Serum sulfatide abnormality is associated with increased oxidative stress in hemodialysis patients
Bibliographic record
Abstract
Sulfatides are major glycosphingolipids of lipoproteins that influence atherosclerosis and blood coagulation. Our previous cross-sectional study of hemodialysis patients showed that serum sulfatide levels decreased markedly with increasing duration of hemodialysis treatment, which may contribute to the development of cardiovascular disease. However, this past study could not demonstrate the time-dependent change in serum sulfatide levels in each patient, and the underlying mechanism is unknown. To confirm the time-dependent aggravation of serum sulfatide abnormality, 95 stable hemodialysis outpatients were followed up for 3 years. To show the underlying mechanisms, we statistically analyzed correlations between serum sulfatide levels and clinical factors, including an oxidative stress marker, malondialdehyde. Serum sulfatides were quantified by mass spectrometry after conversion to lysosulfatides. Malondialdehyde was measured using a colorimetric assay. The results showed a time-dependent decrease in serum sulfatide levels associated with increased malondialdehyde levels, although the absolute level of serum malondialdehyde does not determine the baseline level of serum sulfatides. Multiple linear regression analysis showed a significant correlation only between the time-dependent change in serum sulfatide levels and the time-dependent change in serum malondialdehyde levels. This study demonstrated, for the first time, a time-dependent aggravation of serum sulfatide abnormality in hemodialysis patients, as well as the potential relationship between serum sulfatide abnormality and increasing oxidative stress. These findings suggest that oxidative stress might be an aggravating factor in serum sulfatide abnormality. As continuation of hemodialysis treatment hardly improves abnormal serum sulfatide levels or increased oxidative stress, development of novel therapeutic strategies may be important.
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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 | 0.001 | 0.001 |
| 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".