Bibliographic record
Abstract
Introduction: Renal transplantation is preferred for patients with end-stage renal disease.Yet, donor kidney demand outweighs supply.Static cold storage (SCS) at 4˚C is the gold standard for renal preservation.SCS contributes to renal damage through ischemia-reperfusion injury (IRI) involving cell death and inflammation.In porcine models of renal transplantation, we added non-FDA-approved hydrogen sulfide (H2S) donor, AP39, to blood substitute, Hemopure, at 21˚C and 37˚C, which improved renal graft quality; though, the experiment was costly to meet renal metabolic demand.In rats, we studied sodium thiosulfate (STS), an FDAapproved H2S donor, at 4˚C and saw similar benefits.Still, there is a risk of cold IRI.Recent studies show that 10˚C human organ preservation enhanced patient survival without requiring extensive oxygen during preservation.Therefore, we hypothesize that preservation solutions with STS and Hemopure at 10˚C will reduce renal IRI.Methods: With an in vitro model of rat renal IRI, we evaluated STS use at 4˚C, 10˚C, 21˚C, and 37˚C.We treated rat proximal tubular epithelial cells with 150 µM STS for 24 hours hypoxia to mimic ischemia, and 24 hours normoxia to mimic reperfusion.To assess cell viability, we used flow cytometry with Annexin-V and Propidium Iodide to determine apoptosis and necrosis levels, respectively.Results: STS significantly enhanced cell viability at 10˚C compared to 4˚C, 21˚C, and 37˚C, as determined by cell proportion negatively stained with Annexin-V and Propidium Iodide.STS also significantly decreased apoptotic and necrotic cells at 10˚C compared to 4˚C, 21˚C, and 37˚C, as shown by cell p positively stained with Annexin-V and Propidium Iodide (Figures 123).Conclusions: 10˚C STS treatment significantly protects rat proximal tubular epithelial cells from IRI.Our work may translate renal graft preservation at 10˚C with STS and Hemopure into clinical practice to bridge the gap between supply and demand for donor kidneys.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.701 | 0.447 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".