Impact of Age on the Cerebrovascular Proteomes of Wild-Type and Tg-SwDI Mice
Bibliographic record
Abstract
The structural integrity of cerebral vessels is compromised during ageing. Abnormal amyloid (Aβ) deposition in the vasculature can accelerate age-related pathologies. The cerebrovascular response associated with ageing and microvascular Aβ deposition was defined using quantitative label-free shotgun proteomic analysis. Over 650 proteins were quantified in vessel-enriched fractions from the brains of 3 and 9 month-old wild-type (WT) and Tg-SwDI mice. Sixty-five proteins were significantly increased in older WT animals and included several basement membrane proteins (nidogen-1, basement membrane-specific heparan sulfate proteoglycan core protein, laminin subunit gamma-1 precursor and collagen alpha-2(IV) chain preproprotein). Twenty-four proteins were increased and twenty-one decreased in older Tg-SwDI mice. Of these, increases in Apolipoprotein E (APOE) and high temperature requirement serine protease-1 (HTRA1) and decreases in spliceosome and RNA-binding proteins were the most prominent. Only six shared proteins were altered in both 9-month old WT and Tg-SwDI animals. The age-related proteomic response in the cerebrovasculature was distinctly different in the presence of microvascular Aβ deposition. Proteins found differentially expressed within the WT and Tg-SwDI animals give greater insight to the mechanisms behind age-related cerebrovascular dysfunction and pathologies and may provide novel therapeutic targets.
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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) | 0.001 | 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 | 0.001 |
| 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".