Nonlinear Optical Properties of Type I Collagen Fibers Studied by Polarization Dependent Second Harmonic Generation Microscopy
Bibliographic record
Abstract
Collagen (type I) fibers are readily visualized with second harmonic generation (SHG) microscopy though the molecular origin of the signal has not yet been elucidated. In this study, the molecular origin of SHG from type I collagen is investigated using the time-dependent coupled perturbed Hartree-Fock calculations of the hyperpolarizibilities of glycine, proline, and hydroxyproline. Two effective nonlinear dipoles are found to orient in-the-plane of the amino acids, with one of the dipoles aligning close to the pitch orientation in the triple-helix, which provides the dominant contribution to the SHG polarization properties. The calculated hyperpolarizability tensor element ratios for the collagen triple-helix models: [(Gly3)n]3, [(Gly-Pro2)n]3, and [(Gly-Pro-Hyp)n]3, are used to predict the second-order nonlinear susceptibility ratios, χ(zzz)(2)/χ(iiz)(2) and χ(zii)(2)/χ(iiz)(2) of collagen fibers. From SHG microscopy polarization in, polarization out (PIPO) measurements of type I collagen in human lung tissue, a theoretical method is used to extract the triple-helix orientation angle with respect to the collagen fiber. The study shows the dominant role of amino acid orientation in the triple-helix for determining the polarization properties of SHG and provides a method for determining the triple-helix orientation angle in the collagen fibers.
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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".