Analysis of the Efficiency of Photothermal and Photodynamic Cancer Therapy via Nanogolds and Photosensitizers
Bibliographic record
Abstract
Factors influencing the cancer therapy efficiency in both photothermal therapy (PTT) and photodynamic therapy (PDT) using nanogold particles and photosensitizers, respectively, are analyzed. In PTT, heat diffusion kinetics is used to calculate the temperature increase resulted from the nanogold absorption of light energy, whereas photochemical kinetics is used to find the efficacy of PDT, or the generation rate of reactive oxygen species. The critical factors of the PTT/PDT synergistic efficacy include: the concentration of the initiator (nanogold or photosensitizers) in the treated medium, the wavelength and energy of the light applied to the medium. Optimal parameters are calculated for maximum PDT efficacy. In PTT, diode laser (at 810 nm) is used to heat nanogolds (rod-shape or core-shell). In PDT, photosensitizers of riboflavin, 5-ALA, methylene blue and indocyanine green may be used with the associate light at wavelength of (365, 430 nm), (530-670 nm) and (780-850 nm) respectively. Both single light or dual light in infrared or visible wavelength are proposed to activate the photosensitizers or nanogolds. Optimization is required for maximum synergistic efficacy.
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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 | 0.001 | 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) | 0.001 | 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".