Influence of Calcium Ions on Cell Survival and Proliferation in the Context of an Alginate Hydrogel
Bibliographic record
Abstract
One goal of biofabrication is to incorporate living cells into artificial scaffolds in order to repair damaged tissues or organs. Although there are many studies on various biofabrication techniques, the maintenance of cell viability during the biofabrication process and cell proliferation after the process is still a challenging issue. Construction of scaffolds using hydrogels composed of natural materials can avoid exposure of cells to harsh chemicals or temperature extremes but can still entail exposure to non-physiological conditions, causing cell damage or even death. This paper presents an experimental investigation into the influence on Schwann cell survival and proliferation of calcium used for ionic crosslinking of alginate hydrogel during the biofabrication process. The experimental results obtained show the viability and proliferation capacity of cells, either suspended in cell culture medium or encapsulated in hydrogel, and vary with the calcium concentration and the time period of cells exposed to the calcium environment. The experimental results also show the alginate concentration and cell density, that have profound influence on cell survival and proliferation, and solution viscosity as well. This study suggests the incorporation of living cells in calcium-crosslinked hydrogel in the biofabrication process can be regulated for controlled cell survival and proliferation.
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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".