A SoC bio-analysis platform for real-time biological cell analysis-on-a-chip
Bibliographic record
Abstract
Future bio-analysis devices and systems will be heavily dependent on the micro-convergence of SoC platforms with the disparate technologies of MEMS and microfluidics. This paper describes a bio-analysis system that will be part of a future low-power bio-analysis platform being developed jointly in the ATIPS and Bioelectrics Laboratories at the University of Calgary. The analysis technique will exploit dielectrophoresis (DEP), an electrokinetic phenomenon that has demonstrated novel and noninvasive biological cell identification, interrogation and species separation capabilities. Various electrode configurations have been previously developed and implemented, each of which can manipulate cells in a specific manner, and test microstructures have been built by fabricating the electrodes using a standard CMOS process. In this paper we generalize this concept by providing a generic electrode structure, a "lexel" (electric field element) array, which, when integrated with a processor, is capable of generating an arbitrary electric field shape, thus facilitating a programmable sequence of different cell manipulations to be performed. This paper presents a proposal for the "lexel" array, a two dimensional array of discrete, independent electrodes, and discusses it's interfacing with appropriate controlling and sensing electronic components to provide flexible cell manipulation and subsequent analysis capability as part of a System-on-Chip bio-analysis platform.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".