Lipid order degradation in autoimmune demyelination probed by polarization resolved coherent Raman microscopy
Bibliographic record
Abstract
Myelin around axons is currently widely studied by structural analyses and large scale imaging techniques, with the goal to decipher its critical role in neuronal protection. While there is strong evidence that in myelin, lipid composition and lipid membrane morphology are affected during the progression of neurodegenerative diseases, there is no quantitative method yet to report its ultrastructure in tissues at both molecular and macroscopic levels, in conditions potentially compatible with in vivo observations. In this work, we study and quantify molecular order of lipids in myelin at sub-diffraction scales, using label-free polarization resolved Coherent Anti Stokes Raman (PR-CARS), which exploits CARS sensitivity to coupling between light polarization and oriented molecular vibrational bonds. Importantly, the method does not use any a priori parameters in the sample such as lipid type, orientational organization and composition. We show that lipid molecular order of myelin in the mouse spinal cord is significantly reduced throughout the progression of experimental autoimmune encephalomyelitis (EAE), a model for multiple sclerosis, even in myelin regions that appear morphologically unaffected. This technique permits to unravel molecular-scale perturbations of lipid layers at early stage of the demyelination progression, while the membrane architecture at the mesoscopic scale (here about 100 nm) seems much less affected. Such information cannot be brought by pure morphological observation and opens new prospectives towards molecular-scale understanding of neurodegenerative diseases.
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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 | 0.000 | 0.000 |
| 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".