Synthesis and Cytotoxic Activity of Novel Tetrahydrocurcumin Derivatives Bearing Pyrazole Moiety
Bibliographic record
Abstract
Cancer is one of the major causes of death worldwide despite remarkable progress in understanding the mechanism of the disease and finding appropriate treatments to control the disease and prevent death. A large number of natural products have been reported to show anticancer and cancer preventive activities. One good example is the naturally occurring yellow pigment curcumin (Fig. 1 ), which was isolated from the rhizomes of the plant Curcuma longa Linn, a member of the ginger family (Zingiberaceae) [ 1 ]. In vitro and in vivo research as well as clinical studies have shown the anticancer effect of curcumin as an anticancer and chemo-prevention agent [ 2 , 3 ]. Curcumin has also been shown to act as a drug transporter-mediated MDR reversal agent [ 4 , 5 ]. Tetrahydrocurcumin (THC, Fig. 1 ) is a major metabolite derived from curcumin. Reduction of curcumin (Fig. 1 ) by endogenous reductase system leads to THC which plays an important role in curcumin-induced biological effects [ 6 ]. THC can also be chemically synthesized from curcumin by catalytic hydrogenation using PtO 2 or palladium as a catalyst [ 7 , 8 ]. THC has been reported to inhibit tumor metastasis [ 9 ] and tumor angiogenesis in nude mice [ 10 ]. In 2007, Limtrakul and his coworkers demonstrated that THC acted as a MDR modulator when combined with other chemotherapeutics [ 11 ]. Furthermore, THC has protective effect on renal damage resulted from chemotherapy such as cisplatin for the treatment of cancer [ 12 ]. In addition to anticancer activity, THC displayed potent antioxidant activity [ 13 ], antidiabetic, anti-inflammatory, antiatherosclerotic and antihepatotoxic effects [ 14 ].
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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".