Single living cell manipulation and identification using microsystems technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it