Complex cellular environments imaged by SERS nanoprobes using sugars as an all-in-one vector
Bibliographic record
Abstract
Raman spectroscopy coupled with confocal microscopy offers an alternative bioimaging technique overcoming limitations associated with sensitivity, tissue penetration and image resolution. Allied to the surface-enhanced Raman scattering (SERS) properties of gold nanoparticles (AuNP), we designed SERS nanoprobes with enhanced properties and straightforward application as bio-labelling agents for gliomas. The ensuing nanoprobes coated with simple sugar units (galactose or glucose) allowed assessing information about their intracellular localization (vesicular structures), with impressive sensitivity towards complex environments and proved the ability to overcome biological auto-fluorescence and high penetration in tissues. We validate the use of sugars as an all-in-one vector (Raman reporter, conferring high stability, biocompatibility and affinity to glioma cells) as imaging agents using an impressive technique.
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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".