Cutaneous porphyrins exhibit anti-stokes fluorescence that is detectable in sebum (Conference Presentation)
Bibliographic record
Abstract
Porphyrins produced by Propionibacterium acnes represent the principal fluorophore associated with acne, and appear as orange-red luminescence under the Wood’s lamp. Assessment of acne based on Wood’s lamp (UV) or visible light illumination is limited by photon penetration depth and has limited sensitivity for earlier stage lesions. Inducing fluorescence with near infrared (NIR) excitation may provide an alternative way to assess porphyrin-related skin disorders. We discovered that under 785 nm CW laser excitation PpIX powder exhibits fluorescence emission in the shorter wavelength range of 600-715 nm with an intensity that is linearly dependent on the excitation power. We attribute this shorter wavelength emission to anti-Stokes fluorescence. Similar anti-Stokes fluorescence was also detected focally in all skin-derived samples containing porphyrins. Regular (Stokes) fluorescence was present under UV and visible light excitation on ex vivo nasal skin and sebum from uninflamed acne, but not on nose surface smears or sebum from inflamed acne. Co-registered CW laser-excited anti-Stokes fluorescence and fs laser-excited multi-photon fluorescence images of PpIX powder showed similar features. In the skin samples because of the anti-Stokes effect, the NIR-induced fluorescence was presumably specific for porphyrins since there appeared to be no anti-Stokes emission signals from other typical skin fluorophores such as lipids, keratins and collagen. Anti-Stokes fluorescence under NIR CW excitation is more sensitive and specific for porphyrin detection than UV- or visible light-excited regular fluorescence and fs laser-excited multi-photon fluorescence. This approach also has higher image contrast compared to NIR fs laser-based multi-photon fluorescence imaging. The anti-Stokes fluorescence of porphyrins within sebum could potentially be applied to detecting and targeting acne lesions for treatment via fluorescence image guidance.
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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) | 0.013 | 0.003 |
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".