Exploiting the role of nanoparticles for use in hydrogel-based bioprinting applications: concept, design, and recent advances
Bibliographic record
Abstract
Three-dimensional (3D) bioprinting is an emerging tissue engineering approach that aims to develop cell or biomolecule-laden, complex polymeric scaffolds with high precision, using hydrogel-based "bioinks". Hydrogels are water-swollen, highly crosslinked polymer networks that are soft, quasi-solid, and can support and protect biological materials. However, traditional hydrogels have weak mechanical properties and cannot retain complex structures. They must be reinforced with physical and chemical manipulations to produce a mechanically resilient bioink. Over the past few years, we have witnessed an increased use of nanoparticles and biological moiety-functionalized nanoparticles to fabricate new bioinks. Nanoparticles of varied size, shape, and surface chemistries can provide a unique solution to this problem primarily because of three reasons: (a) nanoparticles can mechanically reinforce hydrogels through physical and chemical interactions. This can favorably influence the bioink's 3D printability and structural integrity by modulating its rheological, biomechanical, and biochemical properties, allowing greater flexibility to print a wide range of structures; (b) nanoparticles can introduce new bio-functionalities to the hydrogels, which is a key metric of a bioink's performance, influencing both cell-material and cell-cell interactions within the hydrogel; (c) nanoparticles can impart "smart" features to the bioink, making the tissue constructs responsive to external stimuli. Responsiveness of the hydrogel to magnetic field, electric field, pH changes, and near-infrared light can be made possible by the incorporation of nanoparticles. Additionally, bioink polymeric networks with nanoparticles can undergo advanced chemical crosslinking, allowing greater flexibility to print structures with varied biomechanical properties. Taken together, the unique properties of various nanoparticles can help bioprint intricate constructs, bringing the process one step closer to complex tissue structure and organ printing. In this review, we explore the design principles and multifunctional properties of various nanomaterials and nanocomposite hydrogels for potential, primarily extrusion-based bioprinting applications. We illustrate the significance of biocompatibility of the designed nanocomposite hydrogel-based bioink for clinical translation and discuss the different parameters that affect cell fate after cell-nanomaterial interaction. Finally, we critically assess the current challenges of nanoengineering bioinks and provide insight into the future directions of potential hydrogel bioinks in the rapidly evolving field of bioprinting.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".