Towards a personalized approach to aromatase inhibitor therapy: a digital microfluidic platform for rapid analysis of estradiol in core-needle-biopsies
Bibliographic record
Abstract
Despite advances in breast cancer prevention and treatment, variability in patient-response has revealed the need for a more "personalized" approach to medicine, in which treatments are tailored to each patient's biology. Motivated by this idea, we introduce a technique that allows for quantification of small-molecule analytes directly from core needle biopsy (CNB) tissue samples on a miniaturized platform. The new technique, powered by digital microfluidics, integrates tissue-liquid extraction and magnetic bead-based competitive immunoassay for quantification of estradiol in milligram-sized CNB samples. Each measurement (from start to finish) requires ∼40 minutes, a duration consistent with a visit to a doctor's office. The performance of the new technique was validated by the gold-standard analysis method (high performance liquid chromatography coupled to tandem mass spectrometry), and was applied to evaluate human patient samples before and after a course of treatment with aromatase inhibitor therapy. We propose that the new technique has great potential for eventual use for fast, automated, and quantitative analysis of biomarkers in tissue samples, towards a personalized medicine approach.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".