Targeting Cancer Stem Cell Lines as a New Treatment of Human Cancer
Bibliographic record
Abstract
Many studies have demonstrated that most cancers are clonal and are maintained by a cancer stem cell. Cancer stem cells have been identified in blood, breast, brain, lungs, gastrointestinal, prostate and ovarian cancer. Under normal homeostasis tissue specific stem cell division would be under strict control. When proliferation becomes independent of normal cellular controls, cancer develops. Studies indicate that cancer stem cells maintain their ability to differentiate, which explains the variety of cell types observed in tumors. Most therapies are directed at the fast growing tumor mass but not the slow dividing cancer stem cells and therefore the cancer is not eradicated. Understanding the process of transformation from a highly regulated stem cell to a cancer stem cell requires an understanding of genetic and epigenetic processes as well as having an understanding of the stem cell niche and the interaction of the stem cells with supportive cells in the niche. Current research is helping us to understand stem cells and stem cell regulation and in turn this will help to develop novel therapies to eliminate cancer and the initiating cancer stem cell. The relevant patents on the stem cell regulation and cancer therapy by stem cells are discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".