A Brief Review of Visualization Techniques for Nerve Tissue Engineering Applications
Bibliographic record
Abstract
In nerve tissue engineering, scaffolds act as carriers for cells and biochemical factors and as constructs providing appropriate mechanical conditions. During nerve regeneration, new tissue grows into the scaffolds, which degrade gradually. To optimize this process, researchers must study and analyze various morphological and structural features of the scaffolds, the ingrowth of nerve tissue, and scaffold degradation. Therefore, visualization of the scaffolds as well as the generated nerve tissue is essential, yet challenging Visualization techniques currently used in nerve tissue engineering include electron microscopy, confocal laser scanning microscopy (CLSM), and micro-computed tomography (micro-CT or μCT). Synchrotron-based micro-CT (SRμCT) is an emerging and promising technique, drawing considerable recent attention. Here, we review typical applications of these visualization techniques in nerve tissue engineering. The promise, feasibility, and challenges of SRμCT as a visualization technique applied to nerve tissue engineering are also discussed.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".