Designed synthesis of bright and in vivo excretable multiplexed surface-enhanced Raman nanoparticle palettes
Bibliographic record
Abstract
In this talk, I will discuss our work to synthesize Raman nanoparticle imaging probes for noninvasive multiplexed imaging of tumor xenograft preclinical models and their potential translation. We have created multicore SERS nanoparticles that emit Raman signals as bright as NIR fluorescence. This enabled us to perform noninvasive, multiplexed imaging of tumors in live mouse models. On the other hand, despite their low toxicity, large gold nanoparticle-based SERS probes are non-excretable, limiting their use in humans. We have addressed this by developing Raman-active supraparticles assembled from renally clearable nanoclusters, which exhibit bright Raman scattering and are highly excretable, offering a promising replacement for non-excretable SERS nanotags for further translation.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".