Coregistered autofluorescence-optical coherence tomography imaging of human lung sections
Bibliographic record
Abstract
Autofluorescence (AF) imaging can provide valuable information about the structural and metabolic state of tissue that can be useful for elucidating physiological and pathological processes. Optical coherence tomography (OCT) provides high resolution detailed information about tissue morphology. We present coregistered AF-OCT imaging of human lung sections. Adjacent hematoxylin and eosin stained histological sections are used to identify tissue structures observed in the OCT images. Segmentation of these structures in the OCT images allowed determination of relative AF intensities of human lung components. Since the AF imaging was performed on tissue sections perpendicular to the airway axis, the results show the AF signal originating from the airway wall components free from the effects of scattering and absorption by overlying layers as is the case during endoscopic imaging. Cartilage and dense connective tissue (DCT) are found to be the dominant fluorescing components with the average cartilage AF intensity about four times greater than that of DCT. The epithelium, lamina propria, and loose connective tissue near basement membrane generate an order of magnitude smaller AF signal than the cartilage fluorescence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".