Lab-on-Chip platforms: enabling technology for label-free detection, separation, patterning and «in vitro» culture of cells
Bibliographic record
Abstract
Biomedical devices developed for detection, sorting and in vitro culture of cells are important tools in both clinical diagnostics and fundamental research. Recently, with the advances in miniaturization, Lab-on-Chip (LOC) devices have started to play an important role in detection and enrichment of rare cells. Since they do not alter the properties of target cells, label-free methods are a suitable option for cell separation. This dissertation focused on two main label-free approaches, namely adhesion-based and size-based and several novel microchips were introduced for separation of target cells using both size-based and adhesion-based approaches. The first device a multilayered, fully thermoplastic-based microfluidic chip was designed and fabricated for high-throughput size-based separation of micro/nano particles and cells. High-throughput (100 μl/min) separation of micro/nano particles and rare primary cells, with greater than 95% separation efficiency, was successfully demonstrated (Chapters 4 and 5). The second series of microchips were based on adhesion-based separation; multiplex covalently attached microarrays and gradients of biomolecules were produced and embedded inside a single microfluidic chip (chapters 7 and 8). The developed bio-functional interfaces were embedded in a multi-purpose adhesion-based microchip to simultaneously capture, separate, pattern and culture primary and rare cells in vitro. Using this chip, oligodendrocyte progenitor cells and cardiomyocytes were successfully separated from rat brain and heart tissues, respectively with greater than 95% separation efficiency in 10min (Chapter 9). Separation of two dissimilar primary cells, in terms of biological properties and initial population, demonstrated the universality of the developed chip towards efficient cell separation. More importantly separated cells can be cultured on the same chip for different subsequent applications such as proliferation for cell therapy or drug testing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".