Utility of Micro‐Ultrasound and Contrast Agents in the Assessment of a Mouse Model of Renal Ischemic Reperfusion Injury
Bibliographic record
Abstract
High‐resolution micro‐ultrasound enables non‐invasive real‐time quantification of blood velocity measurements, perfusion and endothelial cell biomarkers preclinically. In nephrology research, ischemic reperfusion injury results in extensive damage to both ischemic tissue and systemic tissue in the kidney. This abstract describes the investigation of a mouse model of renal ischemic reperfusion using a dedicated micro‐ultrasound system. Using this novel imaging system, surgical technique was confirmed non‐invasively and perfusion changes within the microvasculature of the contralateral kidney were quantified and the increased expression of P‐selectin was quantified in the kidneys. Results showed a decrease in microvasculature perfusion in the contralateral kidney, the cortex, the medulla, and most dramatically in the cortical‐medullary border zone. An increase in P‐selectin expression as an inflammatory biomarker was observed most dramatically in the cortex and border zone of the ipsilateral kidney. As such, micro‐ultrasound has proven to be a valuable tool for in vivo imaging of mouse models of ischemic reperfusion injury and in quantification of kidney perfusion and inflammatory response.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".