Promising nanotherapy in treating leishmaniasis
Bibliographic record
Abstract
Leishmaniases are infectious diseases caused by an intracellular protozoan in humans by 20 different species of Leishmania among more than 53 species. There are at least twelve million cases of infections worldwide and three hundred and fifty million people are at risk in at least 98 developing countries in Africa, South-East Asia, and the Americas. Only Brazil presented high burden for both visceral leishmaniasis (VL) and cutaneous (CL). Chemotherapy is the main means of dealing with this infection. Nevertheless, only a few effective drugs are available, and each has a particular disadvantage; toxicity and long-term regimens compromise most chemotherapeutic options, which decreases patient compliance and adherence to the treatment and consequently the emergence of drug-resistant strains. Nano drug delivery systems (NanoDDS) can direct antileishmanial drug substances for intracellular localization in macrophage-rich organs such as bone marrow, liver, and spleen. This strategy can improve the therapeutic efficacy and reduce the toxic effects of several antileishmanial drug substances. This review is an effort to comprehensively compile recent findings, with the aim of advancing understanding of the importance of nanotechnology for treating leishmaniases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".