A Microfluidic Technique for Quantification of Steroids in Core Needle Biopsies
Bibliographic record
Abstract
Core needle biopsy (CNB) sampling is known to be inexpensive and minimally invasive relative to traditional tissue resectioning. But CNBs are often not used in analytical settings because of the tiny amount of sample and analyte. To address this challenge, we introduce an analytical method capable of multiplexed steroid quantification in CNB samples-those studied here ranged in mass from 2 to 8 mg. The new method uses digital microfluidics to extract steroids from CNB tissue samples (including a solid-phase extraction cleanup step) followed by analysis by high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS). The method has limits of detection of 3.6, 1.6, 5.8, and 8.5 fmol for estradiol, androstendione, testoterone, and progesterone, respectively. We propose that future generations of this method may be useful for regular quantification of steroids in core needle biopsy samples of breast tissue to inform dosage and timing of antihormone or hormone replacement therapies as part of a personalized medicine approach to treating a variety of hormone-sensitive disorders.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".