A multimodal 3D imaging approach of pore networks in the human femur to assess age‐associated vascular expansion and Lacuno‐Canalicular reduction
Bibliographic record
Abstract
Cellular communication in the mechanosensory osteocyte Lacuno-Canalicular Network (LCN) regulates bone tissue remodeling throughout life. Age-associated declines in LCN size and connectivity dysregulate mechanosensitivity to localized remodeling needs of aging or damaged tissue, compromising bone quality. Synchrotron radiation-based micro-Computed Tomography (SRμCT) and Confocal Laser Scanning Microscopy (CLSM) were employed to visualize LCN and vascular canal morphometry in an age series of the anterior femur (males n = 14, females n = 11, age range = 19-101, mean age = 55). Age-associated increases in vascular porosity were driven by pore coalescence, including a significant expansion in pore diameter and a significant decline in pore density. In contrast, the LCN showed significant age-associated reductions in lacunar volume fraction, mean diameter, and density, and in canalicular volume fraction and connectivity density. Lacunar density was significantly lower in females across the lifespan, exacerbating their age-associated decline. Canalicular connectivity density was also significantly lower in females but approached comparable declining male values in older age. Our data illuminate the trajectory and potential morphometric sources of age-associated bone loss. Increased vascular porosity contributes to bone fragility with aging, while an increasingly reduced and disconnected LCN undermines the mechanosensitivity required to repair and reinforce bone. Understanding why and how this degradation occurs is essential for improving the diagnosis and treatment of age-related changes in bone quality and fragility.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".