Multiphoton microscopy study of the morphological and quantity changes of collagen and elastic fiber components in keloid disease
Bibliographic record
Abstract
Multiphoton microscopy was used to study the extracellular matrix of keloid at the molecular level without tissue fixation and staining. Direct imaging of collagen and elastin was achieved by second harmonic generation and two-photon excited fluorescence, respectively. The morphology and quantity of collagen and elastin in keloid were characterized and quantitatively analyzed in comparison to normal skin. The study demonstrated that in keloid, collagen content increased in both the upper dermis and the deep dermis, while elastin mostly showed up in the deep dermis and its quantity is higher compared to normal skin. This suggests the possibility that abnormal fibroblasts synthesized an excessive amount of collagen and elastin at the beginning of keloid formation, corresponding to the observed deep dermis, while after a certain time point, the abnormal fibroblast produced mostly collagen, corresponding to the observed upper dermis. The morphology of collagen and elastin in keloid was disrupted and presented different variations. In the deep dermis, elastic fibers showed node structure, while collagen showed obviously regular gaps between adjacent bundles. In the upper dermis, collagen bundles aligned in a preferred direction, while elastin showed as sparse irregular granules. This new molecular information provided fresh insight about the development process of keloid.
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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) | 0.001 | 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".