Tunable degradation of low-fouling carboxybetaine-hyaluronic acid hydrogels for applications in cell encapsulation
Bibliographic record
Abstract
Abstract Low-fouling hydrogels with tunable degradation rates and biochemical environments have the potential to improve adoptive cell therapies for cancer immunotherapy and regenerative medicine. To this end, we developed in situ gelling hydrogels from low-fouling poly(carboxybetaine-co-maleimide) (pCBM) random copolymers and thiolated hyaluronic acid (HA-SH). pCBM-HA hydrogel enzymatic degradation rates were tuned 5 fold by altering pCBM composition (4, 11, and 16 maleimide mol%) and 2.3 fold by HA-SH concentration (1–2 wt%). pCBM-HA gels were low-fouling towards bovine serum albumin (BSA; adsorbed ∼20 μ g cm −2 ) and resisted fibroblast adhesion. To control pCBM-HA bioactivity, the cell adhesive peptide CGRGDS was immobilized on pCBM to promote fibroblast adhesion (39% decrease in circularity), which increased metabolic activity by ∼50%. pCBM-HA modified with CGRGDS enhanced the metabolic activity of encapsulated T cells by ∼21% compared to gels without HA, indicating their potential for immunotherapies. Low-fouling pCBM-HA hydrogels provide a vehicle with tunable degradation rates and biochemical environments for encapsulation applications in cell adoptive therapies.
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".