Optical monitoring of kidney oxygenation and hemodynamics using a miniaturized near-infrared sensor
Bibliographic record
Abstract
Background: Following human renal allograft transplant primary graft dysfunction can occur early in the postoperative period as a result of acute tubular necrosis, acute rejection, drug toxicity, and vascular complications. Successful treatment of graft dysfunction requires early detection and accurate diagnosis so that disease-specific medical and/or surgical intervention can be provided promptly. However, current diagnostic methods are not sensitive or specific enough, so that identifying the cause of graft dysfunction is problematic and often delayed. Near-infrared spectroscopy (NIRS) is an established optical method that monitors changes in tissue hemodynamics and oxygenation in real time. We report the feasibility of directly monitoring kidney the kidney in an animal model using NIRS to detect renal ischemia and hypoxia. Methods: In an anesthetized pig, a customized continuous wave spatially resolved (SR) NIRS sensor was fixed directly to the surface of the surgically exposed kidney. Changes in the concentration of oxygenated (O2Hb) deoxygenated (HHb) and total hemoglobin (THb) were monitored before, during and after renal artery clamping and reperfusion, and the resulting fluctuations in chromophore concentration from baseline used to measure variations in renal perfusion and oxygenation. Results: On clamping the renal artery THb and O<sub>2</sub>Hb concentrations declined progressively while HHb rose. With reperfusion after releasing the artery clamp O<sub>2</sub>Hb and THb rose while HHb fell with all parameters returning to its baseline. This pattern was similar in all three trials. Conclusion: This pilot study indicates that a miniaturized NIRS sensor applied directly to the surface of a kidney in an animal model can detect the onset of renal ischemia and tissue hypoxia. With modification, our NIRS-based method may contribute to early detection of renal vascular complications and graft dysfunction following renal transplant.
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".