High numerical aperture imaging allows chirality measurement in individual collagen fibrils using polarization second harmonic generation microscopy
Bibliographic record
Abstract
Second harmonic generation (SHG) microscopy is a commonly used technique to study the organization of collagen within tissues. However, individual collagen fibrils, which have diameters much smaller than the resolution of most optical systems, have not been extensively investigated. Here we probe the structure of individual collagen fibrils using polarization-resolved SHG (PSHG) microscopy and atomic force microscopy. We find that longitudinally polarized light occurring at the edge of a focal volume of a high numerical aperture microscope objective illuminated with linearly polarized light creates a measurable variation in PSHG signal along the axis orthogonal to an individual collagen fibril. By comparing numerical simulations to experimental data, we are able to estimate parameters related to the structure and chirality of the collagen fibril without tilting the sample out of the image plane, or cutting tissue at different angles, enabling chirality measurements on individual nanostructures to be performed in standard PSHG microscopes. The results presented here are expected to lead to a better understanding of PSHG results from both collagen fibrils and collagenous tissues. Further, the technique presented can be applied to other chiral nanoscale structures such as microtubules, nanowires, and nanoribbons.
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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".