Microfabricated System for Parallel Single-Cell Capillary Electrophoresis
Bibliographic record
Abstract
Performing single-cell electrophoresis separations using multiple parallel microchannels offers the possibility of both increasing throughput and eliminating cross-contamination between different separations. The instrumentation for such a system requires spatial and temporal control of both single-cell selection and lysis. To address these problems, a compact platform is presented for single-cell capillary electrophoresis in parallel microchannels that combines optical tweezers for cell selection and electromechanical lysis. Calcein-labeled acute myloid leukemia (AML) cells were selected from an on-chip reservoir and transported by optical tweezers to one of four parallel microfluidic channels. Each channel entrance was manufactured by F2-laser ablation to form a 20- to 10-microm tapered lysis reservoir, creating an injector geometry effective in confining the cellular contents during mechanical shearing of the cell at the 10-microm capillary entrance. The contents of individual cells were simultaneously injected into parallel channels resulting in electrophoretic separation as recorded by laser-induced fluorescence of the labeled cellular contents.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".