Pre‐ and Post Hemodialysis Procalcitonin Levels and Their Relationships with Immunoregulatory, Proinflammatory Cytokines in Chronic Hemodialysis Patients
Bibliographic record
Abstract
Background/Aims: Procalcitonin (PCT) has been described as a new marker of severe infection and sepsis. In this study, we investigated the serum levels of PCT during the hemodialysis (HD) in chronically hemodialyzed patients and whether the PCT levels were correlated with other cytokines. Methods: We measured pre‐ and post‐HD PCT, interleukin (IL‐1), IL‐2, IL‐6, IL‐10, tumor necrosis factor‐α (TNF‐α) concentrations in 24 stable patients undergoing chronic HD [11 males and 13 females; age 41.2 ± 18.0 years, 12 h/week, with a Kt/V of 1.41 ± 0.35, polysynthane (PSN) membrane being used in all patients, without reuse]. Pre‐ and post‐HD PCT concentrations were compared with cytokines such as IL‐1, IL‐2, IL‐6, IL‐10, TNF‐α, and clinical parameters including age, blood pressure, leukocyte, hemoglobin levels, C‐reactive protein (CRP), epoetin (EPO) doses, BUN, creatinine, parathormone (PTH), ferritin, and transferrin levels. Results: Pre‐ and post‐HD serum PCT levels were similar (0.77 ± 0.40 and 0.83 ± 0.61 ng/mL), and higher than upper normal level of 0.5 ng/mL. The levels of IL‐2 and IL‐10 decreased and the levels of IL‐1 and TNF‐α increased. Post‐HD PCT correlated with PTH, IL‐1, IL‐2, and IL‐10. Conclusion: About 60% of patients had elevated PCT levels, HD with low‐flux PSN membrane did not change serum PCT and IL‐6. While IL‐1 and TNF‐α increased, IL‐2 and IL‐10 decreased by PSN membrane during HD. So that PCT levels can be measured just after HD as do prior to start of HD. Is there any relationship between PCT and PTH? PCT may be important in uremic bone disease.
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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 | 0.001 |
| 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".