Dynamic pH fluctuations in cancer cells on CMOS-based Lab-on-Chip ISFET arrays
Bibliographic record
Abstract
Early diagnosis of cancer is a key avenue to improving cancer incidence rates and prognosis. This paper presents a rapid assay for live, mammalian cancer cell culture on a Lab-on-Chip ISFET array fabricated in CMOS technology. Detection of osteosarcoma cells is recorded as a significant difference in endpoint $\mathbf{p H}$ on ISFET sensors. Control measurements of $\mathbf{p H}$ change in cell culture media due to any CO2supply fluctuations in an incubator allow compensation of non-ideal effects including sensor drift and baseline $\mathbf{p H}$ change associated with a bicarbonate buffering system. Distinguishing metrics, including total drift rate and standard deviation of sensor output are extracted and attributed to cell presence on-chip. Mean total drift rate shows an 89.7% difference between sample and control wells, aiding characterisation of cancer cell presence. Standard deviation captures the noise output attributed to cell culture on the sensor surface, reporting an $\mathbf{8 1. 4 \%}$ difference between populations. This Point-of-Care diagnostic device and assay for rapid cell detection on a Lab-on-Chip shows significant potential to provide a rapid and cost-effective diagnostic tool for live cell measurements.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".