Kidney dysfunction induced by protein overload nephropathy reduces serum sulfatide levels in mice
Bibliographic record
Abstract
We recently proposed serum sulfatides as a novel biomarker for cardiovascular disease in patients with end-stage renal failure (ESRF), based on the possible antithrombotic properties of this molecule. In this earlier study, the level of serum sulfatides was gradually decreased in parallel with kidney dysfunction; however the precise mechanism underlying this decrease was unknown. The aim of the present study was to investigate the mechanism underlying the decrease in serum sulfatide levels caused by kidney dysfunction in an experimental animal model. To produce a kidney dysfunction animal model, we prepared a mouse model of protein overload nephropathy. Using high-throughput analysis combined with matrix-assisted laser desorption ionisation time-of-flight mass spectrometry, we measured the levels of sulfatides in the sera, livers, small intestines and kidneys of protein overload nephropathy mice. As the disease progressed, the levels of sulfatides in sera decreased. Also, the levels in livers and small intestines decreased in a similar manner to those in sera, to approximately 60% of the original levels. On the contrary, those in kidneys increased by approximately 1.4-fold. Our results indicate that kidney dysfunction affects the levels of sulfatides in lipoprotein-producing organs, such as livers and small intestines, and lowers the levels of sulfatides in sera.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".