Enrichment of live unlabelled cardiomyocytes from heterogeneous cell populations using manipulation of cell settling velocity by magnetic field
Bibliographic record
Abstract
The majority of available cardiomyocyte markers are intercellular proteins, limiting our ability to enrich live cardiomyocytes from heterogeneous cell preparations in the absence of genetic labeling. Here, we describe enrichment of live cardiomyocytes from the hearts of adult mice in a label-free microfluidic approach. The separation device consisted of a vertical column (15 mm long, 700 μm diameter), placed between permanent magnets resulting in a field strength of 1.23 T. To concentrate the field at the column wall, the column was wrapped with 69 μm diameter nickel wire. Before passing the cells through the column, the cardiomyocytes in the cell suspension had been rendered paramagnetic by treatment of the adult mouse heart cell preparation with sodium nitrite (2.5 mM) for 20 min on ice. The cell suspension was loaded into the vertical column from the top and upon settling, the non-myocytes were removed by the upward flow from the column. The cardiomyocytes were then collected from the column by applying a higher flow rate (144 μl/min). We found that by applying a separation flow rate of 4.2 μl/min in the first step, we can enrich live adult cardiomyocytes to 93% ± 2% in a label-free manner. The cardiomyocytes maintained viability immediately after separation and upon 24 h in culture.
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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 | 0.000 |
| 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 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".