Development of liposomes loaded with anti-leishmanial drugs for the treatment of cutaneous leishmaniasis
Bibliographic record
Abstract
Cutaneous leishmaniasis is caused by different species of Leishmania parasites and its available treatments have not yet provided a strong consistent result. The weak response of current chemotherapeutics is due to their deficient effects on stealth parasites inside macrophages, rapid clearance from the site of action and systemic side effects in high doses. Liposomal formulation of anti-leishmanial drugs could overcome these problems. In this study, different liposomal formulations of three famous anti-leishmanial drugs: Glucantime®, miltefosine and paromomycin were prepared by a modified freeze-drying double emulsion method. Liposome size, zeta potential and encapsulation efficiency were evaluated, and their imaging was carried out by means of atomic force microscopy. Three formulations were evaluated in vivo by subcutaneous injection into skin lesions caused by Leishmania major in BALB/c mice. Encapsulation efficiency of prepared liposomes was up to 90%; however, they inherited a bimodal size distribution that caused their encapsulation efficiency to decrease to 50% during filtering sterilization. Besides, the effect of surface charge was significant on preparation procedure, size and encapsulation efficiency. All three formulations reduced amastigote counts and lesion size but only miltefosine-loaded formulations had significant therapeutic effects compared with control group (p < 0.05).
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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".