New Curcuphenol Analogues Possess Anti-Metastatic Biological Activity
Bibliographic record
Abstract
Abstract For eons, turmeric and curcumin have been used as culinary spices and as traditional medicines and as vogue dietary supplements for a growing list of disorders, including arthritis, digestive disorders, respiratory infections, allergies, liver disease, depression and cancer. The activities of these spices are commonly attributed to curcuminoids; however, the medical applications of this class of compounds has been limited due to the low water solubility, chemical instability, acid lability, poor absorption, rapid catabolism by enzymes of the diverse curcuminoids contained in turmeric and curcumin extracts. Furthermore, identifying the bio-active curcuminoids with unique molecular entities responsible for specific medicinal benefit is at its infancy. To overcome these many issues and substantially advance this area of inquiry, we created a water-soluble achiral curcuphenol analogue and a water-soluble racemic analogue that have enhanced chemical characteristics and biological performance, and we subsequently demonstrated their ability to reverse the immune-escape phenotype, a process that enables tumours to hide from host immune responses and thereby provides tumours a significant growth advantage to metastatic tumours. The discovery that curcuphenols can reverse tumour immune-escape mechanisms and thereby reduce tumour growth, provides a rationale for the development of advanced dissecting nutraceuticals and bioceuticals for unique chemical entities as therapeutic building blocks to synthesize analogues with optimal chemical characteristics capable of harnessing the power of the immune system to extinguish metastatic cancers and beyond.
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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 | 0.001 |
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".