Spectroscopic characterization and microscopic imaging of extracted and <i>in situ</i> cutaneous collagen and elastic tissue components under two‐photon excitation
Bibliographic record
Abstract
BACKGROUND/PURPOSES: Understanding the two-photon excitation spectral characteristics and microscopic morphology of cutaneous collagen and elastic tissue components is important for applying multiphoton microscopy (MPM) in basic skin biology research and for clinical diagnosis. METHODS: We developed a system for two-photon excitation spectral measurements at various excitation wavelengths. The microscopic morphology was studied using a commercial multiphoton microscope. RESULTS: We obtained two-photon excitation fluorescence (TPEF) excitation-emission matrices (EEM), for the first time, of purified collagen and elastin samples, as well as in situ collagen and elastic fibers within excised human dermis. The EEM of the dermis was found to be similar to that of elastin. The excitation spectra for second harmonic generation (SHG) from purified collagen and excised dermis were also studied and were found to have similar spectral shapes. CONCLUSION: This study, using the EEM spectroscopic approach, confirmed a previous imaging study inference that in the dermis, TPEF predominantly originates from elastic fibers, while SHG originates solely from collagen fibers. The EEM data and SHG excitation spectra obtained in this study can be used to guide the selection of excitation wavelengths for MPM applications in basic skin biology research and for clinical diagnosis.
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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 | 0.000 | 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) | 0.001 | 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".