Investigation of novel carboxymethyl chitosan-based bioinks for 3D bioprinting of neural tissues
Bibliographic record
Abstract
Abstract The formulation of bioinks is critical for successful 3D bioprinting. It influences printability, stability, and cell behavior. One of the main demands in 3D bioprinting is the development of bioink formulations that can balance long-term cell viability and compositional similarities to the extracellular matrix (ECM) with rheological properties for 3D printing. To address this challenge, this study tested new bioinks using carboxymethyl chitosan (N,O-CMCS or O-CMCS), alginate, and fibrin, which are promising biomaterials due to their biocompatibility and likeness to the ECM. 3D bioprinting of neural tissues comes with additional challenges because neural cells are highly sensitive to environmental conditions. Therefore, we optimized our bioink formulations for the 3D bioprinting of neural progenitor cells derived from human induced pluripotent stem cells (hiPSC-NPC). Here we report a neural tissue constructed 3D bioprinted with a hiPSC-NPC-laden 1% N,O-CMCS, 1% alginate, and 20 mg ml−1 fibrin. This formulation exhibited uniform consistency and minimal extrusion force fluctuations (approximately 8 KPa), indicating homogeneity and optimal printability using an extrusion-based bioprinter. In contrast, O-CMCS formulations did not support neural tissue differentiation while higher concentrations of N,O-CMCS or alginate (3% w/v) resulted in increased viscosity and poorly defined scaffolds. The optimized bioink demonstrated significant water retention, swelling up to 15 times its original weight without losing structural integrity, thus providing a conducive environment for cell culture. Live/dead staining revealed over 60% cell viability over 30 d, underscoring its suitability for long-term cell applications. Immunocytochemistry confirmed that the optimized N,O-CMCS-based bioink effectively guided cells toward further differentiation into neurons and astrocytes, thus forming a 3D bioprinted construct that is able to replicate different neural cell types found in the neural tissue. The optimized bioink described in this study lays the groundwork for future works that will focus on detailing how different CMCS groups affect tissue maturation and functionality in 3D bioprinted constructs that can potentially be used for future neural tissue modeling and drug screening.
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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) | 0.000 | 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 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".