Photoacoustic imaging for assessing ischemic kidney damage in vivo
Bibliographic record
Abstract
Ischemic reperfusion injuries (IRIs) occur after blood returns to a tissue or organ after a period without oxygen or nutrients, which causes an inflammatory response leading to heterogeneous scarring of the nearby tissue and vasculature. This is associated with long-term decreases blood flow, and necrosis. Although most commonly associated with heart attacks and strokes, IRIs are also a side effect of organ transplants, when the organ is reperfused in the recipient’s body after being transported from the donor to the transplant hospital. Currently, the optimal method of monitoring for IRI is limited to biopsies, which are invasive and poorly monitor the spatial heterogeneity of the damage. To non-invasively identify changes in kidneys, the left renal artery in mice (n=3) was clamped for 45 minutes to create an IRI event. Both kidneys of each animal were monitored using photoacoustics (PA) with the VevoLAZR system (Fujifilm-VisualSonics, Toronto) three, four and eight weeks after surgery. IRI-treated kidneys show increased picosirius red staining, indicative of collagen (0.601 vs 0.042, p < 0.0001), decreased size as assessed by cross-sectional area (7.8 mm2 vs 35.9 mm2 , p < 0.0001), and decreased oxygen saturation (sO2; 62% vs 77%, p = 0.02). Analysis of the photoacoustic data shows that a two-point metric, the 715:930 nm ratio of the whole kidney (1.05 vs 0.57, p = 0.049) and the optical spectral slope (OSS) (0.8 * 10-3 vs 3.0 * 10-3, p = 0.013) are both able to differentiate between IRI-treated and healthy kidneys. These data suggest that photoacoustics can be used as a non-invasive method to observe in vivo changes in the kidney due to IRI.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".