Alginate-gelatin-carboxymethylcellulose bioink designing and bioprinting to improve fibroblast cell niche
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it