Classification of cell types using a microfluidic device for mechanical and electrical measurement on single cells
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
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.
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