Interactions between lemongrass and lavender essential oils in combination with ampicillin influencing antibacterial activity on Sporosarcina ureae and Serratia liquefaciens
Bibliographic record
Abstract
The purpose of this study is to determine the effects of various combinations of essential oils (EOs) with antibiotics on bacterial growth. The molecular mechanisms behind the effects of individual phytochemicals in EOs and antibiotics is well understood, unlike the mechanisms behind the interactions between multiple phytochemicals and antibiotics in a mixture. Serratia liquefaciens and Sporosarcina ureae were exposed to various treatments of different combinations of Lavandula officinalis (lavender oil), Cymbopogon citratus (lemongrass oil) with ampicillin. For each treatment group, mean zones of inhibition (ZOI) were measured after exposure for 48 hours. Controls for both species did not yield any ZOI whereas all other treatments resulted in the inhibition of bacterial growth in both Serratia liquefaciens and Sporosarcina ureae. Statistical analyses showed that the combination of lemongrass oil and ampicillin was significantly more effective than all other treatments for Serratia liquefaciens. The lemongrass oil and ampicillin treatment was the only treatment that displayed additive effects. All treatments for Sporosarcina ureae, with the exception of the control and lavender oil treatments, showed a significantly higher mean ZOI when compared to control and lavender oil treatments. It was concluded that lemongrass oil was a better candidate to be included in antibacterial cocktails than lavender oil. However, further investigation is required to elucidate EOs that interact synergistically with ampicillin when acting on Serratia liquefaciens and Sporosarcina ureae. Additionally, further investigation into the molecular mechanisms behind the interactions of the components found in these EOs with ampicillin is required.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".