Immunomagnetic T cell capture from blood for PCR analysis using microfluidic systems
Bibliographic record
Abstract
A one-step immunomagnetic separation technique was performed on a microfluidic platform for the isolation of specific cells from blood samples. The cell isolation and purification studies targeted T cells, as a model for low abundance cells (about 1:10,000 cells), with more dilute cells as the ultimate goal. T cells were successfully separated on-chip from human blood and from reconstituted blood samples. Quantitative polymerase chain reaction analysis of the captured cells was used to characterize the efficiency of T cell capture in a variety of flow path designs. Employing many (4-8), 50 microm deep narrow channels, with the same overall cross section as a single, 3 mm wide channel, was much more effective in structuring dense enough magnetic bead beds to trap cells in a flowing stream. The use of 8-multiple bifurcated flow paths increased capture efficiencies from approximately 20 up to 37%, when compared to a straight 8-way split design, indicating the value of ensuring uniform flow distribution into each channel in a flow manifold for effective cell capture. Sample flow rates of up to 3 microL min(-1) were evaluated in these capture beds.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".