Orientation Matters: Polarization Dependent IR Spectroscopy of Collagen from Intact Tendon Down to the Single Fibril Level
Bibliographic record
Abstract
Infrared (IR) spectroscopy has been used for decades to study collagen in mammalian tissues. While many changes in the spectral profiles appear under polarized IR light, the absorption bands are naturally broad because of tissue heterogeneity. A better understanding of the spectra of ordered collagen will aid in the evaluation of disorder in damaged collagen and in scar tissue. To that end, collagen spectra have been acquired with polarized far-field (FF) Fourier Transform Infrared (FTIR) imaging with a Focal Plane Array detector, with the relatively new method of FF optical photothermal IR (O-PTIR), and with nano-FTIR spectroscopy based on scattering-type scanning near-field optical microscopy (s-SNOM). The FF methods were applied to sections of intact tendon with fibers aligned parallel and perpendicular to the polarized light. The O-PTIR and nano-FTIR methods were applied to individual fibrils of 100-500 nm diameter, yielding the first confirmatory and complementary results on a biopolymer. We observed that the Amide I and II bands from the fibrils were narrower than those from the intact tendon, and that both relative intensities and band shapes were altered. These spectra represent reliable profiles for normal collagen type I fibrils of this dimension, under polarized IR light, and can serve as a benchmark for the study of collagenous tissues.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".