Imaging mass spectrometry allows for neuroanatomic-specific detection of gangliosides in the healthy and diseased brain
Bibliographic record
Abstract
Gangliosides have a wide variety of biological functions due to their location on the outer leaflet of plasma membranes. They form a critical component of membrane rafts, or ganglioside-enriched microdomains, where they influence the physical properties of the membrane as well as its function. Gangliosides can change their structure to meet their external and internal environmental demands. This ability to change structure makes gangliosides both fascinating and technologically challenging targets to identify and understand. A full understanding on how gangliosides are regulated within the central nervous system (CNS) is critical, as ganglioside dysregulation is observed in the aging brain as well as in several neurodegenerative injuries and diseases such as stroke, Alzheimer's disease, Parkinson's disease, Huntington's disease and several lysosomal storage disorders diseases, including Tay Sach's disease. Mass spectrometry (MS) has become a useful means to better understand ganglioside composition and function. Imaging mass spectrometry (IMS) provides the added benefit of placing analytical information within an anatomical context. This review article will discuss recent advances in MS-based detection methods, with a focus on IMS-based approaches to help understand the spatial-specific role gangliosides in the healthy brain as in CNS injuries and disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".