To Die or to Survive, a Fatal Question for the Destiny of Prostate Cancer Cells after Androgen Deprivation Therapy
Bibliographic record
Abstract
Prostate cancer is the most frequently diagnosed non-skin cancer in adult males in North America and is the second leading cause of cancer-related mortality. For locally advanced or metastatic disease, androgen deprivation, through medical or surgical castration, is the primary treatment to induce prostate cancer cell death and extend patient survival. However, the vast majority of cancers progress to a castration-resistant/androgen-independent state where the cell death processes are no longer active. This review describes the main cell death processes, apoptosis, autophagy, necrosis and necroptosis, which may be activated in prostate cancers after androgen deprivation therapy as well as the molecular mechanisms through which the cancers progress to become castration resistant. In particular, the central role of persistent androgen receptor (AR)-mediated signaling and AR crosstalk with other critical cell signaling pathways, including (i) the PI3K/Akt pathway, (ii) receptor tyrosine kinases, (iii) the p38 MAPK pathway, and (iv) the Wnt/β-catenin pathway, as well as reactivation of AR by de novo synthesized androgen are discussed in this context. Understanding the molecular changes that subvert normal cell death mechanisms and thereby compromise the survival of prostate cancer patients continues to be a major challenge.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".