Towards on‐line monitoring of cell growth in microporous scaffolds: Utilization and interpretation of complex permittivity measurements
Bibliographic record
Abstract
Here we demonstrate the ability to characterize microporous scaffolds and evaluate cell concentration variation via the utilization and interpretation of complex permittivity measurements (CP), a direct and nondestructive method. Polymer-based microporous scaffolds are of importance to tissue engineering, particularly in the promotion of cell adhesion, proliferation, and differentiation in predefined shapes. Chitosan gel scaffolds were seeded with increasing concentrations of macrophages to simulate cell growth. Complex permittivity measurements were performed using a dielectric probe and a vector network analyzer over a frequency ranging from 200 MHz to 2 GHz. An effective medium theory was applied to interpret the data obtained; respectively, Looyenga and Maxwell-Wagner-Hanai functions were used to retrieve the porosity and the variation of the cell concentration from the CP measurements. Calculated porosities were in agreement with experimental evaluation-porosity ranged from 81-96%. Changes in cell concentration inside the scaffolds upon injection of differing cell concentrations into the scaffold were detected distinguishably. Variations resulting from the cumulative injection of 400-1800 microL of 10(6) cells/mL solution into the scaffold were monitored. Results suggest that CP measurements in combination with an appropriate effective medium approximation can enable on-line monitoring of cell growth within scaffolds.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".