Aging affects mechanical properties and lubricin/PRG4 gene expression in normal ligaments
Bibliographic record
Abstract
Age-related changes in ligament properties may have clinical implications for injuries in the mature athlete. Previous preclinical models documented mechanical and biochemical changes in ligaments with aging. The purpose of this study was to investigate the effect of aging on ligament properties (mechanical, molecular, biochemical) by comparing medial collateral ligaments (MCLs) from 1-year-old and 3-year-old rabbits. The MCLs underwent mechanical (n=7, 1-year-old; n=7, 3-year-old), molecular (n=8, 1-year-old; n=6, 3-year-old), collagen and glycosaminoglycan (GAG) content (n=8, 1-year-old; n=6, 3-year-old) and water content (n=8, 1-year-old; n=5, 3-year-old) assessments. Mechanical assessments evaluated total creep strain, failure strain, ultimate tensile strength and modulus. Molecular assessments using RT-qPCR evaluated gene expression for collagens, proteoglycans, hormone receptors, and matrix metalloproteinases and their inhibitors. While total creep strain and ultimate tensile strength were not affected by aging, failure strain was increased and modulus was decreased comparing MCLs from 3-year-old rabbits to those from 1-year-old rabbits. The mRNA expression levels for lubricin/proteoglycan 4 (PRG4) and tissue inhibitor of metalloproteinase-3 increased with aging; whereas, the mRNA expression levels for estrogen receptor and matrix metalloproteinase-1 decreased with aging. Collagen and GAG content assays and water content assessments did not demonstrate any age-related changes. The increased failure strain and decreased modulus with aging may have implications for increased susceptibility to ligament damage/injury with aging. Lubricin/PRG4 gene expression was affected by aging and its speculated role in ligament function may be related to interfascicular lubrication, which in turn may lead to altered mechanical function with aging and increases in potential for injury.
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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".