Raman spectroscopy predicts the link between claw keratin and bone collagen structure in a rodent model of oestrogen deficiency
Bibliographic record
Abstract
Osteoporosis is a common disease characterised by reduced bone mass and an increased risk of fragility fractures. Low bone mineral density is known to significantly increase the risk of osteoporotic fractures, however, the majority of non-traumatic fractures occur in individuals with a bone mineral density too high to be classified as osteoporotic. Therefore, there is an urgent need to investigate aspects of bone health, other than bone mass, that can predict the risk of fracture. Here, we successfully predicted association between bone collagen and nail keratin in relation to bone loss due to oestrogen deficiency using Raman spectroscopy. Raman signal signature successfully discriminated between ovariectomised rats and their sham controls with a high degree of accuracy for the bone (sensitivity 89%, specificity 91%) and claw tissue (sensitivity 89%, specificity 82%). When tested in an independent set of claw samples the classifier gave 92% sensitivity and 85% specificity. Comparison of the spectral changes occurring in the bone tissue with the changes occurring in the keratin showed a number of common features that could be attributed to common changes in the structure of bone collagen and claw keratin. This study established that systemic oestrogen deficiency mediates parallel structural changes in both the claw (primarily keratin) and bone proteins (primarily collagen). This strengthens the hypothesis that nail keratin can act as a surrogate marker of bone protein status where systemic processes induce changes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".