Fast Thermoresponsive Poly(oligoethylene glycol methacrylate) (POEGMA)-Based Nanostructured Hydrogels for Reversible Tuning of Cell Interactions
Bibliographic record
Abstract
Reactive electrospinning is demonstrated as a viable method to create fast-responsive and degradable macroporous thermoresponsive hydrogels based on poly(oligoethylene glycol methacrylate) (POEGMA). Hydrazide- and aldehyde-functionalized POEGMA precursor polymers were coelectrospun to create hydrazone cross-linked nanostructured hydrogels in a single processing step that avoids the need for porogens, phase separation-driving additives, or scaffold postprocessing. The resulting nanostructured hydrogels can respond reversibly and repeatedly to changes in external temperature within seconds, in contrast to the minutes-to-hours response time observed with bulk hydrogels. Furthermore, nearly quantitative cell delamination can be achieved within 2 min of incubation at 4 °C, resulting in the recovery of as many or more (as well as more proliferatively active) cells from the substrate relative to the conventional trypsinization protocol. The combined macroporosity, nanoscale feature size, and interfacial switching potential of these nanostructured hydrogels thus offer promise for manipulating cell-hydrogel interactions as well as other applications in which rapid responses to external stimuli are desirable.
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 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".