Relative Contributions of Hemoglobin and Myoglobin to Near-Infrared Spectroscopic Images of Cardiac Tissue
Bibliographic record
Abstract
Near-infrared (NIR) spectroscopic imaging is emerging as a unique tool for intra-operative assessment of myocardial oxygenation, but quantitative interpretation of the images is not straightforward. One confounding factor specific to muscle tissue (both skeletal and cardiac) is that the visible/NIR absorbance spectrum of myoglobin (Mb), an intracellular O(2) storage protein, is virtually identical to that of hemoglobin (Hb). As a consequence, the relative contributions of Mb and Hb to the NIR spectra measured in vivo for blood perfused muscle tissue cannot be determined from the measured spectra alone. To estimate the relative contributions of Mb and Hb to NIR spectra and spectroscopic images, isolated pig hearts were perfused first with a Hb-free blood substitute (Krebs-Henseleit buffer; KHB) and then with a 50/50 KHB/blood mixture, with spectroscopic images acquired at each step. Tissue Mb levels were estimated directly from the measurements during KHB perfusion, and total (Mb+Hb) levels were estimated from the images acquired during 50/50 blood/KHB perfusion. Myoglobin accounted for 63 +/- 11% of the total heme content during perfusion with the 50/50 mixture (implying that Mb would contribute 46% of the combined (Mb+Hb) NIR profile during whole blood perfusion), confirming that Mb contributes substantially to near-infrared absorbance spectra of blood perfused cardiac tissue.
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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 | 0.000 | 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) | 0.001 | 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".