Antifungal Activity of Turmeric Extract (Curcuma longa Linn) Fortified with Silver Nanoparticles Against Pathogenic Fungi
Bibliographic record
Abstract
Trichophyton spp. is the most common etiological agent of human dermatophytosis worldwide.T. mentagrophytes and T. rubrum have various phenotypic virulence factors that allow the infection to establish and evolve.In traditional medicine and herbal remedies, medicinal plants have long played a significant role in producing secondary metabolites such as antimicrobial compounds.The main aim of this research is to investigate the effects of different forms of turmeric extract and silver nanoparticles on inhibiting the growth of certain pathogenic fungi, specifically Trichophyton mentagrophytes and Trichophyton rubrum.The study involved using aqueous and alcoholic extracts of turmeric, as well as an aqueous extract supplemented with silver nanoparticles.These extracts were mixed with a nutrient medium at various concentrations (5, 10, 15, and 20 mg/mL) to assess their effectiveness against fungal isolates.The inhibitory diameter for each concentration and type of extract (aqueous, alcoholic, and silver nanoparticle fortified) was measured to determine their inhibitory activity.Furthermore, the minimum inhibitory concentration for each type of extract was determined.The sensitivity of isolated fungi to the extracts varied, with T. rubrum showing a greater sensitivity than T. mentagrophytes.The results also revealed that alcoholic turmeric extract showed significant superiority over all other concentrations without nanoparticles, and also when adding 0.1 mg/mL of silver nanoparticles with the growth of the fungus Trichophyton mentagrophytes was lowest, it reached (12 and 8) mm without and with the addition of nanoparticles respectively.The findings highlight the potential antifungal properties of the different turmeric extracts tested in this study.For further research, the authors suggest exploring different concentrations or combinations with other nanoparticles.
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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".