Evaluation of hyperbranched polyglycerol for cold perfusion and storage of donor kidneys in a pig model of kidney autotransplantation
Bibliographic record
Abstract
Hyperbranched polyglycerol (HPG) is a biocompatible polyether polymer that is a potential colloid component in a preservation solution for suppressing interstitial edema during cold storage of a donor organ. This study evaluated the outcomes of kidney transplants after cold perfusion and storage with a HPG-based preservation solution (HPGS) in a pig model of kidney autotransplantation. The left kidneys of farm pigs (weighing 35-45 kg) were perfused with and stored in either cold HPGS or standard UW solution (UWS), followed by transplantation to the right side after right nephrectomy. The survival and function of transplants were determined by the urine output, and serum creatinine (SCr) and blood urea nitrogen (BUN) of recipients. Transplant injury was examined by histological analysis. Here, we showed that there was no significant difference between HPGS and UWS in the prevention of tissue edema, but HPGS was more effective than UWS for initial blood washout of kidney perfusion and for the prevention of cold ischemia injury during cold storage. After autotransplantation, the kidneys preserved with HPGS (HPG group) had better functional recovery than those with UWS (UW group), indicated by significantly more urine output and lower levels of SCr and BUN. The survived grafts in HPG group had less tissue damage than those in UW group. In conclusion, as compared to the UWS the HPGS has less negative impact on kidney cold ischemia during cold storage, resulting in improving immediate functional recovery after transplantation, suggesting that HPG is a promising colloid for donor kidney preservation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".