Chitotriosidase as a novel biomarker of early atherosclerosis in hemodialysis patients
Bibliographic record
Abstract
INTRODUCTION: Increasing evidence suggests that inflammation and increased macrophage activity have a central role in pathogenesis of atherosclerosis. It is shown that chitotriosidase (CHIT-1) is a marker of macrophage activity in atherosclerotic plaque, and is found associated with severity of atherosclerotic lesion. There is no data about CHIT-1 activity of hemodialysis patients in the literature. Thus, we hypothesized that in hemodialysis patients, CHIT-1 levels might be a novel biomarker in early atherosclerosis. METHODS: Forty-five hemodialysis patients were included in the study (age: 61.93 ± 13.34). Intima media thickness (IMT) was evaluated with high-resolution B-mode ultrasonography. Biomarker levels were measured in serum of patients. FINDINGS: We found positive correlation among IMT, age (R: 0.426, P: 0.004) and, CHIT-1 value (R: 0.462, P: 0.001) in spearman correlation analysis. When age, CRP, creatinine, P, Alb, CHIT-1 were chosen as measures that can effect IMT in multiple regression model, IMT level was related with CHIT-1 (Beta: 0,396, P: 0.012) and age (Beta: 0,313 P: 0,048) independently. DISCUSSION: In conclusion, this is the first report showing that serum CHIT-1 level was related independently with carotid IMT in hemodialysis patients. This biomarker might have an unknown role in the development of atherosclerosis during uremia.
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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 | 0.001 | 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".