In vitro ion adsorption and cytocompatibility of dicalcium phosphate ceramics
Bibliographic record
Abstract
Abstract Background In vitro cell testing of degradable bioceramics such as brushite or monetite is often challenging due to the ion release into or adsorption from the culture medium. These ionic changes are then mostly responsible for cell proliferation and activity, which prohibits the investigation of effects originating from surface topography or further material modifications. Methods Here, we aimed to solve this problem by developing a pre-conditioning regime following the repeated immersion of brushite and monetite samples in various Ca 2+ , Mg 2+ and PO 4 3− containing electrolytes, followed by studying ion adsorption / release as well as changes in phase composition and in vitro cytocompatibility with MG63 cells. Results The results demonstrated that by using DMEM cell culture medium in a ratio of 10 ml/sample was sufficient to minimize changes of ionic composition after 7 d with a daily change of the medium. This leads to changes of the surface composition with dissolution of the brushite phase. In turn, this also positively influences the in vitro cytocompatibility with a 2–3 fold higher cell number and cell activity on the DMEM pretreated surfaces. Conclusions Controlled sample washing prior to cell testing using DMEM medium seems to be a valuable procedure not only to stabilize the pH during cell culture but also to maintain ion concentrations within a cell friendly range.
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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 | 0.000 |
| 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".