Three-dimensional simultaneous optical coherence tomography and confocal fluorescence microscopy for investigation of lung tissue
Bibliographic record
Abstract
Although several strategies exist for a minimal-invasive treatment of patients with lung failure, the mortality rate of acute respiratory distress syndrome still reaches 30% at minimum. This striking number indicates the necessity of understanding lung dynamics on an alveolar level. To investigate the dynamical behavior on a microscale, we used three-dimensional geometrical and functional imaging to observe tissue parameters including alveolar size and length of embedded elastic fibers during ventilation. We established a combined optical coherence tomography (OCT) and confocal fluorescence microscopy system that is able to monitor the distension of alveolar tissue and elastin fibers simultaneously within three dimensions. The OCT system can laterally resolve a 4.9 μm line pair feature and has an approximately 11 μm full-width-half-maximum axial resolution in air. confocal fluorescence microscopy visualizes molecular properties of the tissue with a resolution of 0.75 μm (laterally), and 5.9 μm (axially) via fluorescence detection of the dye sulforhodamine B specifically binding to elastin. For system evaluation, we used a mouse model in situ to perform lung distension by application of different constant pressure values within the physiological regime. Our method enables the investigation of alveolar dynamics by helping to reveal basic processes emerging during artificial ventilation and breathing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".