Optical micromanipulation and analysis of single cells on a microchip platform
Bibliographic record
Abstract
Ongoing efforts to engineer a system capable of selecting and labeling single cells using optical micromanipulation tools and performing electrophoretic separation on the contents of a single cell using the 'lab-on-a-chip' format are presented. At the heart of this design, are channels with 10micrometers diameter cross-sections, etched using a molecular fluorine laser. Individual cells are moved on the microchip using optical tweezers. These single cells are brought into contact with a liposome containing fluorescent tags. The liposome and cell are fused using optical scissors; resulting in a cell with labeled components. This cell is lysed using the optical scissors, and high voltage is applied to separate the contents. This design will allow us to directly look at protein and mRNA expression from a single cell without amplifying the contents of interest, as well as to obtain the population averages and their variations from the analysis of a sufficient number of individual cells.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".