Oral Cells-On-Chip: Design, Modeling and Experimental Results
Why this work is in the frame
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Bibliographic record
Abstract
Recent advances in periodontal studies have attracted the attention of researchers to the relation between oral cells and gum diseases, which is a real threat to overall human health. Among various microfabrication technologies, Complementary Metal Oxide Semiconductors (CMOSs) enable the development of low-cost integrated sensors and circuits for rapid and accurate assessment of living cells that can be employed for the early detection and control of periodontal diseases. This paper presents a CMOS capacitive sensing platform that can be considered as an alternative for the analysis of salivatory cells such as oral neutrophils. This platform consists of two sensing electrodes connected to a read-out capacitive circuitry designed and fabricated on the same chip using Austria Mikro Systeme (AMS) 0.35 µm CMOS process. A graphical user interface (GUI) was also developed to interact with the capacitive read-out system and the computer to monitor the capacitance changes due to the presence of saliva cells on top of the chip. Thanks to the wide input dynamic range (IDR) of more than 400 femto farad (fF) and high resolution of 416 atto farad (aF), the experimental and simulation results demonstrate the functionality and applicability of the proposed sensor for monitoring cells in a small volume of 1 µL saliva samples. As per these results, the hydrophilic adhesion of oral cells on the chip varies the capacitance of interdigitated electrodes (IDEs). These capacitance changes then give an assessment of the oral cells existing in the sample. In this paper, the simulation and experimental results set a new stage for emerging sensing platforms for testing oral samples.
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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