Lacunar-canalicular network in femoral cortical bone is reduced in aged women and is predominantly due to a loss of canalicular porosity
Bibliographic record
Abstract
The lacunar-canalicular network (LCN) of bone contains osteocytes and their dendritic extensions, which allow for intercellular communication, and are believed to serve as the mechanosensors that coordinate the processes of bone modeling and remodeling. Imbalances in remodeling, for example, are linked to bone disease, including fragility associated with aging. We have reported that there is a reduction in scale for one component of the LCN, osteocyte lacunar volume, across the human lifespan in females. In the present study, we explore the hypothesis that canalicular porosity also declines with age. To visualize the LCN and to determine how its components are altered with aging, we examined samples from young (age: 20–23 y; n = 5) and aged (age: 70–86 y; n = 6) healthy women donors utilizing a fluorescent labelling technique in combination with confocal laser scanning microscopy. A large cross-sectional area of cortical bone spanning the endosteal to periosteal surfaces from the anterior proximal femoral shaft was examined in order to account for potential trans-cortical variation in the LCN. Overall, we found that LCN areal fraction was reduced by 40.6% in the samples from aged women. This reduction was due, in part, to a reduction in lacunar density (21.4% decline in lacunae number per given area of bone), but much more so due to a 44.6% decline in canalicular areal fraction. While the areal fraction of larger vascular canals was higher in endosteal vs. periosteal regions for both age groups, no regional differences were observed in the areal fractions of the LCN and its components for either age group. Our data indicate that the LCN is diminished in aged women, and is largely due to a decline in the canalicular areal fraction, and that, unlike vascular canal porosity, this diminished LCN is uniform across the cortex.
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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 | 0.000 | 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".