Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality
Bibliographic record
Abstract
Roughly 10% of the world's population has chronic kidney disease (CKD). In its advanced stages, CKD greatly increases the risk of hospitalization and death. Although kidney transplantation has revolutionized the care of advanced CKD, clinicians have limited ways of assessing donor kidney quality. Thus, optimal donor kidney-recipient matching cannot be performed, meaning that some patients receive damaged kidneys that function poorly. Fibrosis is a form of chronic damage often present in donor kidneys, and it is an important predictor of future renal function. Currently, no safe, easy-to-perform technique exists that accurately quantifies renal fibrosis. We describe a potentially novel photoacoustic (PA) imaging technique that directly images collagen, the principal component of fibrotic tissue. PA imaging noninvasively quantifies whole kidney fibrotic burden in mice, and cortical fibrosis in pig and human kidneys, with outstanding accuracy and speed. Remarkably, 3-dimensional PA imaging exhibited sufficiently high resolution to capture intrarenal variations in collagen content. We further show that PA imaging can be performed in a setting that mimics human kidney transplantation, suggesting the potential for rapid clinical translation. Taken together, our data suggest that PA collagen imaging is a major advance in fibrosis quantification that could have widespread preclinical and clinical impact.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".