Relative risk for cardiovascular morbidity in hemodialysis patients regarding gene polymorphism for IL-10, IL-6, and TNF
Bibliographic record
Abstract
Uremia-related inflammation is prone to be a key factor to explain high cardiovascular morbidity in hemodialysis patients. Genetic susceptibility may be of importance, including IL-10, IL-6, and TNF. The aim was to analyze IL-10, IL-6, and TNF gene polymorphisms in a group of hemodialysis patients and to correlate the findings with cardiovascular morbidity. This study included 169 patients on regular hemodialysis at Zvezdara University Medical Center. Gene polymorphisms for IL-10, IL-6 and TNF were determined using PCR. These findings were correlated with the cardiovascular morbidity data from patient histories. Heterozygots for IL-10 gene showed significantly lower incidence of cardiovascular events (p = 0.05) and twice lower risk for development of myocardial infarction, but experienced twice higher risk for left ventricular hypertrophy. Regarding TNF gene polymorphism, patients with A allele had 1.5-fold higher risk for cerebrovascular accident and cardiovascular events and 2-fold higher risk for hypertension and peripheral vascular disease. Patients with G allele of IL-6 gene experienced 1.5-fold higher risks for cerebrovascular accident. We need studies with larger number of patients for definitive conclusion about the influence of gene polymorphisms on cardiovascular morbidity in hemodialysis patients and its importance in everyday clinical practice.
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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 | 0.000 | 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) | 0.002 | 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".