Surface-enhanced hyper Raman hyperspectral imaging and probing in animal cells
Bibliographic record
Abstract
Hyper Raman scattering, that is, spontaneous, two-photon excited Raman scattering, of organic molecules becomes strong when it occurs as surface-enhanced hyper Raman scattering (SEHRS), in the proximity of plasmonic nanostructures. Its advantages over one-photon excited surface-enhanced Raman scattering (SERS) include complementary vibrational information resulting from different selection rules, probing of very small focal volumes, and beneficial excitation with long wavelengths. Here, imaging of macrophage cells by SEHRS is demonstrated, using SEHRS labels consisting of silver nanoparticles and two different molecules, 2-naphthalenethiol and para-mercaptobenzoic acid, that are excited off-resonance. The vibrational signatures of the molecules are discriminated using hyperspectral analysis and provide information about the subcellular localization of the SEHRS probes. The SEHRS based hyperspectral imaging approach presented here uses principal component analysis (PCA) to localize the reporter molecules inside the cells and is augmented by hierarchical cluster analysis (HCA). The high sensitivity of SEHRS spectra with respect to small environmental changes can be utilized for mapping of physiological parameters in the endosomal system of the cells. This is illustrated by discussing the spatial distribution of endosomes of varying pH inside the cytosol.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".