Barleria extracts containing barlerin and verbascoside boost immunity and regulate CYP450 gene in prostate cancer
Bibliographic record
Abstract
Barleria species have been traditionally utilized for medicinal purposes. This study provides a comprehensive analysis of six Barleria leaf extracts, namely B. cristata, B. lupulina, B. prionitis, B. repens, B. siamensis, and B. strigosa, to elucidate their metabolite composition, toxicity, immunomodulatory functions, and roles in cytochrome P450 (CYP) gene expression. The findings indicate that two key metabolites, barlerin and verbascoside, are present in all six Barleria extracts, with B. siamensis exhibiting the highest amount of these compounds at 0.43 mg/g (barlerin) and 1.02 mg/g (verbascoside) of dried leaf, respectively. In terms of toxicological effects, B. cristata and B. siamensis demonstrated significant anti-proliferative activity against PC-3 cells by inducing DNA damage, enhancing apoptosis, and obstructing the cell cycle. However, these extracts did not exert cytotoxic effects on PBMCs, HPrEC, and THLE-3. Conversely, B. strigosa extract exhibited mild toxicity towards HPrECs and moderate toxicity towards THLE-3 cells. Furthermore, treatment with these extracts activated PBMCs, leading to the upregulation of cytokine genes, including IL-2, IL-10, IL-12, IL-15, IL-21, and IFN-γ, which promoted cytotoxicity for PC-3 cells. Additionally, B. siamensis extract significantly suppressed the expression of CYP450 genes, including CYP1A2, CYP3A4, CYP2D6, and CYP2E1, whereas B. strigosa extract induced the overexpression of CYP2E1. In conclusion, Barleria extracts containing barlerin and verbascoside exhibit immunomodulatory properties by activating immune cells to target cancer cells. Moreover, these extracts influence the expression of CYP450 genes, potentially impacting their bioavailability and therapeutic efficacy.
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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) | 0.001 | 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".