Investigation of human multiple sclerosis lesions using high resolution spectrally unmixed CARS microscopy
Bibliographic record
Abstract
The pathology of multiple sclerosis (MS) involves both the gray and white matter regions of the brain and spinal cord. It is characterized by various combinations of demyelination, inflammatory infiltration, axonal degeneration, and later gliosis in chronic lesions. While acute and chronic white matter plaques are well characterized and easily identified, evidence indicates that the CNS of MS patients may be globally altered, with subtle abnormalities found in grossly normal appearing white matter (NAWM) and in diffusely abnormal white matter (DAWM) where histochemical stains and advanced magnetic resonance imaging indicate altered tissue composition. Thus, the prototypical acute inflammatory lesion may merely represent the most obvious manifestation of a chronic widespread involvement of the CNS, which is difficult to examine reliably. The current study deals with the microstructure and biochemistry of demyelination, remyelination and axonal loss in various regions of post-mortem human MS brain, including NAWM, areas of remyelination and more typical acute and chronic lesions. The myelin sheath, neuroglia and perivascular spaces were investigated using a novel Coherent Anti-Stokes Raman Scattering (CARS) microscope with simultaneous Two-Photon Excited Fluorescence (TPEF) imaging. The active CH stretching region between ~ 2800 and 3000 cm-1 was probed to provide chemically specific, high resolution, label-free imaging pertaining to the progression of the disease. CARS data were correlated with TPEF and conventional histochemical and immunohistochemical stains. Our novel CARS microscopy system provides detailed morphological and biochemical information regarding CNS pathology in MS and that may be applicable to a broad range of other human brain and spinal cord disorders.
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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) | 0.000 | 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".