Synergistic Effect of Copper Nanoparticles and Paromomycin in the Treatment of Cutaneous Leishmaniasis
Bibliographic record
Abstract
The interest in nanotechnology applications in medicine, particularly for combating microbial infections, has surged in recent years. This study investigated the in vitro and in vivo antileishmanial effects of copper nanoparticles (CuNPs) that were green synthesised using Capparis spinosa fruit extract, both on their own and in conjunction with paromomycin (PM). CuNPs were synthesised from a methanolic extract of C. spinosa. We assessed the in vitro antileishmanial activity of CuNPs (10-200 μg/mL) as well as the same concentrations of CuNPs (10-200 μg/mL) combined with PM (10-200 μg/mL), targeting the promastigote and amastigote forms of Leishmania major. Additionally, we evaluated the cytotoxic effects of CuNPs on THP1 cells. Subsequently, we tested these formulations on female BALB/c mice infected with L. major. The study measured footpad swelling, quantified parasite load through real-time PCR, and assessed levels of cytokines such as interleukin-4 (IL-4) and gamma interferon (IFN-ɤ), nitric oxide (NO), and arginase (ARG). The results demonstrated that CuNPs, particularly when combined with PM, significantly inhibited (p < 0.001) the growth of L. major promastigotes and amastigotes and stimulated IFN-ɤ, NO production and reduced IL-4 and ARG levels (p < 0.05). Importantly, CuNPs exhibited minimal cytotoxicity towards THP1 cells. In infected mice, the treatment with CuNPs, notably in combination with PM, resulted in a significant (p < 0.05) reduction in the mean number of parasites. Treatment with CuNPs at concentrations of 100 and 200 mg/mL led to a decrease in lesion diameter. The results of this study highlight the potent antileishmanial activity and synergistic effects of CuNPs, both alone and in combination with PM, against L. major promastigotes and amastigote forms, as well as their potential in treating cutaneous leishmaniasis (CL) in BALB/c mice.
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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) | 0.000 | 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".