A comparative study to evaluate the effects of antibiotics, plant extracts and fluoride-based toothpaste on the oral pathogens isolated from patients with gum diseases in Pakistan
Bibliographic record
Abstract
Oral diseases caused by various microorganisms are common around the world. Scientific research has now been focusing on novel medicines to overcome bacterial resistance and antibiotics side effects; therefore, the current study was designed to assess the efficacy of certain antibiotics, toothpaste, and medicinal plant extracts (Ajuga bracteosa and Curcuma longa) versus the bacterial pathogens isolated from the human oral cavity. A total of 130 samples were collected from Khyber Teaching Hospital Peshawar, Pakistan, among those 27 species isolated, and eight bacterial species were identified from the samples. Among all the bacterial species, Staphylococcus aureus (29.62%) and Proteus mirabilis (22.2%) were found to be more prevalent oral pathogens. In comparison, the least pervasive microbes were Proteus vulgaris, Shigella sonnei, Escherichia coli and Aeromonas hydrophila. The study also suggested that dental problems were more prevalent in males (41-50 years of age) than females. Among the eight antibiotics used in the study, the most promising results were shown by Foxicillin against A. hydrophila. The survey of TP1 revealed that it showed more potent antagonist activity against Proteus vulgaris as compared TP2 and TP3 that might be due to the high content of fluoride. The Curcuma longa showed more significant activity than Ajuga bracteosa (Stem, leaves and root) extracts. The data obtained through this study revealed that antibiotics were more effective for oral bacterial pathogens than toothpaste and plant extracts which showed moderate and low activity, respectively. Therefore, it is suggested that the active compounds in individual medicinal plants like Curcuma longa and Ajuga bracteosa could replace the antibiotics when used in daily routine as tooth cleansers or mouth rinses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".