Age-Related Changes in the Mechanical Regulation of Bone Healing Are Explained by Altered Cellular Mechanoresponse
Bibliographic record
Abstract
Increasing age is associated with a reduced bone regeneration potential and increased risk of morbidities and mortality. A reduced bone formation response to mechanical loading has been shown with aging, and it remains unknown if the interplay between aging and mechanical stimuli during regeneration is similar to adaptation. We used a combined in vivo/in silico approach to investigate age-related alterations in the mechanical regulation of bone healing and identified the relative impact of altered cellular function on tissue patterns during the regenerative cascade. To modulate the mechanical environment, femoral osteotomies in adult and elderly mice were stabilized using either a rigid or a semirigid external fixator, and the course of healing was evaluated using histomorphometric and micro-CT analyses at 7, 14, and 21 days post-surgery. Computer models were developed to investigate the influence of the local mechanical environment within the callus on tissue formation patterns. The models aimed to identify the key processes at the cellular level that alter the mechanical regulation of healing with aging. Fifteen age-related biological alterations were investigated on two levels (adult and elderly) with a design of experiments setup. We show a reduced response to changes in fixation stability with age, which could be explained by reduced cellular mechanoresponse, simulated as alteration of the ranges of mechanical stimuli driving mesenchymal stem cell differentiation. Cellular mechanoresponse has been so far widely ignored as a therapeutic target in aged patients. Our data hint to mechanotherapeutics as a potential treatment to enhance bone healing in the elderly. © 2019 American Society for Bone and Mineral Research.
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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) | 0.002 | 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".