The brain lipidomes of subcortical ischemic vascular dementia and mixed dementia
Bibliographic record
Abstract
Despite its importance as the leading cause of vascular dementia, the primary pathogenic mechanisms in subcortical ischemic vascular dementia (SIVD) have remained elusive. Because of the lack of approved therapeutic agents for SIVD, there is a pressing need to identify novel therapeutic targets. Comparative lipidomic analyses of SIVD and mixed dementia (i.e., SIVD and Alzheimer's disease, MixD) may also confer new insights pertaining to the possible interaction between neurodegenerative and vascular mechanisms in the pathogenesis of dementia. Liquid chromatography coupled to mass spectrometry was used to comprehensively analyze the lipidomes of white and gray matter from the temporal cortex of nondemented controls, SIVD, and MixD subjects. Detailed molecular profiles highlighted the pathologic relevance of gray matter sphingolipid fatty acyl chain heterogeneity in dementia. In addition, the levels of sulfatides and lysobisphosphatidic acids were progressively increased in the temporal cortex gray matter from control to SIVD to MixD. White matter phospholipid profiles indicated possible adaptive mechanisms (i.e., increased unsaturation) to chronic ischemia in SIVD and elevated membrane degradation in MixD.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".