<i>Ex vivo</i> and <i>in vivo</i> HIV-1 latency reversal by “Mukungulu,” a protein kinase C-activating African medicinal plant extract
Bibliographic record
Abstract
Summary Current HIV latency reversing agents (LRAs) have had limited success in clinic, indicating the need for new strategies that can reactivate and/or eliminate HIV reservoirs. “Mukungulu,” prepared from the bark of Croton megalobotrys Müll. Arg., is traditionally used for HIV/AIDS management in Northern Botswana despite containing an abundance of protein kinase C (PKC)-activating phorbol esters (“namushens”). Here we show that Mukungulu is tolerated in mice at up to 12.5 mg/kg while potently reversing latency in antiretroviral therapy (ART)-suppressed HIV-infected humanized mice at 5 mg/kg. In peripheral blood mononuclear cells (PBMC) and isolated CD4+ T-cells from ART-suppressed people living with HIV-1, 1 µg/mL Mukungulu reverses latency on par with or superior to anti-CD3/CD28 positive control, as measured by HIV gag-p24 protein expression, where the magnitude of HIV reactivation in PBMC corresponds to intact proviral burden levels in CD4+ T-cells. Bioassay-guided fractionation identifies 5 namushen phorbol ester compounds that reactivate HIV expression, yet namushens alone do not match Mukungulu’s activity, suggesting additional enhancing factors. Together, these results identify Mukungulu as a robust natural LRA which may warrant inclusion in future LRA-based HIV cure and ART-free remission efforts.
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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 | 0.001 |
| 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".