Automatic detecting and counting magnetic beads‐labeled target cells from a suspension in a microfluidic chip
Bibliographic record
Abstract
A novel microfluidic method of continually detecting and counting beads-labeled cells from a cell mixture without fluorescence labeling was presented in this paper. The detection system is composed of a microfluidic chip (with a permanent magnet inserted along the channel), a signal amplification circuit, and a LabView® based data acquisition device. The microfluidic chip can be functionally divided into separation zone and detection zone. By flowing the pre-labeled sample solution, the target cells will be sequentially separated at the separation zone by the permanent magnet and detected and counted at the detection zone by a microfluidic resistive pulse sensor. Experiments of positive separation and detection of T-lymphocytes and negative separation and detection of cancer cells from the whole blood samples were carried out to demonstrate the effectiveness of this method. The methodology of utilizing size difference between magnetic beads and cell-magnetic beads complex for beads-labeled cell detection is simple, automatic, and particularly suitable for beads-based immunoassay without using fluorescence labeling.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".