Nano-Enabled SERS Reporting Photosensitizers
Bibliographic record
Abstract
To impart effective cellular damage via photodynamic therapy (PDT), it is vital to deliver the appropriate light dose and photosensitizer concentration, and to monitor the PDT dose delivered at the site of interest. In vivo monitoring of photosensitizers has in large part relied on their fluorescence emission. Palladium-containing photosensitizers have shown promising clinical results by demonstrating near full conversion of light to PDT activity at the cost of having undetectable fluorescence. We demonstrate that, through the coupling of plasmonic nanoparticles with palladium-photosensitizers, surface-enhanced Raman scattering (SERS) provides both reporting and monitoring capability to otherwise quiescent molecules. Nano-enabled SERS reporting of photosensitizers allows for the decoupling of the therapeutic and imaging mechanisms so that both phenomena can be optimized independently. Most importantly, the design enables the use of the same laser wavelength to stimulate both the PDT and imaging features, opening the potential for real-time dosimetry of photosensitizer concentration and PDT dose delivery by SERS monitoring.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".