<i>In Vitro</i> and <i>In Vivo</i> Efficacy of Novel Flavonoid Dimers against Cutaneous Leishmaniasis
Bibliographic record
Abstract
Treatment of leishmaniasis by chemotherapy remains a challenge because of limited efficacy, toxic side effects, and drug resistance. We previously reported that synthetic flavonoid dimers have potent antipromastigote and antiamastigote activity against Leishmania donovani, the causative agent of visceral leishmaniasis. Here, we further investigate their leishmanicidal activities against cutaneous Leishmania species. One of the flavonoid dimers (compound 39) has marked antipromastigote (50% inhibitory concentrations [IC50s], 0.19 to 0.69 μM) and antiamastigote (IC50s, 0.17 to 2.2 μM) activities toward different species of Leishmania that cause cutaneous leishmaniasis, including Leishmania amazonensis, Leishmania braziliensis, Leishmania tropica, and Leishmania major. Compound 39 is not toxic to peritoneal elicited macrophages, with IC50 values higher than 88 μM. In the mouse model of cutaneous leishmaniasis induced by subcutaneous inoculation of L. amazonensis in mouse footpads, intralesional administration of 2.5 mg/kg of body weight of compound 39.HCl can reduce footpad thickness by 36%, compared with that of controls values. The amastigote load in the lesions was reduced 20-fold. The present study suggests that flavonoid dimer 39 represents a new class of safe and effective leishmanicidal agent against visceral and cutaneous 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".