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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".