Cancer stem cells as targets for cancer therapy: selected cancers as examples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is becoming increasingly evident that cancer constitutes a group of diseases involving altered stem-cell maturation/differentiation and the disturbance of regenerative processes. The observed malignant transformation is merely a symptom of normal differentiation processes gone astray rather than the primary event. This review focuses on the role of cancer stem cells (CSCs) in three common but also relatively under-investigated cancers: head and neck, ovarian, and testicular cancer. For didactic purpose, the physiology of stem cells is first introduced using hematopoietic and mesenchymal stem cells as examples. This is followed by a discussion of the (possible) role of CSCs in head and neck, ovarian, and testicular cancer. Aside from basic information about the pathophysiology of these cancers, current research results focused on the discovery of molecular markers specific to these cancers are also discussed. The last part of the review is largely dedicated to signaling pathways active within various normal and CSC types (e.g. Nanog, Nestin, Notch1, Notch2, Oct3 and 4, Wnt). Different elements of these pathways are also discussed in the context of therapeutic opportunities for the development of targeted therapies aimed at CSCs. Finally, alternative targeted anticancer therapies arising from recently identified molecules with cancer-(semi-)selective capabilities (e.g. apoptin, Brevinin-2R) are considered.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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 it