VREF when exposed to totarol. In summary, totarol emerges as a promising adjuvant with vancomycin for inhibiting VREF, addressing vancomycin resistance and biofilm formation—critical challenges associated with VRE infection. Since this was an in vitro study, the role of totarol in the clinical implications of VREF treatment remains to be demonstrated.
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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".