Advances in 3D Bioprinting for Neuroregeneration: A Literature Review of Methods, Bioinks, and Applications
Bibliographic record
Abstract
Recent advancements in 3D-bioprinting technology have sparked a growing interest in its application for brain repair, encompassing tissue regeneration, drug delivery, and disease modeling. This literature review examines studies conducted over the past five years to assess the current state of research in this field. Common bioprinting methods and key parameters influencing their selection are explored, alongside an analysis of the diverse types of bioink utilized and their associated parameters. The extrusion-based 3D-bioprinting method emerged as the most widely studied and popular topic, followed by inkjet-based and laser-based bioprinting and stereolithography. Regarding bioinks, fibrin-based and collagen-based bioinks are predominantly utilized. Furthermore, this review elucidates how 3D bioprinting holds promise for neural tissue repair, regeneration, and drug screening, detailing the steps involved and various approaches employed. Neurovascular 3D printing and bioscaffold 3D printing stand out as the top two preferred methods for brain repair. The recent studies’ shortcomings and potential solutions to address them are also examined and discussed. Overall, by synthesizing recent findings, this review provides valuable insights into the potential of 3D bioprinting for advancing brain repairment strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".