Simultaneous Near-IR Spectroscopy and Magnetic Resonance Imaging to Assess Cerebral Oxygenation and Brain Water during Hypoxia-Ischemia in 2-week-old Rats
Bibliographic record
Abstract
Cerebral near-infrared spectroscopy can potentially probe several parameters related to the onset of stroke and the ensuing tissue damage. One obvious marker of ischemia is cerebral oxygenation, which can be lowered sharply in stroke-affected tissue. Also commonly assessed, though less straightforward to recover, is the redox state of the cytochrome aa3 copper center. Finally, parameters that are in principle available but seldom recovered from in vivo near-IR spectra are changes in water concentration and scattering properties of the tissue. We have evaluated the potential for near-IR spectroscopy to detect relevant changes in cerebral oxygenation, blood volume, water content, and scattering properties in an infant rat stroke model that is well characterized by magnetic resonance imaging methods. The specific aim was to acquire near-IR spectra simultaneously with MR images and to correlate stroke-associated changes detected via these two modalities prior to, during and after a hypoxia-ischemia episode within this stroke model. Presented here are results from the design and testing of a near-IR illumination/detection system that is compatible with an MR imaging system, and the recovery of trends in the near-IR spectra that complement the hypoxic-ischemic changes observed in the MR images. Unexpectedly large intensity changes observed for the in vivo near-IR water absorptions are ascribed to hypoxia-induced variations in effective optical pathlength, suggesting that the water absorptions may prove generally useful as a means to track such changes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".