Filter-based method for background removal in high-sensitivity wide-field-surface-enhanced Raman scattering imaging<i>in vivo</i>
Bibliographic record
Abstract
As molecular imaging moves towards lower detection limits, the elimination of endogenous background signals becomes imperative. We present a facile background-suppression technique that specifically segregates the signal from surface-enhanced Raman scattering (SERS)-active nanoparticles (NPs) from the tissue autofluorescence background in vivo. SERS NPs have extremely narrow spectral peaks that do not overlap significantly with endogenous Raman signals. This can be exploited, using specific narrow-band filters, to image picomolar (pM) concentrations of NPs against a broad tissue autofluorescence background in wide-field mode, with short integration times that compare favorably with point-by-point mapping typically used in SERS imaging. This advance will facilitate the potential applications of SERS NPs as contrast agents in wide-field multiplexed biomarker-targeted imaging in vivo.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".