Physical and Mechanical Characterization of Fibrin-Based Bioprinted Constructs Containing Drug-Releasing Microspheres for Neural Tissue Engineering Applications
Bibliographic record
Abstract
Three-dimensional bioprinting can fabricate precisely controlled 3D tissue constructs. This process uses bioinks—specially tailored materials that support the survival of incorporated cells—to produce tissue constructs. The properties of bioinks, such as stiffness and porosity, should mimic those found in desired tissues to support specialized cell types. Previous studies by our group validated soft substrates for neuronal cultures using neural cells derived from human-induced pluripotent stem cells (hiPSCs). It is important to confirm that these bioprinted tissues possess mechanical properties similar to native neural tissues. Here, we assessed the physical and mechanical properties of bioprinted constructs generated from our novel microsphere containing bioink. We measured the elastic moduli of bioprinted constructs with and without microspheres using a modified Hertz model. The storage and loss modulus, viscosity, and shear rates were also measured. Physical properties such as microstructure, porosity, swelling, and biodegradability were also analyzed. Our results showed that the elastic modulus of constructs with microspheres was 1032 ± 59.7 Pascal (Pa), and without microspheres was 728 ± 47.6 Pa. Mechanical strength and printability were significantly enhanced with the addition of microspheres. Thus, incorporating microspheres provides mechanical reinforcement, which indicates their suitability for future applications in neural tissue engineering.
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) | 0.000 | 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".