Multimodal coherent anti-Stokes Raman scattering microscopy reveals microglia-associated myelin and axonal dysfunction in multiple sclerosis-like lesions in mice
Bibliographic record
Abstract
Myelin loss and axonal degeneration predominate in many neurological disorders; however, methods to visualize them simultaneously in live tissue are unavailable. We describe a new imaging strategy combining video rate reflectance and fluorescence confocal imaging with coherent anti-Stokes Raman scattering (CARS) microscopy tuned to CH(2) vibration of myelin lipids, applied in live tissue of animals with chronic experimental autoimmune encephalomyelitis (EAE). Our method allows monitoring over time of demyelination and neurodegeneration in brain slices with high spatial resolution and signal-to-noise ratio. Local areas of severe loss of lipid signal indicative of demyelination and loss of the reflectance signal from axons were seen in the corpus callosum and spinal cord of EAE animals. Even in myelinated areas of EAE mice, the intensity of myelin lipid signals is significantly reduced. Using heterozygous knock-in mice in which green fluorescent protein replaces the CX(3)CR1 coding sequence that labels central nervous system microglia, we find areas of activated microglia colocalized with areas of altered reflectance and CARS signals reflecting axonal injury and demyelination. Our data demonstrate the use of multimodal CARS microscopy for characterization of demyelinating and neurodegenerative pathology in a mouse model of multiple sclerosis, and further confirm the critical role of microglia in chronic inflammatory neurodegeneration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".