Editorial: Strategies in the drug discovery and development for leishmaniasis: immunomodulators, natural products, synthetic compounds, and drug repositioning
Bibliographic record
Abstract
Strategies in the drug discovery and development for leishmaniasis: immunomodulators, natural products, synthetic compounds, and drug repositioning Leishmaniasis is a group of tropical diseases that affect millions of people worldwide.These diseases are transmitted by vectors and caused by protozoa of the genus Leishmania.The WHO reported that in 2020, Leishmaniasis was present in 98 countries or territories, and it posed a severe public health issue in four eco-epidemiological regions: Americas, East Africa, North Africa, West and Southeast Asia.That year, there were reports of 208,357 cases of Cutaneous Leishmaniasis (CL) and 12,838 cases of Visceral Leishmaniasis (VL) (Ruiz-Postigo et al., 2021).For the last 70 years, the available chemotherapy has been constituted by first-line (pentavalent antimonials) and second-line drugs (amphotericin B, pentamidine, paramomycin, and miltefosine).Existing drugs generally have disadvantages related to therapeutic approaches, such as route of administration, the need for trained professionals for administration, toxicity, high cost.Considering the number of pathogenic species concerning therapeutic options, this arsenal still needs to be improved (Burza et al., 2018).The lack of a vaccine or effective chemotherapy has stimulated many studies Frontiers in Cellular and Infection Microbiology frontiersin.org01
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.029 | 0.021 |
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".