EFFECTS OF TOPOISOMERASES INHIBITORS PROTOBERBERINE ON LEISHMANIA DONOVANI GROWTH, MACROPHAGE FUNCTION, AND INFECTION
Bibliographic record
Abstract
DNA topoisomerases play a pivotal role in the regulation of cell division. Inhibition of Leishmania spp. topoisomerases represents an alternative to control parasite growth. Cancer research led to the development of several potent topoisomerase inhibitors such as topoisomerase I, topoisomerase II, or both (monobenzimidazole, terbenzimidazole, and protoberberine alkaloid-related compounds) that are effective antitumor agents. In the present study, we evaluated the efficacy of these compounds against Leishmania spp. growth in vitro. Some protoberberine compounds showed pronounced antileishmanial activity and were selected for further analysis in macrophages. These compounds did not affect macrophage viability and only slightly reduced macrophage nitric oxide generation in response to interferon-gamma. Moreover, exposure of infected macrophages to these compounds significantly reduced parasite loads. Collectively, our data suggest that protoberberine-related compounds have powerful antileishmania action and that minor structural variations among them can substantially improve their activity to restrict Leishmania spp. infection in vitro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".