Optical Tissue Clearing Enables Rapid, Precise and Comprehensive Assessment of Three-Dimensional Morphology in Experimental Nerve Regeneration Research
Bibliographic record
Abstract
Abstract Morphological analyses are key outcome assessments for nerve regeneration studies but are historically limited to tissue sections. Novel optical tissue clearing techniques enabling three-dimensional imaging of entire organs at a subcellular resolution have revolutionized morphological studies of the brain. To extend their applicability to experimental nerve repair studies we adapted these techniques to nerves and their motor and sensory targets in rats. The solvent-based protocols rendered harvested peripheral nerves and their target organs transparent within 24 h while preserving tissue architecture and fluorescence. Optical clearing is compatible with conventional laboratory techniques, including retrograde labelling studies, and computational image segmentation, providing fast and precise cell quantitation. Further, optically cleared organs enable three-dimensional morphometry at an unprecedented scale including dermatome-wide innervation studies, tracing of intramuscular nerve branches and mapping of neurovascular networks. Given their wide-ranging applicability, rapid processing times and low costs, tissue clearing techniques are likely to be a key technology for next-generation nerve repair studies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".