MP76-19 ONE IN THE SAME? THE HISTOPATHOLOGICAL DIFFERENCES IN RADICAL VS DONOR NEPHRECTOMY SPECIMENS
Bibliographic record
Abstract
limitation of short term assessment, this study aims to optimize decellularization methods that can preserve functional vascular architecture for long-term implantation.METHODS: We compared three different decellularization protocols (1% Triton X-100, 0.25% and 0.5% SDS) and assessed the effects of the decellularization on the maintenance of pig kidney's glomeruli and afferent artery using angiography, vascular corrosion casts, and scanning electron microscopy (SEM) analysis.Particularly, the vascular casting technique was efficiently used to analyze normal morphology and functional architecture of a vascular luminal structure.RESULTS: Angiographic images of native and decellularized kidneys using three decellularization methods show clear visualization of main renal artery, segmental and lobar arteries, indicating no structural changes after decellularization.SEM analysis of the vascular casts of the different kidneys demonstrate that native 1% Triton treated kidney retained small arteries and glomeruli structures compared with the decellularized kidneys with SDS treatment.CONCLUSIONS: Our results demonstrate that the decellularization protocol using 1% Triton X-100 was most effective in preserving microvasculatures of the renal scaffold and that the optimized method may contribute to vascular patency long-term following kidney implantation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.004 |
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".