Targeting Cancer Stem Cells with Defined Compounds and Drugs
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
Abstract : Cancer stem cells (CSCs) are a subpopulation of tumor cells that possess self-renewal and tumor initiation capacity and the ability to give rise to the heterogenous lineages of cancer cells that comprise the tumor. CSCs possess numerous intrinsic mechanisms of resistance to chemotherapeutic drugs, novel tumor-targeted drugs and radiation therapy, allowing them to survive current cancer therapies and to initiate tumor recurrence and metastasis. Recently, different pathways that confer resistance and survival of CSCs, but also compounds and drugs that selectively target some of these pathways in CSCs have been identified. Such compounds and drugs include antibiotics like salinomycin, phytochemicals such as parthenolide, cyclopamine, EGCG, resveratrol, curcumin, sulforaphane and oxymatrine, the small molecule inhibitors vismodegib and repertaxin, monoclonal antibodies and antibody constructs raised against cell surface proteins expressed by CSCs, and, surprisingly, some classical drugs such as metformin, tranilast and thioridazine. These agents exhibit significant anti-CSC activity, alone or in combination with cytostatic drugs or tumor-targeted drugs, as recently shown in vitro and in human xenograft mice. Since current cancer therapies fail to eliminate CSCs, leading to cancer recurrence and progression, selective targeting of CSCs with compounds and drugs introduced herein may represent a novel therapeutic strategy to eradicate cancer.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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