Live animal myelin histomorphometry of the spinal cord with video-rate multimodal nonlinear microendoscopy
Bibliographic record
Abstract
In vivo imaging of cellular dynamics can be dramatically enabling to understand the pathophysiology of nervous system diseases. To fully exploit the power of this approach, the main challenges have been to minimize invasiveness and maximize the number of concurrent optical signals that can be combined to probe the interplay between multiple cellular processes. Label-free coherent anti-Stokes Raman scattering (CARS) microscopy, for example, can be used to follow demyelination in neurodegenerative diseases or after trauma, but myelin imaging alone is not sufficient to understand the complex sequence of events that leads to the appearance of lesions in the white matter. A commercially available microendoscope is used here to achieve minimally invasive, video-rate multimodal nonlinear imaging of cellular processes in live mouse spinal cord. The system allows for simultaneous CARS imaging of myelin sheaths and two-photon excitation fluorescence microendoscopy of microglial cells and axons. Morphometric data extraction at high spatial resolution is also described, with a technique for reducing motion-related imaging artifacts. Despite its small diameter, the microendoscope enables high speed multimodal imaging over wide areas of tissue, yet at resolution sufficient to quantify subtle differences in myelin thickness and microglial motility.
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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 | 0.001 | 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) | 0.002 | 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".