Intense Uptake of Liposomal Curcumin by Multiple Myeloma Cell Lines: Comparison to Normal Lymphocytes, Red Blood Cells and Chronic Lymphocytic Leukemia Cells
Bibliographic record
Abstract
BACKGROUND/AIM: Curcumin is being widely investigated for its anticancer properties and several studies in the literature suggest that curcumin is distributed to a higher degree in cancer cells compared to normal cells. The goal of this study was to investigate the disposition of curcumin in the form of Lipocurc™ in multiple myeloma (MM)-causing plasma cell lines and B-lymphocytes from healthy individuals and compare the uptake to previously published data for red blood cells (RBCs), peripheral blood mononuclear cells (PBMCs) from healthy individuals and PBMCs from patients with chronic lymphocytic leukemia (CLL-cells). MATERIALS AND METHODS: Two MM-producing cell lines were studied: RPMI-8266, an IgG lambda cell line, and NCL-H929, an IgA kappa line. The distribution of liposomal curcumin and its metabolism to the major stable metabolite tetrahydrocurcumin (THC) were measured in vitro in the cell lines and B-lymphocytes. The cells were incubated in plasma protein-supplemented media with liposomal curcumin (Lipocurc™) for 15 min at 37°C and the levels of curcumin and THC in cells and medium were determined by liquid chromatography tandem mass spectrometry. RESULTS: cells in RBCs, PBMCs and CLL cells, respectively. Conversion of curcumin to THC was greatest in PBMCs, considerably less in CLL cells and minimal or absent in B-lymphocytes and MM cell lines. CONCLUSION: The extremely intense uptake of curcumin (as Lipocurc™) in both MM lines further suggests that Lipocurc™ should be investigated in the treatment of patients with this disease.
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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".