Biological cell trap arrays with applications to extraordinary optical transmission based immunobiosensing assays
Bibliographic record
Abstract
This thesis’ work is part of a multi-disciplinary project developing a novel immunobiosensing (IBS) platform to monitor antibody (Ab) production by specific cells trapped in a micromachined slide. This platform consists of two subsystems, the micromachined slide featuring the cell traps and the IBS slide integrated with the traps to gauge the affinity with which Ab(s) secreted by specific trapped cells bind an immobilized target antigen (Ag). This thesis’ primary contributions involve the design, fabrication, and experimental testing of multiple cell trap generations—including their integration with co-designed IBS slides. Hydrodynamic flows and sedimentation under gravity are used to trap cells, as they are gentler and require fewer components than other methods. Hydrodynamic flows that guide cells into cup-based traps in enclosed microfluidic channels are investigated. However, their enclosed nature complicates removing extraneous untrapped cells, selectively retrieving trapped cells, and device cleaning between experiments. An open system involving arrays of microwell-(MW)-based traps are used for all subsequent traps. Multiple generations of MW traps and co-designed IBS slides are presented, with each generation refined to further streamline alignment and examination via optical microscopy. Statistical analyses of the observed distribution of trapped cells in the MWs confirm that sedimentation is Poisson distributed, and further suggest that a zero-inflated Poisson (ZIP) function serves as a superior model. This thesis shows that cells can be trapped into an open array of MW traps subsequently aligned with an IBS slide to gauge the affinity with which cell-secreted Ab(s) bind a target Ag. Further refinement to the Extraordinary Optical Transmission (EOT)-based IBS slide used in this thesis is required to achieve Ab-Ag binding detection at the desired single cell/trap level and to improve the IBS slide re-usability. Integration of the traps with a different IBS subsystem is also a possibility. As potential future work, it is proposed that a micropipette needle be used to obtain a revised single cell/trap distribution prior to IBS slide integration and to retrieve the cells of interest selectively.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".