Monitoring angiogenesis using a human compatible calibration for broadband near-infrared spectroscopy
Bibliographic record
Abstract
Angiogenesis is a hallmark of many conditions, including cancer, stroke, vascular disease, diabetes, and high-altitude exposure. We have previously shown that one can study angiogenesis in animal models by using total hemoglobin (tHb) as a marker of cerebral blood volume (CBV), measured using broadband near-infrared spectroscopy (bNIRS). However, the method was not suitable for patients as global anoxia was used for the calibration. Here we determine if angiogenesis could be detected using a calibration method that could be applied to patients. CBV, as a marker of angiogenesis, is quantified in a rat cortex before and after hypoxia acclimation. Rats are acclimated at 370-mmHg pressure for three weeks, while rats in the control group are housed under the same conditions, but under normal pressure. CBV increased in each animal in the acclimation group. The mean CBV (%volume/volume) is 3.49%± 0.43% (mean ± SD) before acclimation for the experimental group, and 4.76%± 0.29% after acclimation. The CBV for the control group is 3.28%± 0.75%, and 3.09%± 0.48% for the two measurements. This demonstrates that angiogenesis can be monitored noninvasively over time using a bNIRS system with a calibration method that is compatible with human use and less stressful for studies using animals.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| 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".