Nonlinear laser scanning microscopy of human vocal folds
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Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: The purpose of this work was to apply nonlinear laser scanning microscopy (NLSM) for visualizing the morphology of extracellular matrix proteins within human vocal folds. This technique may potentially assist clinicians in making rapid diagnoses of vocal fold tissue disease or damage. Microstructural characterization based on NLSM provides valuable information for better understanding molecular mechanisms and tissue structure. STUDY DESIGN: Experimental, ex vivo human vocal fold. METHODS: A custom-built multimodal nonlinear laser scanning microscope was used to scan fibrillar proteins in three 4% formaldehyde-fixed cadaveric samples. Collagen and elastin, key extracellular matrix proteins in the vocal fold lamina propria, were imaged by two nonlinear microscopy modalities: second harmonic generation (SHG) and two-photon fluorescence (TPF), respectively. An experimental protocol was introduced to characterize the geometrical properties of the imaged fibrous proteins. RESULTS: NLSM revealed the biomorphology of the human vocal fold fibrous proteins. No photobleaching was observed for the incident laser power of ∼60 mW before the excitation objective. Types I and III fibrillar collagen were imaged without label in the tissue by intrinsic SHG. Imaging while rotating the incident laser light-polarization direction confirmed a helical shape for the collagen fibers. The amplitude, periodicity, and overall orientation were then computed for the helically distributed collagen network. The elastin network was simultaneously imaged via TPF and found to have a basket-like structure. In some regions, particularly close to the epithelium, colocalization of both extracellular matrix components were observed. CONCLUSIONS: A benchmark study is presented for quantitative real-time, ex vivo, NLSM imaging of the extracellular macromolecules in human vocal fold lamina propria. The results are promising for clinical applications.
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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) | 0.000 | 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 | 0.000 | 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 it