Detection and quantification of circulating tumor cells in mouse models of human breast cancer using immunomagnetic enrichment and multiparameter flow cytometry
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
BACKGROUND: Circulating tumor cells (CTCs) in the peripheral blood of breast cancer patients may be an important indicator of metastatic disease and poor prognosis. However, the use of experimental models is required to fully elucidate the functional consequences of CTCs. The purpose of this study was to optimize the sensitivity of multiparameter flow cytometry for detection of human tumor cells in mouse models of breast cancer. METHODS: MDA-MB-468 human breast cancer cells were serially diluted in whole mouse blood. Samples were lysed and incubated with a fluorescein isothiocyanate-conjugated anti-human leukocytic antigen antibody and a phycoerythrin-conjugated anti-mouse pan-leukocyte CD45 antibody. Samples were then immunomagnetically depleted of CD45-positive leukocytes, fixed, permeabilized, and stained with propidium iodide before flow cytometric analysis. RESULTS: Human breast cancer cells could be differentiated from mouse leukocytes based on increased light scatter, cell surface marker expression, and aneuploid DNA content. The method was found to have a lower sensitivity limit of 10(-5) and was effective for detecting human breast cancer cells in vivo in the circulation of experimental mice carrying primary human mammary tumors. CONCLUSIONS: This technique has the potential to be a valuable and sensitive tool for investigating the biological relevance of CTCs in experimental mouse models of breast cancer.
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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 | 0.001 | 0.001 |
| 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