In Vivo Optical Imaging of Myelination Events in a Myelin Basic Protein Promoter-Driven Luciferase Transgenic Mouse Model
Bibliographic record
Abstract
The compact myelin sheath is important for axonal function, and its loss can lead to neuronal cell death and irreversible functional deficits. Myelin is vulnerable to a variety of metabolic, toxic, and autoimmune insults. In diseases like multiple sclerosis, there is currently no therapy to stop myelin loss, underscoring the need for neuroprotective and remyelinating therapies. Noninvasive, robust techniques are also needed to confirm the effect of such therapies in animal models. This article describes the generation, characterization, and potential uses for a myelin basic protein-luciferase (MBP-luci) transgenic mouse model, in which the firefly luciferase reporter gene is selectively controlled by the MBP promoter. In vivo bioluminescence imaging can be used to visualize and quantify demyelination and remyelination at the transcriptional level, noninvasively, and in real time. Transgenic mice were assessed in the cuprizone-induced model of demyelination, and luciferase activity highly correlated with demyelination and remyelination events as confirmed by both magnetic resonance imaging and postmortem histological analysis. Furthermore, MBP-luci mice demonstrated enhanced luciferase signal and remyelination in the cuprizone model after treatment with a peroxisome proliferator activated receptor-delta selective agonist and quetiapine. Imaging sensitivity was further enhanced by using CycLuc 1, a luciferase substrate, which has greater blood-brain barrier penetration. We demonstrated the utility of MBP-luci model in tracking myelin changes in real time and supporting target and therapeutic validation efforts.
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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 | 0.001 | 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 | 0.001 |
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".