Circulating Endothelial Cells as Potential Markers of Atherosclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Vascular endothelial cell (VEC) injury represents a major initiating step in the process of atherosclerosis, which may lead to cerebral infarction. "Circulating endothelial cell" (CEC) is an index of ongoing endothelial injury, while intimal-medial thickness (IMT) detected by sounography was used to evaluate the severity of atherosclerosis. However, to our knowledge, there is no study that investigated the relationship of these two determinations. Our study was designed to address correlate CEC with IMT. METHODS AND RESULTS: The study population consisted of 30 patients with acute cerebral infarction (ACI) and 30 age- and sex-matched volunteers as controls. The CEC counts were determined using Hladovec's method. All subjects underwent a 2-dimensional ultrasound examination of both carotid arteries to measure IMT. CEC counts in ACI group were significantly increased compared with control group (4.88+/-2.14 cells /0.91 microl vs 2.73+/-1.95/0.9 microl, P<0.01); IMT in ACI patients was also significantly thicker compared with volunteers (2.72+/-1.07 mm vs 1.73+/-0.99 mm, P<0.01). There was positive correlation between CEC counts and maximal carotid artery IMT in both groups (r=0.522, P<0.01 in ACI patients and r=0.395, P<0.05 in healthy volunteers). CONCLUSIONS: Circulating endothelial cell counts can directly reflect the vascular injury. CEC counts parallel IMT. The CEC may be an independent predictor of cerebral infarction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".