Phytic acid as alternative setting retarder enhanced biological performance of dicalcium phosphate cement in vitro
Bibliographic record
Abstract
Dicalcium phosphate cement preparation requires the addition of setting retarders to meet clinical requirements regarding handling time and processability. Previous studies have focused on the influence of different setting modifiers on material properties such as mechanical performance or injectability, while ignoring their influence on biological cement properties as they are used in low concentrations in the cement pastes and the occurrence of most compounds in human tissues. Here, analyses of both material and biological behavior were carried out on samples with common setting retardants (citric acid, sodium pyrophosphate, sulfuric acid) and novel (phytic acid). Cytocompatibility was evaluated by in vitro tests with osteoblastic (hFOB 1.19) and osteoclastic (RAW 264.7) cells. We found cytocompatibility was better for sodium pyrophosphate and phytic acid with a three-fold cell metabolic activity by WST-1 test, whereas samples set with citric acid showed reduced cell number as well as cell activity. The compressive strength (CS) of cements formed with phytic acid (CS = 13 MPa) were nearly equal to those formed with citric acid (CS = 15 MPa) and approximately threefold higher than for other setting retardants. Due to a proven cytocompatibility and high mechanical strength, phytic acid seems to be a candidate replacement setting retardant for dicalcium phosphate cements.
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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".