Investigating regional lipid expression profiles in post-mortem Alzheimer's disease brain tissue using MALDI-IMS
Bibliographic record
Abstract
Alzheimer's disease (AD) is a debilitating and progressive neurodegenerative condition that accounts for the vast majority of dementia diagnoses annually. Several brain regions have been implicated in the neuropathology of AD although the mechanisms of AD progression are poorly understood. The majority of pathological studies have focused on proteins, whereas perturbations in lipid expression within the AD brain are relatively under described. Lipids are the primary structural component of cell membranes, key players in neuroprotective and apoptotic pathways; and can even stimulate or inhibit transmembrane protein pumps. Profiling lipid expression may prove critical to understanding the complex underlying neurodegenerative mechanisms that comprise AD. Previously, detection of lipids in AD brain tissue has been limited by a lack of analytical imaging techniques capable of detecting complex lipid species and a need for fresh flash frozen tissue for mass spectrometry analysis. In this study we utilize a protocol developed in our lab to profile lipid expression in situ using matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). For the first time, we present the lipid profile of several neuroanatomical regions critically implicated in AD pathology including the entorhinal cortex, nucleus basalis of Meynert, hippocampus, periventricular white matter and subcortical U-fibres in post-mortem AD and non-AD brains. This data will support an ongoing effort to better understand the underlying role of lipid dysregulation in AD pathogenesis.
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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 | 0.001 | 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 | 0.001 |
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".