Is There Any Relationship between Serum Levels of IL‐10 and Atherosclerosis in Hemodialysis Patients?
Bibliographic record
Abstract
Background: Cardiovascular complications due to atherosclerosis (AS) are the major cause of mortality in hemodialysis (HD) patients. Inflammation may play an important role in the development of AS. Several studies have demonstrated the association of acute‐phase proteins and cytokines with AS in the general population and in HD patients. Interleukin‐10 (IL‐10) is an anti‐inflammatory cytokine. The aim of study was to compare serum levels inflammatory and anti‐inflammatory indicators in HD patients according to the presence or absence of AS. Methods: Thirty‐three HD patients were enrolled. AS was defined as the detection of plaques by Doppler ultrasonography. The patients were subgrouped according to the presence or absence of plaques. Serum levels of IL‐1, IL‐2, IL‐6, IL‐10, C‐reactive protein (CRP) and tumor necrosis factor‐α (TNF‐α) were measured. The factors for AS such as age, gender, hypertension, hyperlipidemia, and HD duration were also evaluated. Results: We found that the patients with AS had significantly higher hs‐CRP and lower IL‐10. Blood pressure values were also increased in patients with AS. Additionally, there was an increased correlation between CRP and IL‐10. Conclusions: AS(+) patients undergoing HD had low serum levels of anti‐inflammatory cytokine IL‐10 and high serum levels of hs‐CRP. These results may suggest that the limitation of anti‐inflammatory response in atherosclerotic uremic patients is a triggering or contributing factor for AS.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".