Bibliographic record
Abstract
Human skin is an optical organ which constantly interacts with light. Its optical properties are fundamental to understanding various effects of light-tissue interaction inhuman skin and to laser applications in medicine and biology. The aim of this thesis was to test two hypotheses: (1) that skin optical properties can be quantitatively modeled, and (2)that in vivo spectroscopic measurements can be used to derive information about skin structures. Both theoretical and experimental procedures were used to test these hypotheses. Experimental studies were completed in terms of macroscopic, in vivo diffuse reflectance spectroscopy, autofluorescence spectroscopy, and temporal behavior of the autofluorescence signal during continuous laser exposure, as well as microscopic in vitro fluorophore distribution and spectral differences. Several interesting physical phenomena were discovered. Three new methods were developed for deriving information of different skin layers from in vivo and in vitro measurements. The most interesting finding was that, under continuous laser exposure, skin autofluorescence decays following a double exponential function and that the autofluorescence recovery takes about six days. A seven layer skin optical model was developed based on(1) the structural anatomy of skin, (2) published optical properties of different skin layers and blood, and (3) measured skin fluorophore micro-distribution. Monte Carlo simulation was used to solve the Boltzmann equation of radiative transfer for the new skin model. The solutions provided a detailed knowledge of light propagation in skin tissue. The theoretical modeling unified the microscopic properties with the macroscopic in vivo skin measurements. The physical meaning of the auto fluorescence double exponential decay dynamics was also elucidated. It was shown that the coefficients of the double decay can be used to estimate the fractional contributions of different skin layers to the observed in vivo auto fluorescence signal. Using this approach, it was determined that the fractional contribution of the stratum corneum was - 14%, while the calculated value for the Monte Carlo skin model was - 15%, providing a close agreement between experimental and theoretical values. The dermis contributed the remaining 85% of the observed in vivo signal. We therefore, believe that the thesis hypotheses have been substantially proven. Although the skin has complicated inhomogeneous structures, its optical properties can now be quantitatively modeled and various modalities of in vivo skin spectroscopy can be used to derive information on skin structures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".