Surface-Enhanced Raman Scattering Nanoendoscope for Quantification of a Protein Released under Physiological Stimulation in Brain Tissue
Bibliographic record
Abstract
A surface-enhanced Raman scattering (SERS) biosensor with minimal invasiveness and high spatial resolution has been developed as a nanoendoscope to detect changes in protein concentrations at specific sites in biological tissues. While generally applicable to various tissues or proteins, the SERS nanoendoscope is demonstrated for the quantitative detection of S100β, an astrocytic protein whose plasmatic levels are known to vary in several neuropathologies such as Alzheimer’s disease, schizophrenia, Down syndrome, Parkinson’s disease and epilepsy, but for which intratissular levels have not been locally monitored, demonstrating key attributes of the SERS nanoendoscope. The SERS nanoendoscope is fabricated with densely and well-dispersed deposited gold nanoparticles modified with anti-S100β primary antibody on pulled optical fibers with a tip diameter of 700 nm, conducive to noninvasive and regiospecific detection of the S100β protein in different regions of mouse brain slices under different physiological stimuli with micrometer resolution. Quantification was performed ex vivo using SERS-active nanotags with secondary antibodies with detection limits of 5 and 7 nM in phosphate-buffered saline solution and mouse brain slice, respectively. Various physiological stimuli were then applied ex vivo to wild-type and S100β-knockout mouse brain slices to demonstrate the SERS nanoendoscope under physiological conditions. The average concentration of S100β was increased to 27, 45, and 48 nM upon N -methyl- d -aspartate, electrical, and optogenetic stimulation, respectively, statistically higher than all controls, demonstrating the ability of the SERS nanoendoscope to quantify protein release in biological tissues.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".