The Microfluidic Trapping of Antibody-Secreting Cells
Bibliographic record
Abstract
Therapeutic antibodies (Abs) are a rapidly growing and economically promising biotechnological research area. Therapeutic Ab production typically involves screening large numbers of Ab-secreting cells (ASCs) in order to identify those producing Abs targeting a specific antigen (Ag) with the highest affinity; a process often requiring weeks to complete. We are contributing to a multidisciplinary project focused upon the development of an immunobiosensing array ultimately intended to directly monitor the Ag-specific Ab production by thousands of ASCs on a single slide in real-time. Each ASC shall be microfluidically guided and trapped near a surface plasmon (SP) resonant nanohole array sensor so as to detect the binding of secreted Abs to Ag immobilized onto the sensor’s surface. This paper presents the initial progress of our contribution to this project: the development of polymeric microfluidic devices to guide and trap large ASC populations within arrays of single-cell traps. More specifically, this paper presents several different polymer-based microfluidic trapping devices, based upon perfusive flow-through cell traps and microwells which trap settling cells, which have been evaluated using COMSOL® simulations and tested using microsphere- and cell-based flow experiments. Our initial results are promising, and verify the functionality of our microfluidic cell trap designs.
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.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".