Chinese Journal of Cancer www.cjcsysu.com
Bibliographic record
Abstract
According to the cancer stem cell theory, cancers can be initiated by cancer stem cells. This makes cancer stem cells prime targets for therapeutic intervention. Eradicating cancer stem cells by efficient targeting agents may have the potential to cure cancer. In this review, we summarize recent breakthroughs that have improved our understanding of cancer stem cells, and we discuss the therapeutic strategy of targeting cancer stem cells, a promising future direction for cancer stem cell research. Key words Cancer stem cells, targeting therapy, pharmaceutical carriers Review Advances in medical investigation have fostered a deeper understanding of cancer, from tissue anatomy to molecular mechanisms of tumor initiation and development. However, there is no consensus on how cancers are initiated or how they develop from small neoplasms to fatal tumors. Based on experimental evidence, scientists have proposed that tumors may possess cells similar in phenotype to normal stem cells and that these cancer stem cells (CSCs) are the origin of tumors. At the end of the 20th century, Canadian scientists identified a hierarchy in leukemia cells, a breakthrough that laid the foundation for CSC research and cast new light on cancer therapy. Here, we will summarize recent discoveries about CSCs and discuss how the cancer stem cell theory has impacted the potential for targeted therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.149 | 0.029 |
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".