Preparation and Characterization of Alginate‐Based Bioinks for Three‐Dimensional Bioprinting of Cell‐Laden Constructs
Bibliographic record
Abstract
Biomaterial-based bioinks are increasingly utilized in bioprinting to engineer three-dimensional (3D) constructs with living cells for tissue engineering and disease modeling. Among various bioinks explored, alginate-based formulations stand out due to their good biocompatibility, mild gelation conditions, tunable mechanical properties, and ease of crosslinking via divalent cations such as calcium. Despite their widespread use, standardized protocols for preparing alginate-based bioinks and characterizing bioprinted constructs have not been well documented. Our laboratory has developed and validated reproducible methods for preparing a variety of alginate-based bioinks and printing cell-laden constructs tailored for diverse applications. In this article, we present detailed step-by-step protocols covering bioink preparation and rheological characterization, extrusion-based bioprinting of cell-laden constructs, post-printing culture and co-culture techniques, printability assessment, and live/dead and immunofluorescence assays. These protocols serve as a standardized framework for the fabrication and characterization of 3D bioprinted alginate-based cell-laden constructs, thereby facilitating translational research in tissue engineering, disease modeling, and preclinical therapeutic development. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Bioink preparation Basic Protocol 2: Bioink characterization using rheology Basic Protocol 3: Scaffold design and bioprinting Support Protocol: 3D-printing parameter determination Basic Protocol 4: Printability and cell viability analyses, and immunofluorescence assay.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 0.001 |
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".