Breast Cancer Aptamers: Current Sensing Targets, Available Aptamers, and Their Evaluation for Clinical Use in Diagnostics
Bibliographic record
Abstract
Breast cancer is the most commonly occurring cancer in women worldwide, and the rate of diagnosis continues to increase. Early detection and targeted treatment towards histological type is crucial to improving outcomes, but current screening methods leave some patients at risk of late diagnosis. The risk of late diagnosis and progressed disease is of particular concern for young women as current screening methods are not recommended early in life. Aptamers are oligonucleotides that can bind with high specificity to target molecules such as proteins, peptides, and other small molecules. They are relatively cheap to produce and are invariable from batch to batch, making them ideal for use in large-scale clinical or screening programs. The use of aptamers for breast cancer screening, diagnosis, and therapeutics is promising, but comparison of these aptamers and their corresponding biomarkers for use in breast cancer is significantly lacking. Here, we compare the currently available aptamers for breast cancer biomarkers and their respective biomarkers, as well as highlight the electrochemical sensors that are in development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".