Development of a Novel 3D Culture System for Screening Features of a Complex Implantable Device for CNS Repair
Bibliographic record
Abstract
Tubular scaffolds which incorporate a variety of micro- and nanotopographies have a wide application potential in tissue engineering especially for the repair of spinal cord injury (SCI). We aim to produce metabolically active differentiated tissues within such tubes, as it is crucially important to evaluate the biological performance of the three-dimensional (3D) scaffold and optimize the bioprocesses for tissue culture. Because of the complex 3D configuration and the presence of various topographies, it is rarely possible to observe and analyze cells within such scaffolds in situ. Thus, we aim to develop scaled down mini-chambers as simplified in vitro simulation systems, to bridge the gap between two-dimensional (2D) cell cultures on structured substrates and three-dimensional (3D) tissue culture. The mini-chambers were manipulated to systematically simulate and evaluate the influences of gravity, topography, fluid flow, and scaffold dimension on three exemplary cell models that play a role in CNS repair (i.e., cortical astrocytes, fibroblasts, and myelinating cultures) within a tubular scaffold created by rolling up a microstructured membrane. Since we use CNS myelinating cultures, we can confirm that the scaffold does not affect neural cell differentiation. It was found that heterogeneous cell distribution within the tubular constructs was caused by a combination of gravity, fluid flow, topography, and scaffold configuration, while cell survival was influenced by scaffold length, porosity, and thickness. This research demonstrates that the mini-chambers represent a viable, novel, scale down approach for the evaluation of complex 3D scaffolds as well as providing a microbioprocessing strategy for tissue engineering and the potential repair of SCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".