The role of plasma angiotensin-converting enzyme and interleukin-6 levels on the prognosis of non-dialysis chronic kidney disease patients
Bibliographic record
Abstract
Context: Inflammatory factors and oxidative stress were discovered to play significant roles in the progression of chronic kidney disease (CKD). There is, however, no research on the direct impact of high plasma angiotensin converting enzyme (ACE) and interleukin (IL)-6 levels on CKD prognosis, particularly in non-hemodialysis patients. Aims: To investigate the potential role of plasma ACE and IL-6 levels in CKD prognosis. Methods: A total of 75 non-dialysis CKD patients participated in this cross-sectional study. The estimated glomerular filtration rate (e-GFR) and albuminuria were used to determine the prognosis of CKD. The plasma ACE and IL-6 levels were measured using an enzyme-linked immunoassay (ELISA). Spearman's rank correlational analysis was used to examine the relationship between ACE and IL-6 plasma levels with the prognosis of CKD. Results: The result showed a statistically significant correlation between age and plasma ACE (p = 0.038, r = 0.241), serum creatinine, and urine albumin-creatinine ratio with CKD prognosis (p<0.0001). A negative significant correlation was found between the e-GFR and CKD prognosis (p<0.0001). Additionally, there were also significant correlations between plasma ACE and IL-6 with CKD prognosis (p = 0.021, r = 0.266 and p = 0.04, r = 0.238, respectively). A significant positive correlation was also found between plasma ACE and IL-6 (p = 0.024, r = 0.260). Conclusions: There was a significant correlation between plasma ACE and IL-6 levels with CKD prognosis. Further investigation revealed a statistically significant positive relationship between plasma ACE and IL-6 levels.
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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) | 0.001 | 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".