The interleukin‐10 promoter genotype predicts diastolic dysfunction in maintenance hemodialysis patients
Bibliographic record
Abstract
Interleukin-10 (IL-10) predominantly acts as an anti-inflammatory factor. Polymorphisms in the IL-10 gene promoter determine quantitative cytokine production. Doppler echocardiography and tissue Doppler imaging (TDI) are superior to conventional echocardiography to evaluate diastolic dysfunction. The IL-10 gene promoter polymorphism at position (-1082) was studied for its association with conventional and Doppler echocardiographic and TDI parameters in 112 hemodialysis (HD) patients. Blood pressure, serum C-reactive protein (CRP), and albumin levels were also examined for the association study. The genetic association study showed that among the HD patients, there was no difference in the prevalence of systolic and diastolic dysfunction between genotypes on conventional echocardiography. However, using Doppler echocardiography and TDI, high producers for the IL-10 -1082 promoter (-1082/GG) have higher E velocities, E/A values, lateral, and septal E' velocities and a lower isovolumic ventricular relaxation time than low (-1082/AA) and intermediate producers (-1082/GA). Significantly higher levels of serum CRP levels and lower plasma albumin levels were found in low and intermediate producers for the IL-10 -1082 promoter than high producers. The IL-10 genotype may balance the effects of inflammatory cytokines on the myocardium and may be a determinant of LV function in HD patients.
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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 | 0.002 |
| 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) | 0.001 | 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".