Multicellular Layered Nanofibrous Poly(Oligo Ethylene Glycol Methacrylate) (POEGMA)‐Based Hydrogel Scaffolds via Reactive Cell Electrospinning
Bibliographic record
Abstract
While hydrogels are demonstrated to be effective scaffolds for soft tissue engineering, existing fabrication techniques pose limitations in terms of being able to reproduce both the micro/nanofibrous structures of native extracellular matrix as well as the spatial arrangement of different cell types inherent of more complex tissues. Herein, a reactive cell electrospinning strategy is described using hydrazide and aldehyde-functionalized poly(oligoethylene glycol methacrylate) precursor polymers that can create nanofibrous hydrogel scaffolds with controllable local cell gradients using a sequential all-aqueous process that does not require additives or external energy. Cells can be encapsulated directly during the fabrication process in different layers within the scaffold, enabling localized segregation of different cell types within the structures without compromising their capacity to proliferate (≈4-fold increase in cell density over a 14 day incubation period). This sequential reactive electrospinning approach thus offers promise to generate coculture fibrous hydrogel networks in which both the nanoscale architecture and the cell distribution can be controlled, as it is essential to recreate more complex types of 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".