Using intracellular plasmonics to characterize nanomorphology in human cells
Why this work is in the frame
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Bibliographic record
Abstract
Determining the characteristics and localization of nanoparticles inside cells is crucial for nanomedicine design for cancer therapy. Hyperspectral imaging is a fast, straightforward, reliable, and accurate method to study the interactions of nanoparticles and intracellular components. With a hyperspectral image, we could collect spectral information consisting of thousands of pixels in a short time. Using hyperspectral images, in this work, we developed a label-free technique to detect nanoparticles in different regions of the cell. This technique is based on plasmonic shifts taking place during the interaction of nanoparticles with the surrounding medium. The unique optical properties of gold nanoparticles, localized surface plasmon resonance bands, are influenced by their microenvironment. The LSPR properties of nanoparticles, hence, could provide information on regions in which nanoparticles are distributed. To examine the potential of this technique for intracellular detection, we used three different types of gold nanoparticles: nanospheres, nanostars and Swarna Bhasma (SB), an Indian Ayurvedic/Sidha medicine, in A549 (human non-small cell lung cancer) and HepG2 (human hepatocellular carcinoma) cells. All three types of particles exhibited broader and longer bands once they were inside cells; however, their plasmonic shifts could change depending on the size and morphology of particles. This technique, along with dark-field images, revealed the uniform distribution of nanospheres in cells and could provide more accurate information on their intracellular microenvironment compared to the other particles. The region-dependent optical responses of nanoparticles in cells highlight the potential application of this technique for subcellular diagnosis when particles with proper size and morphology are chosen to reflect the microenvironment effects properly.
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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 | 0.000 |
| 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) | 0.000 | 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 it