Exploring the Antibacterial Potential and Underlying Mechanisms of Prunella vulgaris L. on Methicillin-Resistant Staphylococcus aureus
Bibliographic record
Abstract
Prunella vulgaris L. (PV) is a widely distributed plant species, known for its versatile applications in both traditional and contemporary medicine, as well as in functional food development. Despite its broad-spectrum antimicrobial utility, the specific mechanism of antibacterial action remains elusive. To fill this knowledge gap, the present study investigated the antibacterial properties of PV extracts against methicillin-resistant Staphylococcus aureus (MRSA) and assessed their mechanistic impact on bacterial cells and cellular functions. The aqueous extract of PV demonstrated greater anti-MRSA activity compared to the ethanolic and methanolic extracts. UPLC-ESI-MS/MS tentatively identified 28 phytochemical components in the aqueous extract of PV. Exposure to an aqueous extract at ½ MIC and MIC for 5 h resulted in a significant release of intracellular nucleic acid (up to 6-fold) and protein (up to 10-fold) into the extracellular environment. Additionally, this treatment caused a notable decline in the activity of several crucial enzymes, including a 41.51% reduction in alkaline phosphatase (AKP), a 45.71% decrease in adenosine triphosphatase (ATPase), and a 48.99% drop in superoxide dismutase (SOD). Furthermore, there was a decrease of 24.17% at ½ MIC and 27.17% at MIC in tricarboxylic acid (TCA) cycle activity and energy transfer. Collectively, these findings indicate that the anti-MRSA properties of PV may stem from its ability to disrupt membrane and cell wall integrity, interfere with enzymatic activity, and impede bacterial cell metabolism and the transmission of information and energy that is essential for bacterial growth, ultimately resulting in bacterial apoptosis. The diverse range of characteristics exhibited by PV positions it as a promising antimicrobial agent with broad applications for enhancing health and improving food safety and quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 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".