Advances in Molecular Perspectives of Tumor-Initiating Cells on Cancer Therapy
Bibliographic record
Abstract
Cancer stem cells (CSCs) known as tumorigenic cells are biologically distinct from diverse subpopulations. Cancer cell heterogeneity readily leads to development of drug resistance and tolerance to treatment. CSC hypothesis has resulted in incredible impact on the understanding and insight into tumor biology. More importantly, advances in molecular perspectives have achieved in the recent decades although many aspects of this hypothesis remain speculative and are still evolving. CSC has been considered a new cellular target for anticancer drug discovery. Along with identification of different CSC markers such as CD133, CD24, CD44, CD90 and signaling pathways such as Wnt/β-catenin, hedgehog and so on, different kinds of therapeutic approaches have been developed to work on these molecular targets, resulting in selective inhibition of CSC functions including self-renewal and differentiation. Most recent studies demonstrated that CR1 expression in colon CSC can promote the stem cell clone formation, CCR7 promotes breast CSC growth, TP53 splice can enhance the pluripotency of CSC through the positive regulation of Sox2, Oct3/4 and Nanog and other key factors, to increase the potential risk of cancer recurrence, and an exciting finding is that carbon nanomaterials may be used as a CSC sniper. Selectively targeting CSC has shown promising perspectives and may open a new venue for the treatment of cancer. Cell Mol Med Res. 2023;1(1):3-7 doi: https://doi.org/10.14740/cmmr10e
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".