Comprehensive Evaluation of Peripheral Nerve Regeneration in the Acute Healing Phase Using Tissue Clearing and Optical Microscopy in a Rodent Model
Bibliographic record
Abstract
Peripheral nerve injury (PNI), a common injury in both the civilian and military arenas, is usually associated with high healthcare costs and with patients enduring slow recovery times, diminished quality of life, and potential long-term disability. Patients with PNI typically undergo complex interventions but the factors that govern optimal response are not fully characterized. A fundamental understanding of the cellular and tissue-level events in the immediate postoperative period is essential for improving treatment and optimizing repair. Here, we demonstrate a comprehensive imaging approach to evaluate peripheral nerve axonal regeneration in a rodent PNI model using a tissue clearing method to improve depth penetration while preserving neural architecture. Sciatic nerve transaction and end-to-end repair were performed in both wild type and thy-1 GFP rats. The nerves were harvested at time points after repair before undergoing whole mount immunofluorescence staining and tissue clearing. By increasing the optic depth penetration, tissue clearing allowed the visualization and evaluation of Wallerian degeneration and nerve regrowth throughout entire sciatic nerves with subcellular resolution. The tissue clearing protocol did not affect immunofluorescence labeling and no observable decrease in the fluorescence signal was observed. Large-area, high-resolution tissue volumes could be quantified to provide structural and connectivity information not available from current gold-standard approaches for evaluating axonal regeneration following PNI. The results are suggestive of observed behavioral recovery in vivo after neurorrhaphy, providing a method of evaluating axonal regeneration following repair that can serve as an adjunct to current standard outcomes measurements. This study demonstrates that tissue clearing following whole mount immunofluorescence staining enables the complete visualization and quantitative evaluation of axons throughout nerves in a PNI model. The methods developed in this study could advance PNI research allowing both researchers and clinicians to further understand the individual events of axonal degeneration and regeneration on a multifaceted level.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".