Innovative 3D bioprinting approaches for advancing brain science and medicine: a literature review
Bibliographic record
Abstract
The rapid advancements in 3D printing technology have revolutionized the field of tissue engineering, particularly in the development of neural tissues for the treatment of nervous system diseases. Brain neural tissue, composed of neurons and glial cells, plays a crucial role in the functioning of the brain, spinal cord, and peripheral nervous system by transmitting nerve impulses and processing information. By leveraging 3D bioprinting and bioinks, researchers can create intricate neural scaffolds that facilitate the proliferation and differentiation of nerve cells, thereby promoting the repair and regeneration of damaged neural tissues. This technology allows for the precise spatial arrangement of various cell types and scaffold materials, enabling the construction of complex neural tissue models that closely mimic the natural architecture of the brain. Human-induced pluripotent stem cells (hiPSCs) have emerged as a groundbreaking tool in neuroscience research and the potential treatment of neurological diseases. These cells can differentiate into diverse cell types within the nervous system, including neurons, astrocytes, microglia, oligodendrocytes, and Schwann cells, providing a versatile platform for studying neural networks, neurodevelopment, and neurodegenerative disorders. The use of hiPSCs also opens new avenues for personalized medicine, allowing researchers to model diseases and develop targeted therapies based on individual patient profiles. Despite the promise of direct hiPSC injections for therapeutic purposes, challenges such as poor localization and limited integration have led to the exploration of biomaterial scaffolds as supportive platforms for cell delivery and tissue regeneration. This paper reviews the integration of 3D bioprinting technologies and bioink materials in neuroscience applications, offering a unique platform to create complex brain and tissue architectures that mimic the mechanical, architectural, and biochemical properties of native tissues. These advancements provide robust tools for modelling, repair, and drug screening applications. The review highlights current research, identifies research gaps, and offers recommendations for future studies on 3D bioprinting in neuroscience. The investigation demonstrates the significant potential of 3D bioprinting to fabricate brain-like tissue constructs, which holds great promise for regenerative medicine and drug testing models. This approach offers new avenues for studying brain diseases and potential treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".