Circulating Endothelial Progenitor Cells and Age-Related White Matter Changes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The objective was to evaluate the relationship between circulating endothelial progenitor cells (EPC) and age-related white matter changes (ARWMC). Endothelial dysfunction plays a role in the development of ARWMC. EPC incorporate into sites endothelial damage and are thought to be involved in the repair of vascular risk factor induced endothelial injury. ARWMC can be evaluated using CT or MRI. METHODS: In 172 individuals, circulating EPC were defined by the surface markers CD31 and von Willebrand factor. ARWMC were rated on CT scan using the ARWMC scale and divided into 3 groups based on ARWMC scale score (ARWMC score 0 [none], score 1-10 [mild-to-moderate], score >10 [severe]). Severity of ARWMC was correlated with levels of EPC and vascular risk factors. RESULTS: On univariate analysis, EPC were found to be significantly lower in patients with severe ARWMC (P=0.01). ARWMC were also associated with hypertension (P<0.001), age (P<0.001), creatinine clearance (P=0.031), C-reactive protein (P<0.001), and use of angiotensin-converting enzyme or angiotensin receptor blocker (P=0.004). Multiple logistic regression analysis identified EPC level, age, hypertension, and hypertriglyceridemia as significant independent predictors of severe ARWMC. CONCLUSIONS: Levels of circulating EPC were significantly lower in patients with severe ARWMC. Other variables significantly associated with severe ARWMC were age, hypertension, and hypertriglyceridemia. Further study is required to delineate the pathophysiological relationship between EPC, vascular risk factors, and ARWMC.
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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 | 0.001 |
| 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 | 0.000 |
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".