Detecting the Origin of Cancer‐Mobile Quantum Probe for Single Cancer Stem Cell Detection
Bibliographic record
Abstract
Abstract Cancer stem cells (CSC) are believed to be the driving force of cancer metastases and are a rare population of self‐renewing cells that contribute majorly to the poor outcomes of cancer therapy. The detection of CSC is of utmost importance to shed light on the indestructible nature of certain solid tumors and their metastatic ability. However, tumors tend to harbor CSCs in a specialized niche, making the detection process difficult. Currently, there is no method available to detect CSCs. The significance of this work is twofold. First, to the best of the knowledge, it is the first time that the detection of CSC is demonstrated. This approach simultaneously detects both the phenotypic and the metabolic state of the cell, thus enabling universal detection of CSC with high accuracy. Second, to the best of the knowledge, for the first time, light is shed on cell chemistry of CSC in their dedicated niche to facilitate a better understanding of the key players involved in the metabolic rewiring of CSC. This work will enable a better understanding of the fundamentals of CSCs, which are critical for the early diagnosis of cancer and the development of therapies for the cure of cancer.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".