Autophagy and Cancer Metastasis: A Trojan Horse
Bibliographic record
Abstract
Metastasis is one of the most important challenges in cancer therapy strategies.1 Therefore, understanding the mechanisms of metastasis is a powerful weapon to increase the survival of patients with cancer and improve their quality of life. For the first time, Jean Claude used the term ‘metastasis’ as one of the most important hallmarks of cancer in 1829.2 Metastasis, a Greek word, means ‘displacement’ (meta meaning ‘next’ and stasis, ‘placement’).3 This term refers to a general description of migration and invasion of tumor cells from the primary tumor site to secondary sites. Metastasis is considered as one of the key etiologies of cancer-related death; therefore, understanding its mechanism in depth has been always on demand in basic and clinical sciences.4 Epithelial to mesenchymal transition (EMT) is one of the several processes, which is involved in metastasis, and development of drug resistance in cancer.5 During EMT, cells gradually convert from epithelial to a mesenchymal phenotype. This enables cancer cells to be more motile, have less extracellular matrix adhesion and be prone to detachment and moving toward distant organs. Beside metastasis, EMT is involved in embryonic development, wound healing, tissue fibrosis and scar formation.6–8 Macroautophagy (hereafter …
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".