Complex Permittivity Measurement as a New Noninvasive Tool for Monitoring In Vitro Tissue Engineering and Cell Signature Through the Detection of Cell Proliferation, Differentiation, and Pretissue Formation
Bibliographic record
Abstract
In in vitro tissue engineering, microporous scaffolds are commonly used to promote cell proliferation and differentiation in three-dimensional structures. Classic measurement methods are particularly time consuming, difficult to handle, and destructive. In this study, a new nondestructive method based on complex permittivity measurement (CPM) is proposed to monitor and track the osteoblast and macrophage differentiation through their morphological variation upon cell attachment and proliferation inside the microporous scaffolds. CPM is performed using a vector network analyzer and a dielectric probe under sterile conditions in a laminar-flow hood. A suitable effective medium approximation (EMA) is applied to fit the data in order to extract the parameters of the different constituents. Our data show that the EMA depolarization factor can be monitored to assess the variation of cell morphology characterizing cell attachment. Discrimination between two batches of scaffolds seeded, respectively, with 2 million and 1 million osteoblast cells is possible; the ratio of their CPM-derived cell volume fractions is in agreement with the ratio of their cell seeding numbers. In addition, cell proliferation inside scaffolds seeded with osteoblasts cultured in alpha minimum essential medium and inside scaffolds seeded with osteoblasts cultured in alpha minimum essential medium supplemented to induce the formation of extracellular matrix is monitored via CPM over several days. CPM-determined cell volume fraction is compared to DNA assay cell counts. Extracellular matrix formation and cell presence was confirmed by scanning electron microscopy. A set of three signature parameters (epsilon'mem, epsilon'cyt, kappa'cyt) characteristic of cell line is extracted from CPM. Distinct signatures are recorded for osteoblasts and macrophages, thus confirming the ability of CPM to discriminate between different cell types. This study demonstrates the potential of CPM as a diagnostic tool to monitor quickly and noninvasively cell growth and differentiation inside microporous scaffolds. Our findings suggest that the use of CPM could be extended to many biomedical applications, such as drug detection and automation of tissue and bacterial cultures in bioreactors.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".