Three-dimensional cell manipulation and patterning using dielectrophoresis via a multi-layer scaffold structure
Bibliographic record
Abstract
Cell manipulation is imperative to the areas of cellular biology and tissue engineering, providing them a useful tool for patterning cells into cellular patterns for different analyses and applications. This paper presents a novel approach to perform three-dimensional (3D) cell manipulation and patterning with a multi-layer engineered scaffold. This scaffold structure employed dielectrophoresis as the non-contact mechanism to manipulate cells in the 3D domain. Through establishing electric fields via this multi-layer structure, the cells in the medium became polarized and were attracted towards the interior part of the structure, forming 3D cellular patterns. Experiments were conducted to evaluate the manipulation and the patterning processes with the proposed structure. Results show that with the presence of a voltage input, this multi-layer structure was capable of manipulating different types of biological cells examined through dielectrophoresis, enabling automatic cell patterning in the time-scale of minutes. The effects of the voltage input on the resultant cellular pattern were examined and discussed. Viability test was performed after the patterning operation and the results confirmed that majority of the cells remained viable. After 7 days of culture, 3D cellular patterns were observed through SEM. The results suggest that this scaffold and its automated dielectrophoresis-based patterning mechanism can be used to construct artificial tissues for various tissue engineering applications.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".