Green Synthesis of Euphorbia tirucalli-Mediated Titanium Dioxide Nanoparticles Against Wound Pathogens
Bibliographic record
Abstract
Background Wound infections caused by pathogens present a considerable global health challenge, resulting in extended healing durations, elevated healthcare expenses, and potential fatalities. Conventional approaches to managing wound pathogens have limitations such as antibiotic resistance, toxicity and allergic reactions. Consequently, there is a rising interest in exploring alternative strategies for preventing and treating wound infections. Titanium dioxide nanoparticles (TiO2NPs) have gained attention for their potential in wound healing, attributed to their distinctive properties, including antimicrobial and anti-inflammatory capabilities. Methods TiO2NPs synthesized through Euphorbia tirucalli were examined for their antibacterial potential against wound pathogens, using the Kirby-Bauer agar-well diffusion method and time-kill curve assay. Furthermore, the cytotoxic effect of the synthesized nanoparticles was evaluated through a brine shrimp lethality assay. Results Green-synthesized TiO2NPs demonstrated potent antimicrobial activity against tested wound pathogens, displaying a zone of inhibition against Pseudomonas aeruginosa (11 mm) and Escherichia coli (10 mm) at the highest concentration of 100 μg/mL. In the time-kill curve assay, the prepared TiO2NPs showed significant bactericidal activity against Pseudomonas aeruginosa followed by Escherichia coli. In the brine shrimp lethality assay, at the lowest concentration of 5 μg/mL of the prepared nanoparticles, 100% of the nauplii remained alive after 48 hours. Conclusion The results indicate that TiO2NPs synthesized using Euphorbia tirucalli extract exhibit potent antimicrobial activity against the tested wound pathogens. Moreover, the prepared nanoparticles exhibit lower toxicity, suggesting their potential use as an alternative to commercially available synthetic drugs.
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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) | 0.001 | 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".