SERS probing of fungal HET-s fibrils formed at neutral and acidic pH conditions
Bibliographic record
Abstract
Advances in precision medical diagnostics require accurate and sensitive characterization of pathogens. In particular, health conditions associated with protein misfolding require an identification of proteinaceous amyloid fibrils or their precursors. These pathogenic entities express specific molecular structures, which require ultra-sensitive, molecular-level detection methods. A potentially transformative technique termed nanoplasmonics employs electro-optical phenomena in the vicinity of specially engineered metal nanostructures. A signature application of nanoplasmonics exploits enhancement of inelastic scattering of light in specific locations near metallic nanostructures, known as surface-enhanced Raman scattering (SERS). We applied SERS complemented with confocal microscopy imaging for ultra-sensitive, non-invasive, and label-free characterization of the fungal prion HET-s (218-289) as a model for β-sheet rich amyloid structures. This characterization employed Au-coated dielectric supports as plasmonic substrates. After confirming the formation of HET-s fibrils at both pH 7.5 and 2.8 using negative staining transmission electron microscopy, we subjected the fibril-containing solutions to multimodal analysis using confocal microscopy and SERS. The SERS spectral fingerprints from all HET-s samples expressed vibrational markers for β-structure, unstructured backbone, and aromatic side-chains. However, relative intensities of major SERS bands were pronouncedly different for the two pH levels. We have analyzed potential origins of the most pronounced SERS bands and proposed hypothetical mechanistic models that could explain the observed SERS fingerprints from HET-s fibrils grown at pH 7.5 and 2.8.
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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) | 0.000 | 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) | 0.000 | 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 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".