Raman and SERS spectroscopy for EV characterization: detection of amyloid beta and bulk chemical analysis (Conference Presentation)
Bibliographic record
Abstract
Extracellular vesicles (EVs) carry molecular cargo that includes nucleic acids, lipids, proteins, and various other biomarkers. Raman and SERS spectroscopy are label-free spectroscopy techniques based on inelastic scattering of laser light interacting with molecular vibrations. In our study, we employed Raman and SERS spectroscopy for the detection of amyloid beta protein in the molecular cargo of small EVs and bulk chemical analysis of EVs. We observed considerable variation as a reflection of the biochemical content of EVs related to the Aβ peptide incorporated in EVs extracted from the AD cell culture model. Next, we developed a new CMOS-based sensing platform for trapping, imaging, and chemical characterization of EVs via SERS (CMOS TrICC) with the experimental enhancement factor 5.0 × 104. We employed this platform for parallel trapping and sensitive biochemical analysis of the 100 nm nanospheres and EVs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".