Quantification of oxidized and reduced cytochrome-c-oxidase by combining discrete-wavelength time-resolved and broadband continuous-wave near-infrared spectroscopy
Bibliographic record
Abstract
Quantification of cytochrome-c-oxidase (CCO) can directly inform about cerebral metabolic capacity and function, but limited options currently exist for its in vivo assessment. Near-infrared spectroscopy (NIRS) has the potential to quantify CCO and its redox states, but hyperspectral absorption measurements are required due to their broad absorption profiles and low concentrations relative to hemoglobin. While this may be achieved with continuous-wave broadband NIRS (bNIRS), separating the signal contributions of absorption and scattering remains a challenge. Alternatively, time-resolved NIRS (trNIRS) can directly disentangle absorption and scattering but is typically constrained to a few wavelengths. This work aimed to develop an approach for quantifying absolute CCO concentration using discrete-wavelength trNIRS to calibrate bNIRS, yielding calibrated bNIRS (cbNIRS). Monte-Carlo simulations were conducted to validate the algorithm. Subsequently, a hybrid cbNIRS system was assembled, and tissue-mimicking phantoms were prepared with blood, Intralipid, and either yeast or sodium dithionite for validation. The simulations demonstrated that the algorithm can accurately measure absorption across the spectral range (error = 0.8 ± 0.4%). Further, the concentrations of CCO and its different redox states were estimated with an error of 7.9% or less. In the phantom experiments, the measured HbT concentration increased with the addition of blood, but not yeast nor sodium dithionite, and the value agreed with the expected concentration estimated from the packed cell volume of blood. A large increase in total CCO was measured only after the addition of yeast (1.8 ± 0.4 µ M). Transitions in the oxygenation state of hemoglobin and redox state of CCO followed the expected trends as the phantom was deoxygenated and reoxygenated. Additionally, the sodium dithionite experiments confirmed that the COO signal measured with cbNIRS is not a result of crosstalk with the hemoglobin signal. This work demonstrates that absolute concentrations of both redox states of CCO can be quantified with high accuracy using cbNIRS. Future work will assess the feasibility of in vivo CCO measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".