A novel approach to 32-channel peripheral nervous system myelin imaging in vivo, with single axon resolution
Bibliographic record
Abstract
OBJECTIVE Intravital spectral imaging of the large, deeply situated nerves in the rat peripheral nervous system (PNS) has not been well described. Here, the authors have developed a highly stable platform for performing imaging of the tibial nerve in live rodents, thus allowing the capture of high-resolution, high-magnification spectral images requiring long acquisition times. By further exploiting the qualities of the topically applied myelin dye Nile red, this technique is capable of visualizing the detailed microenvironment of peripheral nerve demyelination injury and recovery, while allowing us to obtain images of exogenous Schwann cell myelination in a living animal. METHODS The authors caused doxorubicin-induced focal demyelination in the tibial nerves of 25 Thy-1 GFP rats, of which 2 subsets (n = 10 each) received either BFP-labeled SKP-SCs or SCs to the zone of injury. Prior to acquiring images of myelin recovery in these nerves, a tibial nerve window was constructed using a silicone hemitube, a fast drying silicone polymer, and a small coverslip. This construct was then affixed to a 3D-printed nerve stage, which in turn was affixed to an external fixation/microscope stage device. Myelin visualization was facilitated by the topical application of Nile red. RESULTS The authors reliably demonstrated intravital peripheral nerve myelin imaging with micron-level resolution and magnification, and minimal movement artifact. The detailed microenvironment of nerve remyelination can be vividly observed, while exogenously applied Schwann cells and skin-derived precursor Schwann cells can be seen myelinating axons. CONCLUSIONS Topically applied Nile red enables intravital study of myelin in the living rat PNS. Furthermore, the use of a tibial nerve window facilitates stable intravital peripheral nerve imaging, making possible high-definition spectral imaging with long acquisition times.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".