Effectiveness of Piper longum Extract as Natural Irrigant on Antimicrobial Properties and Smear Layer Removal: A Scanning Electron Microscope Study
Bibliographic record
Abstract
Aim: This study aims to evaluate the antimicrobial effectiveness and smear layer removal of Piper longum extract as a natural irrigant in root canal therapy compared to ethylenediaminetetraacetic acid (EDTA), a commonly used irrigant.Materials and methods: A total of 0.5 gm of powdered P. longum fruit was extracted using distilled water and heat.Eighteen freshly extracted human maxillary central incisors were selected and decoronated to a length of 16 mm.Root canals were prepared and divided into two groups (n = 9 each): Group I was treated with 17% EDTA (control), and group II was treated with P. longum extract (test).Irrigants were activated using gutta-percha cones.Samples were sectioned for SEM evaluation at coronal, middle, and apical regions.SEM imaging was performed at ×200 (debris) and ×1,000 (smear layer).The antimicrobial analysis was done by incubating a fresh suspension of microorganisms, and the sterile wells bored were filled with varying concentrations of P. longum, and inhibition zones were measured using a vernier caliper for positive, negative, and experimental groups.Data were recorded and statistically analyzed using SPSS software.Results: Mean smear layer scores were 2.11 ± 0.60 EDTA and 1.66 ± 0.70 (P.longum).Mean debris scores were 2.22 ± 0.44 EDTA and 2.11 ± 0.33 (P.longum).Piper longum demonstrated enhanced smear layer removal and comparable debris clearance.Antimicrobial zones of inhibition for P. longum at 150 µL ranged from 12 mm (Streptococcus mutans) to 11 mm (Candida albicans), comparable to the positive control, ampicillin (bacteria) and fluconazole (fungi).The differences in inhibition zones between P. longum and the positive control were statistically significant (p < 0.05).No zones were observed for negative controls (distilled water).Conclusion: Piper longum extract demonstrated substantial efficacy in removing the smear layer and reducing microbial load within the root canal system, comparable to conventional EDTA.Clinical significance: There is a growing need for biocompatible and sustainable alternatives to synthetic irrigants, which may cause adverse effects such as tissue toxicity or dentin erosion.This study explores the potential of P. longum, a natural herbal extract with known antimicrobial and anti-inflammatory properties, as an adjunctive irrigant in root canal therapy.
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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) | 0.002 | 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".