Single living cell manipulation and identification using microsystems technologies
Bibliographic record
Abstract
Abstract The paper presents the principles and the results of the implementation of dielectrophoresis for separation and identification of rare cells such as circulation tumor cells (CTCs) from diluted blood specimens in media and further label-free identification of the origins of separated cells using radio-frequency (RF) imaging. The separation and the identification units use same fabrication methods which enable system integration on the same platform. The designs use the advantage of higher surface volume ratio which represents the particular feature for micro- and nanotechnologies. Diluted blood in solution of sucrose–dextrose 1–10 is used for cell separation that yields more than 95.3% efficiency. For enhanced sensitivity in identification, RF imaging is performed in 3.5–1 solution of glycerol and trypsin. Resonance cavity performance method is used to determine the constant permittivity of the cell lines. The results illustrated by the signature of specific cells subjected to RF imaging suggest a reliable label-free single cell detection method for identification of the type of CTC.
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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 | 0.001 | 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".