Effect of out of plane orientation on polarization second harmonic generation of single collagen fibrils
Bibliographic record
Abstract
Second harmonic generation (SHG) microscopy has emerged as a powerful technique for visualizing collagen organization within tissues. Amongst the many advantages of SHG is its sensitivity to collagen nanoscale organization, and its presumed sensitivity to the relative out of plane polarity of fibrils. Recent results have shown that circular dichroism SHG (CD-SHG), a technique that has been commonly assumed to reveal the relative out of plane polarity of collagen fibrils, is actually insensitive to changes in fibril polarity. However, results from another research group seem to contradict this conclusion. Both previous results have been based on SHG imaging of collagen fibrils within tissues, therefore, to gain a definitive understanding of the sensitivity of SHG to relative out of plane polarity, the results from individual fibrils are desirable. Here we present polarization resolved SHG microscopy (PSHG) data from individual collagen fibrils oriented out of the image plane by buckling on an elastic substrate. We show through correlation with atomic force microscopy measurements that SHG intensity can be used to estimate the out of plane angle of individual fibrils. We then compare the sensitivity of two PSHG techniques, CD-SHG and polarization-in, polarization-out SHG (PIPO-SHG), to the relative out of plane polarity of individual fibrils. We find that for single fibrils CD-SHG is insensitive to relative out of polarity and we also demonstrate the first direct experimental confirmation that PIPO-SHG reveals the relative out of plane polarity of individual collagen fibrils.
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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) | 0.001 | 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".