Imaging Secondary Structure of Individual Amyloid Fibrils of a β<sub>2</sub>-Microglobulin Fragment Using Near-Field Infrared Spectroscopy
Bibliographic record
Abstract
Amyloid fibril diseases are characterized by the abnormal production of aggregated proteins and are associated with many types of neuro- and physically degenerative diseases. X-ray diffraction techniques, solid-state magic-angle spinning NMR spectroscopy, circular dichroism (CD) spectroscopy, and transmission electron microscopy studies have been utilized to detect and examine the chemical, electronic, material, and structural properties of amyloid fibrils at up to angstrom spatial resolution. However, X-ray diffraction studies require crystals of the fibril to be analyzed, while other techniques can only probe the bulk solution or solid samples. In the work reported here, apertureless near-field scanning infrared microscopy (ANSIM) was used to probe the secondary structure of individual amyloid fibrils made from an in vitro solution. Simultaneous topographic and infrared images of individual amyloid fibrils synthesized from the #21-31 peptide fragment of β(2)-microglobulin were acquired. Using this technique, IR spectra of the amyloid fibrils were obtained with a spatial resolution of less than 30 nm. It is observed that the experimental scattered field spectrum correlates strongly with that calculated using the far-field absorption spectrum. The near-field images of the amyloid fibrils exhibit much lower scattering of the IR radiation at approximately 1630 cm(-1). In addition, the near-field images also indicate that composition and/or structural variations among individual amyloid fibrils were present.
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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".