Drug‐releasing Microspheres for Stem Cell Differentiation
Bibliographic record
Abstract
The ability of stem cells to differentiate into specialized cells make them a valuable tool for therapeutic applications. 3D bioprinting, a subset of additive manufacturing, uses bioinks composed of cells and biomaterials to create living tissues. The use of bioactive factors like small molecules and proteins can promote stem cell differentiation into the desired cell phenotypes for tissue regeneration. Small molecules can accelerate the process of regeneration in tissue engineering, maintain bioactivity in a biological environment, and minimize the costs associated with this process. Additionally, they can be encapsulated in specialized drug-delivery devices called microspheres for controlled release. Microspheres are small (1-1000 μm) spherical particles usually made from biodegradable and biocompatible polymers that can be loaded with drugs and other bioactive components. They can then be integrated into stem-cell-laden bioinks used to form bioprinted tissues, where they will release the encapsulated drug and promote differentiation of stem cells into the desired mature cell type. Microspheres can be widely used to encapsulate a broad range of therapeutic agents, including hydrophilic and hydrophobic small molecule drugs, DNA, and proteins. The release of encapsulated molecules occurs through degradation and erosion of the polymer matrix. This article provides detailed protocols for fabricating and sterilizing drug-releasing microspheres made from poly-ε-caprolactone, a promising biodegradable polymer often used for controlled drug delivery due to its biocompatibility and biodegradation kinetics. Additional protocols describe characterization of the loading and size of microspheres as well as incorporation of microspheres into a fibrin-based bioink for 3D bioprinting. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Fabrication of drug-releasing PCL microspheres Support Protocol 1: Preparation of microspheres for determination of encapsulation efficiency by HPLC Support Protocol 2: Preparation of microspheres for SEM analysis Basic Protocol 2: Incorporation of microspheres into fibrin-based bioink.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".