Fluorescence spectroscopy and birefringence of molecular changes in maturing rat tail tendon
Bibliographic record
Abstract
Tissue remodeling during maturation, wound healing, and response to vascular stress involves molecular changes of collagen and elastin in the extracellular matrix (ECM). Two optical techniques are effective for investigating these changes--laser-induced fluorescence (LIF) spectroscopy and polarizing microscopy. LIF spectroscopy integrates the signal from both elastin and collagen cross-linked structure, whereas birefringence is a measure of only collagen. Our purpose is (1) to evaluate the rat tail tendon (RTT) spectroscopy against data from purified extracted protein standards and (2) to correlate the two optical techniques in the study of RTT and skin. Spectra from tissue samples from 27 male rats and from extracted elastin and collagen were obtained using LIF spectroscopy (357 nm). Birefringence was measured on 5-mum histological sections of the same tissue. Morphometric analysis reveals that elastin represents approximately 10% of tendon volume and contributes to RTT fluorescence. RTT maximum fluorescence emission intensity (FEI(max)), which includes collagen and elastin, increases with animal weight (R(2)=0.64). Birefringence, when plotted against weight, increases to a plateau (nonlinear correlation: R(2)=0.90), tendon having greater birefringence than skin. LIF spectroscopy and collagen fiber birefringence are shown to provide complementary measurements of molecular structure (tendon birefringence versus FEI(max) at R(2)=0.60).
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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".