Site‐specific variations in cutaneous autofluorescence revealed by excitation‐emission matrix spectroscopy
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Although cutaneous autofluorescence has been utilized for evaluation of skin conditions, there is a paucity of data on normal human skin autofluorescence and its dependence on anatomical site. The objective of this study is to use excitation-emission matrix spectroscopy to quantify and characterize skin autofluorescence at different body sites. METHODS: Ten anatomical sites from 30 healthy volunteers were measured with a double-grating excitation-emission matrix spectrofluorometer. RESULTS: For the 10 body sites evaluated, there were four overall patterns of autofluorescence: skin from the head and neck exhibits high superficial and low bilayer fluorescence; the dorsal forearm and dorsal hand have both low superficial and bilayer fluorescence; the upper inner arm and back have high superficial and intermediate bilayer fluorescence; while the palm and thumbnail have both high superficial and bilayer fluorescence. The corresponding fluorescence excitation-emission peaks for these patterns were as follows: head and neck, 3 peaks at 290-300/330-350, 360-380/460-485, and 380-420/610-630 nm; dorsal forearm and dorsal hand, 2 peaks around 295-300/345-360 and 385-395/460-485 nm; upper inner arm and back, 3 peaks around 295-300/335-355, 335-340/390-410, and 375-390/455-480 nm; palm and thumbnail, 3 peaks around 285-300/345-355, 335-345/390-410, and 365-390/450-480 nm. CONCLUSION: Cutaneous fluorescence varies in distinct patterns according to anatomical site, due to the component fluorophores present, skin thickness, and the degree of melanization and long term sun exposure. These EEM patterns for normal skin should be accounted for when interpreting fluorescence signals from disease states and can also be used to guide the selection of optimal wavebands when applying this optical modality.
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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 | 0.001 |
| 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) | 0.002 | 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".