Transcranial light-tissue interaction analysis
Bibliographic record
Abstract
The penetration depth of light plays a crucial role in therapeutic medical applications. In order to design effective medical photonic devices, an in-depth understanding of light’s ability to penetrate tissues (including bone, skin, and fat) is necessary. The amount of light energy absorbed or scattered by tissues affects the intensity of light reaching an intended target in vivo. In this study, we examine the transmittance of light through a variety of cranial tissues for the purpose of determining the efficacy of neuro stimulation using a transcranial laser. Tissue samples collected from a pig were irradiated with a pulsed laser. We first determine the optimal irradiation wavelength of the laser to be 808nm. With varying peak and average power of the laser, we found an inverse and logarithmic relationship between the penetration depth and the intensity of the light. After penetrating the skin and skull of the pig, the light decreases in intensity at a rate of approximately 90.8 (±0.4) percent for every 5 mm of brain tissue penetrated. We also found the correlation between the irradiation time and dosage, using three different lasers (with peak power of 500, 1000, and 1500mW respectively). These data will help deduce what laser power is required to achieve a clinically-realistic model for a given irradiation time. This work is fundamental and the experimental data can be used to supplement existing and future research on the effects of laser light on brain tissue for the design of medical devices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".