Synthesis of Alginate/Collagen Bioink for Bioprinting Respiratory Tissue Models
Bibliographic record
Abstract
Synthesis of bioinks for bioprinting of respiratory tissue requires considerations related to immunogenicity, mechanical properties, printability, and cellular compatibility. Biomaterials can be tailored to provide the appropriate combination of these properties through the synergy of materials with individual pros and cons. Sodium alginate, a water-soluble polymer derived from seaweed, is a cheap yet printable biomaterial with good structural properties; however, it lacks physiological relevance and cell binding sites. Collagen, a common component in the extra cellular matrix of many tissues, is expensive and lacks printability; however, it is highly biocompatible and exhibits sites for cellular binding. This paper presents our study on the synthesis of bioinks from alginate and collagen for use in bioprinting respiratory tissue models. Bioinks were synthesized from 40 mg/mL (4%) alginate and 3 mg/mL (0.3%) collagen in varying ratios (1:0, 4:1, 3:1, 2:1, and 1:1); then examined in terms of rheological properties, printability, compressive, and tensile properties and cellular compatibility. The results illustrate that the ratio of alginate to collagen has a profound impact on bioink performance and that, among the examined ratios, the 3:1 ratio is the most appropriate for use in bioprinting respiratory tissue scaffolds.
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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) | 0.001 | 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".