Spatial variations in the osteocyte lacuno-canalicular network density and analysis of the connectomic parameters
Bibliographic record
Abstract
Osteocyte lacuno-canalicular network (LCN) is comprised of micrometre-sized pores and submicrometric wide channels in bone. Accumulating evidence suggests multiple functions of this network in material transportation, mechanobiological signalling, mineral homeostasis and bone remodelling. Combining rhodamine staining and confocal laser scanning microscopy, the longitudinal cross-sections of six mouse tibiae were imaged, and the connectome of the network was quantified with a focus on the spatial heterogeneities of network density, connectivity and length of canaliculi. In-vivo loading and double calcein labelling on these tibiae allowed differentiating the newly formed bone from the pre-existing regions. The canalicular density of the murine cortical bone varied between 0.174 and 0.243 μm/μm3, and therefore is three times larger than the corresponding value for human femoral midshaft osteons. The spatial heterogeneity of the network was found distinctly more pronounced across the cortex than along the cortex. We found that in regions with a dense network, the LCN conserves its largely tree-like character, but increases the density by including shorter canaliculi. The current study on healthy mice should serve as a motivating starting point to study the connectome of genetically modified mice, including models of bone diseases and of reduced mechanoresponse.
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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) | 0.001 | 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".