Evaluation of neopterin levels in patients undergoing hemodialysis
Bibliographic record
Abstract
Neopterin is a diagnostic or a prognostic biomarker for several pathologies including renal diseases. However, the association between neopterin status and causative main reasons such as diabetes and hypertension for renal disease remains unclear. The aim of the study was to evaluate neopterin levels in diabetes and hypertension patients treated with/without hemodialysis. According to primary renal disorders, the patients undergoing hemodialysis were classified into 4 groups as diabetic nephropathy, hypertensive nephropathy, reflux nephropathy or interstitial nephritis, and others. The controls consisted of healthy subjects, hypertensive subjects, and diabetic individuals without any renal disorder. In the study, both urinary and serum neopterin levels were measured using high performance liquid chromatography and enzyme-linked immunosorbant assay in patients undergoing regular hemodialysis therapy (n=71). The effects of the duration of hemodialysis and treatment of erythropoietin and/or iron on neopterin levels were also evaluated. Neopterin levels were found to be higher in hemodialysis patients than in the healthy controls (P<0.05). A significant difference in neopterin levels was also found between diabetic control patients and diabetic nephropathy patients (P<0.05). A similar significant difference was detected in neopterin levels between hypertensive patients with/without nephropathy (P<0.05). Neopterin may be an early critical marker for progression of nephropathy in diabetic and hypertensive patients in early stages.
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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".