High-pressure irrigation increases adipocyte-like cells at the expense of osteoblasts in vitro
Bibliographic record
Abstract
High-pressure lavage produces greater visible damage to bone at a macroscopic and microscopic level when compared with low-pressure lavage and can result in delay in the healing of fractures. Osteoblasts and adipocytes are derived from mesenchymal stem cells. Conditions which lead to bone loss often involve a switch from the osteoblast to adipocyte lineage. We have therefore examined the effect of high- and low-pressure irrigation on the differentiation of adipocytes. Calvaria-derived bone cells were exposed to either low-pressure or high-pressure irrigation with normal saline. After 14 days the cells were fixed and the osteoblasts and adipocytes quantified using Oil Red O to stain cytoplasmic lipid droplets (triglycerides) in the cells. Osteoblasts were quantified using a commercially available alkaline-phosphatase staining assay. A standard quantitative reverse transcription-polymerase chain reaction (RT-PCR) was performed. Messenger RNA levels for osteocalcin, a marker of osteoblasts, and PPARy2, a marker of adipocytes, were measured. High-pressure lavage resulted in an increase in adipogenesis of 50% when compared with low-pressure lavage. Our findings suggest that high-pressure lavage may promote differentiation of mesenchymal stem cells towards the adipocyte lineage. This may have clinical significance in the development of delayed and nonunion after treatment of fractures of long bones.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".