Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it