Urinary protein profiling with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry in ET<sub>B</sub>receptor-deficient ratsThis article is one of a selection of papers published in the special issue (part 2 of 2) on Forefronts in Endothelin.
Bibliographic record
Abstract
The pathways leading to salt-sensitive hypertension and renal damage in rescued ETB receptor-deficient (ETBRd) rats are still unknown. The objective of the study was therefore to identify modifications of urinary peptide and protein expression in ETBRd rats (n = 9) and wild-type controls (n = 6) using SDS - polyacrylamide gel electrophoresis (SDS-PAGE) and surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) technology. Glomerular filtration rate, glomerulosclerosis, and tubulointerstitial fibrosis did not differ between the groups. ETBRd rats showed slightly higher blood pressure (p < 0.001), media/lumen ratio of intrarenal arteries (p < 0.01), and albuminuria (p < 0.01). SDS-PAGE confirmed albuminuria, but showed no differences in the urinary excretion of low molecular weight proteins (<60 kDa). SELDI-TOF-MS profiling revealed 9 proteomic features at molecular masses (Da) of 2720, 2980, 3130, 3345, 6466, 6682, 8550, 18 729, and 37 492, which were significantly elevated (p < 0.02) in urine of ETBRd rats. The results demonstrate that, independent of structural changes in the kidneys, ETB-receptor deficiency causes specific differences in urinary peptide and protein excretion. SELDI-TOF-MS may be a valuable tool for the characterization of urinary biomarkers helping to uncover the mechanism of ETBR action in the kidney.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".