Turmeric Phyto-NanoParticle (TPNP) enhances cellular bioavailability and anti-inflammatory effect of curcuminoids in human monocytes / macrophages
Bibliographic record
Abstract
Abstract The poor bioavailability of curcuminoids remains a major challenge to therapeutic use. This is largely due to their hydrophobicity, poor absorption, rapid metabolism, and short circulating half-life—limitations that are now being addressed through advances in nano- and micro-emulsion technologies. Curcuminoids and other water-insoluble phyto-polyphenols offer significant putative health benefits as anti-inflammatory, antioxidant, anticancer, radioprotective, and neuroprotective agents. Conventional emulsion-based delivery systems, such as liposomes, micelles, or solid lipid particles, rely on various emulsifying surfactants and/or excipients, some of which may themselves pose health risks. Here, we establish a novel class of all-natural, additive-free, oil-free, and emulsion-free Turmeric Phyto-NanoParticles (TPNPs) formulated directly from turmeric rhizomes and tested in a human monocyte/macrophage cell model to assess bioavailability kinetics and the efficacy of antioxidant and anti-inflammatory potential. TPNPs are enriched with curcuminoids (24.85% by mass), form a homogeneous nanoparticle distribution, exhibit higher antioxidant capacity, and demonstrate significantly improved cellular uptake in both monocytes and macrophages compared to conventionally purified curcuminoids. Favourable cellular pharmacodynamic anti-inflammatory effect of TPNPs was shown by increased levels of the cytoprotective enzyme heme oxygenase-1 (HMOX1), and a more effective reduction in lipopolysaccharide (LPS)-induced tumor necrosis factor (TNF) secretion compared to conventional curcuminoids. TPNPs could thus serve as a stable, non-synthetic, excipient-free formulation for safe and effective delivery of curcuminoids by nanocarriers for inflammatory conditions.
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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".