The atherogenic<i>Scarb1</i>null mouse model shows a high bone mass phenotype
Bibliographic record
Abstract
Scavenger receptor class B, type I (SR-BI), the Scarb1 gene product, is a receptor associated with cholesteryl ester uptake from high-density lipoproteins (HDL), which drives cholesterol movement from peripheral tissues toward the liver for excretion, and, consequently, Scarb1 null mice are prone to atherosclerosis. Because studies have linked atherosclerosis incidence with osteoporosis, we characterized the bone metabolism in these mice. Bone morphometry was assessed through microcomputed tomography and histology. Marrow stromal cells (MSCs) were used to characterize influence of endogenous SR-BI in cell functions. Total and HDL-associated cholesterol in null mice were increased by 32-60%, correlating with its role in lipoprotein metabolism. Distal metaphyses from 2- and 4-mo-old null mice showed correspondingly 46 and 37% higher bone volume fraction associated with a higher number of trabeculae. Histomorphometric analyses in 2-mo-old null male mice revealed 1.42-fold greater osteoblast surface, 1.37-fold higher percent mineralizing surface, and 1.69-fold enhanced bone formation rate. In vitro assays for MSCs from null mice revealed 37% higher proliferation rate, 48% more alkaline phosphatase activity, 70% greater mineralization potential and a 2-fold osterix (Sp7) expression, yet a 0.5-fold decrease in caveolin-1 (Cav1) expression. Selective uptake levels of HDL-associated cholesteryl oleate and estradiol were similar between MSC from wild-type and Scarb1 null mice, suggesting that its contribution to this process is not its main role in these cells. However, Scarb1 knockout stunted the HDL-dependent regulation of Cav1 genic expression. Scarb1 null mice are not prone to osteoporosis but show higher bone mass associated with enhanced bone formation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".