A novel approach to off-clamp partial nephrectomy demonstrates significant improvements in renal injury in an experimental porcine model
Bibliographic record
Abstract
Introduction: We sought to design a partial nephrectomy (PN) with contralateral total nephrectomy porcine model and assess the underlying mechanisms of ischemia reperfusion injury (IRI) after PN using a novel, clinically approved resection device.Methods: Domestic male pigs (n=9) underwent left lower pole PN, allocated to either standard (Group 1) or no ischemia PN (Group 2), followed by contralateral nephrectomy. Biochemical studies were performed at baseline, Day 2, and Day 7; after sacrifice, kidneys were processed for histological analysis. Apoptotic markers were measured by Western blot analyses. Urinary biomarkers were measured to assess acute kidney injury.Results: At Day 2 following PN, there was a significant rise in serum creatinine in Group 1 compared to Group 2 (355 vs. 136 mmol/L; p=0.008). Intra-renal tissue oxygen saturation after PN was inversely correlated with postoperative creatinine (rs -0.75; p=0.012) and the grade of acute tubular necrosis (rs -0.70; p=0.036). We observed a rise in expression of pro-apoptotic markers and pro-inflammatory markers in Group 1 following PN compared to Group 2. Histological analysis revealed higher grade of apoptosis in Group 1.Conclusions: IRI associated with standard PN has a deleterious impact on acute renal function, markers of tissue injury, and histological parameters, compared to off-clamp PN using the ALTRUS device. We identified several intraoperative and postoperative markers that may be used as predictors for functional and histological injury following PN.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".