A blueprint for performing SERS measurements in tissue with plasmonic nanofibers
Bibliographic record
Abstract
Plasmonic nanostructures have found increasing utility due to the increased popularity that surface-enhanced Raman scattering (SERS) has achieved in recent years. SERS has been incorporated into an ever-growing list of applications, with bioanalytical and physiological analyses having emerged as two of the most popular. Thus far, the transition from SERS studies of cultured cells to SERS studies involving tissue has been gradual and limited. In most cases, SERS measurements in more intact tissue have involved nanoparticles distributed throughout the tissue or localized to specific regions via external functionalization. Performing highly localized measurements without the need for global nanoparticle uptake or specialized surface modifications would be advantageous to the expansion of SERS measurements in tissue. To this end, this work provides critical insight with supporting experimental evidence into performing SERS measurements with nanosensors inserted in tissues. We address two critical steps that are otherwise underappreciated when other approaches to performing SERS measurements in tissue are used. Specifically, we demonstrate two mechanical routes for controlled positioning and inserting the nanosensors into the tissue, and we discuss two means of focusing on the nanosensors both before and after they are inserted into the tissue. By examining the various combinations of these steps, we provide a blueprint for performing SERS measurements with nanosensors inserted in tissue. This blueprint could prove useful for the general development of SERS as a tool for bioanalytical and physiological studies and for more specialized techniques such as SERS-optophysiology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".