Exploitation of the antibacterial, antibiofilm and antioxidant activities of Salvadora Persica (Miswak) extract
Bibliographic record
Abstract
The Salvadora persica (S. persica) L. chewing stick, usually known as miswak, is still being employed as an oral hygiene agent for plaque and gingivitis prevention. This study aims to assess the antibacterial, antibiofilm, antioxidant, and phytochemical profile of S. persica extract. The S. persica was purchased from a local market, grinded and extracted with petroleum ether. The disk diffusion, microdilution, and micro-plate assays were performed to evaluate the antibacterial and antibiofilm activities of the prepared extract at different concentrations against β-lactam resistance Streptococcus species. Free radical scavenging 2,2-diphenyl-1-picrylhydrazyl (DPPH) and stable radical cationic chromophore, 2,2-azinobis-(3-ethylbenzothiazoline-6-sulfonate) (ABTS) methods were used to determine their antioxidant activity. Chromatographic and spectrometric analyses were performed using gas chromatography-mass (GC-MS) spectrometry. The minimum inhibitory concentration (MIC) of S. persica extract against β-lactam resistance Streptococcus species ranged from 6.25 to 12.5 mg/mL. The maximum suppression of biofilm formation by S. persica extract was observed at MIC with a percentage of 68.66%, against Streptococcus oralis. The S. persica extract exhibited antioxidant activity with IC50 of 20 µg/mL and 35 µg/mL from DPPH and ABTS, respectively. The phytochemical characterization showed the presence of 22 compounds with major compounds; benzyl isothiocyanate (36.21%) and n-hexadecanoic acid (27.62%). The S. persica extract exhibited antibacterial activity against β-lactam resistant Streptococcus species, showing a promising natural alternative that could be a treatment option.
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) | 0.000 | 0.000 |
| 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".