Stimuli-Responsive Photoacoustic Nanoswitch for <i>in Vivo</i> Sensing Applications
Bibliographic record
Abstract
Photoacoustic imaging provides high-resolution images at depths beyond the optical diffusion limit. To broaden its utility, there is need for molecular sensors capable of detecting environmental stimuli through alterations in photoacoustic signal. Photosynthetic organisms have evolved ingenious strategies to optimize light absorption through nanoscale ordered dye aggregation. Here, we use this concept to synthesize a stimuli-responsive nanoswitch with a large optical absorbance and sensing capabilities. Ordered dye aggregation between light-harvesting porphyrins was achieved through intercalation within thermoresponsive nanovesicles. This causes an absorbance red-shift of 74 nm and a 2.7-fold increase in absorptivity of the Qy-band, with concomitant changes in its photoacoustic spectrum. This spectral feature can be reversibly switched by exceeding a temperature threshold. Using this thermochromic property, we noninvasively determined a localized temperature change in vivo, relevant for monitoring thermal therapies of solid tumors. Similar strategies may be applied alongside photoacoustic imaging, to detect other stimuli such as pH and enzymatic activity.
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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) | 0.001 | 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".