Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells
Bibliographic record
Abstract
This paper presents a microfluidic system for cell type classification using mechanical and electrical measurements on single cells. Cells are aspirated continuously through a constriction channel with cell elongations and impedance profiles measured simultaneously. The cell transit time through the constriction channel and the impedance amplitude ratio are quantified as cell's mechanical and electrical property indicators. The microfluidic device and measurement system were used to characterize osteoblasts (n=206) and osteocytes (n=217), revealing that osteoblasts, compared with osteocytes, have a larger cell elongation length (64.51 ± 14.98 μm vs. 39.78 ± 7.16 μm), a longer transit time (1.84 ± 1.48 s vs. 0.94 ± 1.07 s), and a higher impedance amplitude ratio (1.198 ± 0.071 vs. 1.099 ± 0.038). Pattern recognition using the neural network was applied to cell type classification, resulting in classification success rates of 69.8% (transit time alone), 85.3% (impedance amplitude ratio alone), and 93.7% (both transit time and impedance amplitude ratio as input to neural network) for osteoblasts and osteocytes. The system was also applied to test EMT6 (n=747) and EMT6/AR1.0 cells (n=770, EMT6 treated by doxorubicin) that have a comparable size distribution (cell elongation length: 51.47 ± 11.33 μm vs. 50.09 ± 9.70 μm). The effects of cell size on transit time and impedance amplitude ratio were investigated. Cell classification success rates were 51.3% (cell elongation alone), 57.5% (transit time alone), 59.6% (impedance amplitude ratio alone), and 70.2% (both transit time and impedance amplitude ratio). These preliminary results suggest that biomechanical and bioelectrical parameters, when used in combination, could provide a higher cell classification success rate than using electrical or mechanical parameter alone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".