Role of temperature on bio-printability of gelatin methacryloyl bioink in two-step cross-linking strategy for tissue engineering applications
Bibliographic record
Abstract
Additive manufacturing has shown promising results in reconstructing three-dimensional (3D) living tissues for various applications, including tissue engineering, regenerative medicine, drug discovery, and high-throughput drug screening. In extrusion-based bioprinters, stable formation of filaments and high-fidelity deposition of bioinks are the primary challenges in fabrication of physiologically relevant tissue constructs. Among various bioinks, gelatin methacryloyl (GelMA) is known as a photocurable and physicochemically tunable hydrogel with a demonstrated biocompatibility and tunable biodegradation properties. The two-step crosslinking of GelMA (reversible thermal gelation and permanent photo-crosslinking) has attracted researchers to make complex tissue constructs. Despite promising results in filament formation and printability of this hydrogel, the effect of temperature on physicochemical properties, cytocompatibility, and biodegradation of the hydrogel are to be investigated. This work studies the effect of thermoreversible, physical crosslinking on printability of GelMA. The results of 3D printing of GelMA at different temperatures followed by irreversible chemical photo-crosslinking show that the decrease in temperature improves the filament formation and shape fidelity of the deposited hydrogel, particularly at the temperatures around 15 °C. Time dependant mechanical testing of the printed samples revealed that decreasing the extruding temperature increases the elastic properties of the extruded filaments. Furthermore, our novel approach in minimizing the slippage effect during rheological study enabled to measure changes in linear and non-linear viscoelastic properties of the printed samples at different temperatures. A considerable increase in storage modulus of the extruded samples printed at lower temperatures confirms their higher solid behavior. Scanning electron microscopy revealed a remarkable decrease in porosity of the extruded hydrogels by decreasing the temperature. Chemical analysis by Fourier-transform infrared spectroscopy and circular dichroism showed a direct relationship between the coil-helix transition in hydrogel macromers and its physical alterations. Finally, biodegradation and cytocompatibility of the extruded hydrogels decreased at lower extruding temperatures.
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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) | 0.001 | 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".