Intracranial Surface-Enhanced Raman Scattering Endoscopy for <i>In Vivo</i> Protein Quantification under Physiological Stimulation
Bibliographic record
Abstract
Surface-enhanced Raman scattering (SERS) endoscopes hold great promise for minimally invasive, high-spatial-resolution in vivo protein detection in deep regions of the central nervous system, such as the brainstem. We developed an intracranial SERS endoscope using 70 μm optical fibers decorated with gold nanoparticles and functionalized with the anti-S100β antibody for in vivo monitoring of S100β, an astrocytic protein, across different brain regions. Here, we report that the SERS endoscope can detect varying levels of protein release across multiple brain regions, corresponding to different levels of brain activity. Using optogenetic stimulation of the cortical masticatory area (CMA) to induce rhythmic jaw movements (RJMs) in awake mice, we observed a significant increase in S100β concentration in the trigeminal main sensory nucleus (NVsnpr) located in the brainstem. Notably, the mild stimulation of the CMA, which did not evoke RJMs, resulted in lower, yet detectable, levels of S100β release. Additionally, SERS endoscopes inserted across different locations of the somatosensory cortex of anesthetized mice revealed S100β levels that matched the known activation profiles across cortical hemispheres in response to tactile stimulation of the hindpaw. Overall, these results demonstrate intracranial SERS endoscopy for in vivo protein monitoring in awake animals.
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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) | 0.001 | 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) | 0.001 | 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".