Characterization and Biomedical Applications of Green-Synthesized Selenium Nanoparticles Using Tridax procumbens Stem Extract
Bibliographic record
Abstract
Background Selenium nanoparticles (SeNPs) are one of the metal nanoparticles that have been widely utilized for their anti-microbial, anti-oxidant, anti-inflammatory activities, and other biomedical applications. Tridax procumbens (TP) stem extract is a promising herb species rich in flavonoids, tannins, alkaloids, phytosterols, and hydroxycinnamates, which play a major role in wound healing applications. Aim The study aims to synthesize SeNPs using TP stem extract, characterizations, and its biomedical applications. Materials and methods SeNPs were synthesized using TP stem extract. The green synthesis of SeNPs was confirmed by ultraviolet-visible (UV-vis) spectra analysis. The synthesized SeNPs were characterized using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The agar well diffusion method was utilized to evaluate the anti-bacterial properties of the green synthesized SeNPs using TP stem extract. The anti-oxidant effect of SeNPs was tested using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, ferric-reducing anti-oxidant power assay (FRAP), and hydroxyl radical scavenging assay (H₂O₂). The anti-inflammatory effect was investigated using the bovine serum albumin assay and egg albumin denaturation method, and the cytotoxic effect of the green synthesized SeNPs was tested using the brine shrimp lethality (BSL) assay. Results The green synthesis of SeNPs was confirmed using different types of analysis techniques. The characterizations were done by UV-visible spectroscopy analysis, exhibiting a maximum peak at the range of 330 nm. SEM analysis revealed the shape of the nanoparticle to be hexagonal. The agar well diffusion method exhibited the anti-bacterial efficacy of SeNPs against wound microorganisms with a zone of inhibition of 14.6 mm for Escherichia coli (E. coli), 15.8 mm for Staphylococcus aureus (S. aureus), and 15.4 mm for Pseudomonas aeruginosa (P. aeruginosa). The TP stem-mediated SeNPs showed potential effects in anti-oxidant, anti-inflammatory, and cytotoxic activity, which shows very little toxicity. Conclusion Overall, the green synthesis of TP-stem-mediated SeNPs has great potential in biomedical applications. Thus, the synthesized SeNPs exhibit significant anti-bacterial efficacy against wound pathogens. The TP stem-mediated SeNPs showed potential effects in anti-oxidant, anti-inflammatory, and cytotoxic activity, which shows low toxicity. Furthermore, the green-synthesized SeNPs can be utilized in therapeutic management.
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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".