Highly Specific Recognition of Breast Tumors by an RNA-Cleaving Fluorogenic DNAzyme Probe
Bibliographic record
Abstract
Breast cancer is one of the most commonly diagnosed cancers among females worldwide. Early detection of breast cancer is of vital importance to the reduction of the mortality rate. However, the lack of specific biomarkers that can effectively identify breast cancer cells limits the ability for early diagnosis of breast cancer. RNA-cleaving fluorogenic DNAzymes (RFDs), which can be produced through the systematic evolution of ligands by exponential enrichment (SELEX) process, are catalytic DNA molecules capable of generating a fluorescent signal when the appropriate target is bound. In this study, we carried out a SELEX experiment to select for RFDs that are active in the cell lysate of MDA-MB-231, a model breast cancer cell line. We obtained a RFD probe, named AAI2-5, that can detect MDA-MB-231 at a concentration of cell lysate proteins as low as 0.5 μg/mL (which is equivalent to ∼5000 cell/mL). AAI2-5 is capable of distinguishing MDA-MB-231 cells from normal cells as well as other types of tumor cells, including other subtypes of breast cancer cells. Moreover, AAI2-5 responded positively to more than 90% of malignant breast tumors. This report is the first study to explore the RFD system for the detection of cancer cells. The results suggest that RFD can be potentially applied for the diagnosis and treatment of breast cancer in the future.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".