3D Bioprinting Human‐Induced Pluripotent Stem Cells and Drug‐Releasing Microspheres to Produce Responsive Neural Tissues
Bibliographic record
Abstract
3D bioprinting can produce complex human tissue mimics using stem cells (SCs). Herein, cylindrical constructs containing human‐induced pluripotent stem cell (hiPSC)‐derived neural progenitor cells (NPCs) encapsulated in a fibrin‐based bioink containing polycaprolactone (PCL)–retinoic acid (RA) and purmorphamine (puro)‐releasing microspheres are bioprinted in a layer‐by‐layer fashion using the microfluidic‐based RX1 bioprinter to engineer responsive neural tissues. The differentiated constructs contain neurons expressing ChAT, GABA, and MAP2, astrocytes expressing GFAP, and oligodendrocytes expressing O4 as indicated by immunocytochemistry and flow cytometry analysis on days 30 and 45. The bioprinted tissues also respond to treatment with acetylcholine (Ach) and gamma‐aminobutyric acid (GABA) on days 30 and 45. The use of microsphere‐laden bioinks efficiently promotes neural tissue differentiation and maturation in situ using a lower amount of morphogens in comparison with using soluble drugs. This bioprinting strategy serves as a cost‐effective solution for engineering humanized neural tissues.
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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".