Imaging-guided two-photon excitation-emission-matrix measurements of human skin tissues
Bibliographic record
Abstract
There are increased interests on using multiphoton imaging and spectroscopy for skin tissue characterization and diagnosis. However, most studies have been done with just a few excitation wavelengths. Our objective is to perform a systematic study of the two-photon fluorescence (TPF) properties of skin fluorophores, normal skin, and diseased skin tissues. A nonlinear excitation-emission-matrix (EEM) spectroscopy system with multiphoton imaging guidance was constructed. A tunable femtosecond laser was used to vary excitation wavelengths from 730 to 920 nm for EEM data acquisition. EEM measurements were performed on excised fresh normal skin tissues, seborrheic keratosis tissue samples, and skin fluorophores including: NADH, FAD, keratin, melanin, collagen, and elastin. We found that in the stratum corneum and upper epidermis of normal skin, the cells have large sizes and the TPF originates from keratin. In the lower epidermis, cells are smaller and TPF is dominated by NADH contributions. In the dermis, TPF is dominated by elastin components. The depth resolved EEM measurements also demonstrated that keratin structure has intruded into the middle sublayers of the epidermal part of the seborrheic keratosis lesion. These results suggest that the imaging guided TPF EEM spectroscopy provides useful information for the development of multiphoton clinical devices for skin disease 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".