Increased Osteocyte Lacunae Density in the Hypermineralized Bone Matrix of Children with Osteogenesis Imperfecta Type I
Bibliographic record
Abstract
Osteocytes are terminally differentiated osteoblasts embedded within the bone matrix and key orchestrators of bone metabolism. However, they are generally not characterized by conventional bone histomorphometry because of their location and the limited resolution of light microscopy. OI is characterized by disturbed bone homeostasis, matrix abnormalities and elevated bone matrix mineralization density. To gain further insights into osteocyte characteristics and bone metabolism in OI, we evaluated 2D osteocyte lacunae sections (OLS) based on quantitative backscattered electron imaging in transiliac bone biopsy samples from children with OI type I (n = 19) and age-matched controls (n = 24). The OLS characteristics were related to previously obtained, re-visited histomorphometric parameters. Moreover, we present pediatric bone mineralization density distribution reference data in OI type I (n = 19) and controls (n = 50) obtained with a field emission scanning electron microscope. Compared to controls, OI has highly increased OLS density in cortical and trabecular bone (+50.66%, +61.73%; both p < 0.001), whereas OLS area is slightly decreased in trabecular bone (−10.28%; p = 0.015). Correlation analyses show a low to moderate, positive association of OLS density with surface-based bone formation parameters and negative association with indices of osteoblast function. In conclusion, hyperosteocytosis of the hypermineralized OI bone matrix associates with abnormal bone cell metabolism and might further impact the mechanical competence of the bone tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".