The Relationship between Protein C, Protein S and Cytokines in Acute Ischemic Stroke
Bibliographic record
Abstract
OBJECTIVE: The role of inflammation in the pathogenesis of acute ischemic stroke is well known, but its association with the clinical picture is as yet unclear. MATERIAL AND METHODS: In our study, we measured the serum levels of the proinflammatory cytokines interleukin-1beta (IL-1beta), tumor necrosis factor alpha (TNFalpha) and interleukin-6 (IL-6) within the first 50 h of stroke in 60 acute stroke patients, and examined the association with the natural anticoagulants protein C and free protein S. We compared the results with a control group that consisted of 30 volunteers. We also correlated their levels with the clinical outcomes by using the Canadian Neurological Scale (CNS). RESULTS: Neither stroke patients nor the control group had any elevations in IL-1beta serum levels. However, the levels of serum IL-6 were significantly higher in stroke patients (13.7 +/- 19.46 vs. 4.3 +/- 15.88, p = 0.002). In addition, the protein S levels of patients were lower than those of the controls (84.36 +/- 27.97 vs. 95.9 +/- 25.64, p = 0.007). Although IL-6 showed negative correlation with protein S (r = -0.504, p = 0.000), the other studied cytokines TNFalpha and IL-1beta did not correlate with these natural anticoagulants. Another negative correlation was found between IL-6 and CNS scores (r = -0.451, p = 0.000). In addition, both protein C and protein S positively correlated with CNS (r = 0.263, p = 0.042; r = 0.381, p = 0.003). There was also a positive correlation between protein C and protein S (r = 0.408, p = 0.001). CONCLUSIONS: Our results suggest that TNFalpha and IL1beta serum levels are not elevated in the acute phase of stroke and have no correlation with the natural anticoagulants protein C and protein S. However, a decrease in free protein S may be related to elevated IL-6 levels. In addition, increased levels of IL-6 and reduced levels of protein C and protein S may play a role in acute ischemic stroke severity.
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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 | 0.001 |
| 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".