Alginate-gelatin-carboxymethylcellulose bioink designing and bioprinting to improve fibroblast cell niche
Bibliographic record
Abstract
Most bioinks used in extrusion-based bioprinting are derived from natural hydrogels. Among these, alginate-gelatin blends are widely used but suffer from limited stability and suboptimal mechanical properties. In this study, a tricomponent bioink consisting of alginate, gelatin, and carboxymethylcellulose (CMC) is developed to address these limitations. To retain gelatin's cell-adhesive RGD sequences while minimizing rapid deterioration, the gelatin content was reduced compared to alginate-gelatin bioinks to preserve structural integrity and support cell attachment, spreading, and proliferation. The inclusion of CMC further enhanced the mechanical, rheological, and physical properties of the hydrogel. Four formulations with varying alginate and CMC concentrations were prepared and designated as D-1, D-2, D-3, and D-4. Among these, the D-4 formulation exhibited the highest compressive modulus and shear-thinning properties. NIH-3T3 fibroblasts were incorporated into each bioink formulation to assess cell viability, attachment, and proliferation. The D-4 bioprinted construct demonstrated a 21% increase in cell viability compared to the D-1 sample and a threefold increase in fibroblast proliferation relative to the control. These findings indicated that the alginate-gelatin-CMC bioink significantly improved the mechanical and biological performance over conventional alginate-gelatin formulations, offering a promising cell niche for skin tissue engineering applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".