Quantitative Nanohistology of aging dermal collagen
Bibliographic record
Abstract
ABSTRACT While the external signs of skin aging have been well-defined throughout history, much less is known about aging within the ultrastructure of our skin. Our skin, the largest organ in our body, is structured by collagen through fibrils or large sheets. With the increased use of nanometrology tools in histology, it is now possible to explore how the aging process affects collagen at its most fundamental level, the collagen fibril. Here, we show how atomic force microscopy-based quantitative nanohistology can differentiate skin from different age groups and anatomical sites. Following the definition of specific collagen biomarkers at the nanoscale, we used a segmentation approach to quantify the prevalence of 4 structural biomarkers over a dataset of 42,000 images (30 donors) complemented by extensive nanomechanical analyses (30,000 indentation curves) on histological sections. Our results demonstrate that specific age-related collagen fingerprints could be found when comparing the % prevalence of each marker between the papillary and reticular dermis. A case of abnormal biological aging validated our markers and nanohistology approach. This first extensive study focusing on defining signs of dermal aging at the nanoscale proves we are all unique to our dermal collagen ultrastructure.
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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 | 0.000 | 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".