Heterogeneity of the osteocyte lacuno-canalicular network architecture and material characteristics across different tissue types in healing bone
Bibliographic record
Abstract
Various tissue types, including fibrous connective tissue, bone marrow, cartilage, woven and lamellar bone, coexist in healing bone. Similar to most bone tissue type, healing bone contains a lacuno-canalicular network (LCN) housing osteocytes. These cells are known to orchestrate bone remodeling in healthy bone by sensing mechanical strains and translating them into biochemical signals. The structure of the LCN is hypothesized to influence mineralization processes. Hence, the aim of the present study was to visualize and match spatial variations in the LCN topology with mineral characteristics, within and at the interfaces of the different tissue types that comprise healing bone. We applied a correlative multi-method approach to visualize the LCN architecture and quantify mineral particle size and orientation within healing femoral bone in a mouse osteotomy model (26 weeks old C57BL/6 mice). This approach revealed structural differences across several length scales during endochondral ossification within the following regions: calcified cartilage, bony callus, cortical bone and a transition zone between the cortical and callus region analyzed 21 days after the osteotomy. In this transition zone, we observed a continuous convergence of mineral characteristics and osteocyte lacunae shape as well as discontinuities in the lacunae volume and LCN connectivity. The bony callus exhibits a 34% higher lacunae number density and 40% larger lacunar volume compared to cortical bone. The presented correlations between LCN architecture and mineral characteristics improves our understanding of how bone develops during healing and may indicate a contribution of osteocytes to bone (re)modeling.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".