Multimodal imaging of vocal fold scarring in a rabbit model by multiphoton microscopy
Bibliographic record
Abstract
Vocal fold scarring as a result of injury or disease can lead to voice disorders which can significantly affect the quality of life. During the scarring process, the normally elastic tissue of the vocal fold lamina propria is replaced by a much stiffer collagen-based fibrotic tissue, which impacts the fold’s ability to vibrate. Surgical removal of this tissue is often ineffective and can result in further scarring. Injectable biomaterials, a form of tissue engineering, have been proposed as a potential solution to reduce existing scars or prevent scarring altogether. In order to properly evaluate the effectiveness of these new materials, multiphoton microscopy emerges as an effective tool due to its intrinsic multiple label free contrast mechanisms that highlight extracellular matrix elements. In this study, we evaluate the spatial distribution of collagen and elastin fibers in a rabbit model using second harmonic generation (SHG), third harmonic generation (THG) and two photon autofluorescence (TPAF) applied to unlabeled tissue sections. In comparison to traditional methods that rely on histological staining or immunohistochemistry, SHG, THG and TPAF provide a more reliable detection of these native proteins. The evaluation of collagen levels allows us to follow the extent of scarring, while the presence of elastin fibers is thought to be indicative of the level of healing of the injured fold. Using these imaging modalities, we characterize the outcome of injectable biomaterial treatments in order to direct future treatments for tissue engineering.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".