Effects of <scp>l</scp>‐carnitine supplement on serum inflammatory cytokines, C‐reactive protein, lipoprotein (a), and oxidative stress in hemodialysis patients with Lp (a) hyperlipoproteinemia
Bibliographic record
Abstract
Inflammation, oxidative stress, and high concentration of serum lipoprotein (a) [Lp (a)] are common complications in hemodialysis patients. The present study was designed to investigate the effects of L-carnitine supplement on serum inflammatory cytokines, C-reactive protein (CRP), Lp (a), and oxidative stress in hemodialysis patients with Lp (a) hyperlipoproteinemia [hyper Lp (a)]. This was an unblinded, randomized clinical trial. Thirty-six hyper Lp (a) hemodialysis patients (23 men and 13 women) were randomly assigned to either a carnitine or control group. Patients in the carnitine group received 1000 mg/d oral L-carnitine for 12 weeks, whereas patients in the control group did not receive any L-carnitine supplement. At baseline and the end of week 12, 5 mL of blood were collected after a 12- to 14-hours fast and serum free carnitine, CRP, interleukin-1β, interleukin-6 (IL-6), tumor necrosis factor-α, Lp (a), and oxidized low-density lipoprotein were measured. Serum free carnitine concentration increased significantly by 86% in the carnitine group at the end of week 12 compared with baseline (P<0.001), while serum CRP and IL-6 showed a significant decrease of 29% (P<0.05) and 61% (P<0.001), respectively. No significant changes were observed in serum free carnitine, CRP, and IL-6 in the control group. There were no significant differences between the two groups in mean changes of serum interleukin-1β, tumor necrosis factor-α, Lp (a), and oxidized low-density lipoprotein concentrations. L-carnitine supplement reduces inflammation in hemodialysis patients, but has no effect on hyper Lp (a) and oxidative stress.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".