The Lacunocanalicular Network is Denser in C57BL/6 Compared to BALB/c Mice
Bibliographic record
Abstract
The lacunocanalicular network (LCN) is an intricate arrangement of cavities (lacunae) and channels (canaliculi), which permeates the mineralized bone matrix. In its porosity, the LCN accommodates the cell network of osteocytes. These two nested networks are attributed a variety of essential functions including transport, signaling, and mechanosensitivity due to load-induced fluid flow through the LCN. For a more quantitative assessment of the networks' function, the three-dimensional architecture has to be known. For this reason, we aimed (i) to quantitatively characterize spatial heterogeneities of the LCN in whole mouse tibial cross-sections of BALB/c mice and (ii) to analyze differences in LCN architecture by comparison with another commonly used inbred mouse strain, the C57BL/6 mouse. Both tibiae of five BALB/c mice (female, 26-week-old) were stained using rhodamine 6G and whole tibiae cross-sections were imaged using confocal laser scanning microscopy. Using image analysis, the LCN was quantified in terms of density and connectivity and lacunar parameters, such as lacunar degree, volume, and shape. In the same tibial cross-sections, the calcium content was measured using quantitative backscattered electron imaging (qBEI). A structural analysis of the LCN properties showed that spatially denser parts of the LCN are mainly due to a higher density of branching points in the network. While a high intra-individual variability of network density was detected within the cortex, the inter-individual variability between different mice was low. In comparison to C57BL/6J mice, BALB/c mice showed a distinct lower canalicular density. This reduced network was already detectable on a local network level with fewer canaliculi emanating from lacunae. Spatial correlation with qBEI images demonstrated that bone modeling resulted in disruptions in the network architecture. The spatial heterogeneity and differences in density of the LCN likely affects the fluid flow within the network and therefore bone's mechanoresponse to loading.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".