Targeting Leishmania amazonensis amastigotes through macrophage internalisation of a hydroxymethylnitrofurazone nanostructured polymeric system
Bibliographic record
Abstract
Dextran-coated poly (n-butyl cyanoacrylate) nanoparticles (PBCA-NPs) were prepared and were evaluated for enhanced delivery of a promising anti-Leishmania drug candidate, hydroxymethylnitrofurazone (NFOH), to phagocytic cells.Currently available chemotherapy for leishmaniasis, such as pentavalent antimonials, presents low safety and efficacy.Furthermore, widespread drug resistance in leishmaniasis is rapidly emerging.To overcome these drawbacks, the use of nanosized delivery systems can reduce systemic drug toxicity and increase the drug concentration in infected macrophages, therefore improving treatment of leishmaniasis.PBCA-NPs containing NFOH (PBCA-NFOH-NPs) were prepared by an anionic emulsion polymerisation method.The z-average and polydispersity index (PDI) were determined by photon correlation spectroscopy, the zeta potential by microelectrophoresis and the entrapment efficiency by HPLC.Cytotoxicity was determined using macrophages from BALB/c mice.Efficacy tests were performed using Leishmania amazonensis promastigotes and amastigotes.The z-average of PBCA-NFOH-NPs was 151.5 ± 61.97 nm, with a PDI of 0.104 ± 0.01, a zeta potential of -10.1 ± 6.49 mV and an entrapment efficiency of 64.47 ± 0.43%.Efficacy in amastigotes revealed IC50 values of 0.33 μM and 31.2 μM for the nanostructured and free NFOH, respectively (95-fold increase).The cytotoxicity study indicated low toxicity of the PBCA-NFOH-NPs to macrophages.The selectivity index was 370.6, which is 49-fold higher than free NFOH (7.6).Such findings indicated that improved efficacy could be due to NP internalisation following site-specific drug delivery and reactivation of immune protective reactions by the NP components.Thus, PBCA-NFOH-NPs have the potential to significantly improve the treatment of leishmaniasis, with reduced systemic side effects.
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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".