Evaluation of Free Radical Scavenging and Antimicrobial Activity of Coleus amboinicus-Mediated Iron Oxide Nanoparticles
Bibliographic record
Abstract
Background In this research, iron oxide nanoparticles were synthesized using Coleus amboinicus stem extract, which is used for various diseases such as throat infection, cough, fever, nasal congestion, and digestive problems. Aim This study aimed to formulate a green synthesis of iron oxide nanoparticles mediated by Coleus amboinicus (known as karpuravalli in Tamil) and assess its antimicrobial and antioxidant properties. Materials and methods Iron oxide nanoparticles were synthesized, and then their antimicrobial properties were tested against two specific pathogens, i.e., Streptococcus mutans and Candida albicans, using the agar well diffusion technique. The 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, hydroxyl radical scavenging (H2O2) assay, and ferric ion reducing antioxidant power (FRAP) assay were conducted to check the free radical scavenging activity. Result The results obtained showed that these iron oxide nanoparticles showed better antimicrobial activity against Streptococcus mutans when compared to Candida albicans, and the antioxidant activity showed a very close efficacy when compared to the standard. Conclusion The research has demonstrated the high antioxidant activity and high antibacterial activity of iron oxide nanoparticles using Coleus amboinicus stem, a natural and cheaper antimicrobial drug compared to the drugs present on the market.
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 | 0.000 |
| 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 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".