Bibliographic record
Abstract
Abstract: Intracellular tryptophan (Trp) is catabolized to a large repertoire of metabolites via two major pathways: indoleamine and tryptophan 2, 3-dioxygenases (IDO/TDO) and Trp hydroxylase (TPH) pathways. The catabolites possess diverse biological activities and carry out various physiological functions. Several catabolites such as kynurenine (Kyn) and serotonin promote while melatonin and 5-methoxytryptophan (5-MTP) suppress cancer growth and metastasis. Cancer cell-derived Kyn enhances cancer growth and evasion of immunosurveillance by interacting with cancer cell and immune cell membrane aryl hydrocarbon receptors (AHR), respectively. Serotonin exerts its tumor-promoting activities through type 1 and type 2 serotonin receptors. 5-MTP and melatonin suppress cancer growth and metastasis by common mechanisms, i.e., inhibition of p300 histone acetyltransferase (HAT) and NF-κB activation, and suppression of cyclooxygenase-2 and cytokine transcription. Both metabolites block p38 MAPK signaling pathway. Human cancer tissues express increased levels of IDO, TDO and kynurenine monooxygenase (KMO) which are correlated with reduced patient survival. In summary, cancer Trp metabolism regulates cancer growth and metastasis by complex mechanisms. 5-MTP and melatonin provide valuable lead to develop new drugs for chemo-prevention and adjuvant therapy of cancer.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".