In vivo real time monitoring of vasoconstriction and vasodilation by a combined diffuse reflectance spectroscopy and Doppler optical coherence tomography approach
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A combined diffuse reflectance (DR) spectroscopy and doppler optical coherence tomography (DOCT) approach may offer a powerful means for assessment of tissue function, and potentially provide a way for earlier cancer detection through non-invasive local blood supply measurements. The goal of the study was to compare a DR-derived blood-content-related index to a measure of local blood supply flow as furnished by DOCT during manipulations with blood circulation (vasoconstriction and vasodilation), investigate similarities and differences, complementarity of techniques, and then applying these results to the underlying biology. STUDY DESIGN/MATERIALS AND METHODS: Simultaneous DR-DOCT measurements of local blood supply were conducted during drug and mechanically-induced vasoconstriction and vasodilatation on an externalized intact rat gut in vivo. A simple heuristic metric, termed Blood Supply Index was derived from the spectroscopic DR data. This metric variance due to mechanical and pharmacological manipulation of the local blood supply was recorded, and compared with that of two DOCT-derived metrics, namely normalized blood velocity (v(rel)) and blood vessel diameter (D). SUMMARY AND CONCLUSIONS: During vasoconstriction and vasodilatation, the local blood response was successfully visualized by both DOCT and DR metrics and a reproducible correlation was found between these two measurements. A combined DR-DOCT approach may evolve into a technologically-viable method for cross-validation of the derived haemodynamic metrics, yielding a more reliable functional tissue assessment tool for accurate cancer diagnosis and staging.
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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 | 0.000 | 0.000 |
| 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".