Control Light Delivery in PDT by Taking Account the Optical Properties of Hair Density on the Skin Surface
Bibliographic record
Abstract
Information concerning energy deposition during laser therapy of skin is needed to comprehend and asses the results of clinical procedures in dermatology. The prupose of this study is to show an optical model that predicts infection dose for the skins in different hair density and which can be used to explore whether parameteres of hair on the skin surface, merited the bio optical sudies and phsician during irradiation. A skin optic study by the advanced system analysis program (ASAP) software represents the best way for improving investigation of light propagation into the skin. The ASAP technique of the skin modeling is the process of constructing optical objects, such as a set of skin layers and propagation of a laser beam, whose behavior or properties correspond in some way to a particular real-wold system. The results showed that, hair on the skin surface minimized dose injection of the skin target during the PDT procedure. The differences in penetrating injection dose for layers of skin between low and high hair densities after irradiation, for end epidermis layer at 0.098 mm in the skin region with high and low hair density are 832.1 mW.mm-3 and 853 mW.mm-3 respectively, but for skin without hair is mW.mm-3. Using this resutl, we found that the region of decreased light fluence rate that formed at the epidermis layer significantly reduces the power uptake in deep layers. Moreover, hair density on the skin surface precents light penetration into the deeper region. Therefore, if the hair parameters are ignored, a relatively significant effect of the dose rise occurs in a deeper area resulting great influence in depth of target.
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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 | 0.002 | 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 | 0.001 | 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".