Endonuclease Increases Efficiency of Osteoblast Isolation from Murine Calvariae.
Bibliographic record
Abstract
Abstract Bone is a highly dynamic organ that undergoes remodeling equally regulated by osteoblast-mediated bone formation and osteoclast-mediated bone resorption. To clarify the regulation of osteoblastogenesis, primary murine osteoblasts are required for an in vitro study. Primary osteoblasts are isolated from neonatal calvariae through digestion with collagenase. However, the number of isolated cells from one pup is not enough for further in vitro experiments, leading to an increase in the use of euthanized pups. In the present study, we hypothesized that the viscosity of digested calvariae and digestion solution supplemented with collagenase would result in cell clumping and reduction of isolated cells from bones. We simply added Benzonase, a genetically engineered endonuclease that shears all forms of DNAs/RNAs, in order to reduce nucleic acid-mediated viscosity. We found that addition of Benzonase increased the number of isolated osteoblasts by three fold compared to that without Benzonase through reduction of viscosity. Additionally, Benzonase has no effect on cellular identity and function. The new osteoblast isolation protocol with Benzonase minimizes the number of neonatal pups required for an in vitro study and expands the concept that isolation of other populations including osteocytes that are hard to be purified could be modified by Benzonase.
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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".