Solid Lipid Nanoparticles Encapsulated With Paromomycin: An Effective Oral Formulation Against <i>Leishmania major</i> in Mouse Model
Bibliographic record
Abstract
Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania. The treatment of intracellular infections such as leishmaniasis is often challenging due to limited drug access to infected cells and the development of drug resistance. Therefore, the development of new antileishmanial compounds is essential. Paromomycin sulphate (PM) has shown promise as an antileishmanial drug, and one way to enhance its effectiveness is through appropriate delivery systems. Solid lipid nanoparticles (SLN) are being explored as a potential delivery system for PM, as they offer advantages over other colloidal carriers. In a recent study, PM was loaded into solid lipid nanoparticles (PM-SLN) and its oral effectiveness was evaluated in treating Leishmania (L.) major-infected mice. 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-c), nitric oxide (NO) and Arginase (ARG). Overall, the study demonstrated that oral administration of the PM-SLN formulation is safe and effective in treating leishmaniasis. The SLN in the PM-SLN compound improved the killing of parasites by PM and stimulated a Th1 immune response, indicating its potential as a treatment for leishmaniasis.
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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 | 0.001 |
| 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".