Xanthohumol: A Metabolite with Promising Anti-Neoplastic Potential
Bibliographic record
Abstract
The overwhelming global burden of cancer has posed numerous challenges and opportunities for developing anti-cancer therapies. Phytochemicals have emerged as promising synergistic compounds with potential anti-cancer effects to supplement chemo- and immune-therapeutic regimens. Anti cancer synergistic effects have been investigated in the interaction between phytocompounds derived from flavonoids such as quercetin, apigenin, kaempferol, hesperidin, emodin, etc., and conventional drugs. Xanthohumol is one of the prenylated phytoflavonoid that has demonstrated key anti-cancer activities in in vitro (anti proliferation of cancer cell lines) and in vivo (animal models of xenograft tumours) studies, and has been explored from different dimensions for targeting cancer subtypes. In the last decade, xanthohumol has been investigated how it induces the anti- cancer effects at cellular and molecular levels. The different signalling cascades and targets of xanthohumol are summarized in this review. Overall, this review summarizes the current advances made in the field of natural compounds with special reference to xanthohumol and its promising anti-cancer effects to inhibit tumour progression. The present review has also discussedthe potential of xanthohumol transitioning into a leadingcandidate from nano-therapy viewpoint along with the challenges which need to be addressed for extensive preclinical and clinical anti-cancer studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".