Potential chemopreventive, anticancer and anti-inflammatory properties of a refined artocarpin-rich wood extract of Artocarpus heterophyllus Lam.
Bibliographic record
Abstract
Abstract Colorectal cancer (CRC) represents the third leading cause of death among cancer patients below the age of 50, necessitating improved treatment and prevention initiatives. A crude methanol extract from the wood pulp of Artocarpus heterophyllus was found to be the most bioactive among multiple others, and an enriched extract containing 84% ( w / v ) artocarpin (determined by HPLC–MS–DAD) was prepared. The enriched extract irreversibly inhibited the activity of human cytochrome P450 CYP2C9, an enzyme previously shown to be overexpressed in CRC models. In vitro evaluations on heterologously expressed microsomes, revealed irreversible inhibitory kinetics with an IC 50 value of 0.46 µg/mL. Time- and concentration-dependent cytotoxicity was observed on human cancerous HCT116 cells with an IC 50 value of 4.23 mg/L in 72 h. We then employed the azoxymethane (AOM)/dextran sodium sulfate (DSS) colitis-induced model in C57BL/6 mice, which revealed that the enriched extract suppressed tumor multiplicity, reduced the protein expression of proliferating cell nuclear antigen, and attenuated the gene expression of proinflammatory cytokines ( Il-6 and Ifn-γ ) and protumorigenic markers ( Pcna , Axin2 , Vegf , and Myc) . The extract significantly ( p = 0.03) attenuated (threefold) the gene expression of murine Cyp2c37 , an enzyme homologous to the human CYP2C9 enzyme. These promising chemopreventive, cytotoxic, anticancer and anti-inflammatory responses, combined with an absence of toxicity, validate further evaluation of A. heterophyllus extract as a therapeutic agent.
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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".