Characterization of hyaluronan–methylcellulose hydrogels for cell delivery to the injured spinal cord
Bibliographic record
Abstract
No effective clinical treatment currently exists for traumatic spinal cord injury. Cell replacement therapy holds promise for attaining functional repair. Cells may be delivered directly or near the injury site; however, this strategy requires a delivery vehicle to maintain cell viability. We have identified an injectable, biocompatible, and biodegradable hydrogel scaffold composed of hyaluronan (HA) and methylcellulose (MC) that may be an effective scaffold for therapeutic cell delivery. The purpose of the present study was to determine the effects of polymer concentration on HAMC mechanical strength, gelation time, and cell viability. The yield stress of HAMC, a measure of mechanical stiffness, was tunable via manipulation of MC and HA content. Measurement of the elastic and storage moduli as functions of time revealed that HAMC gels in less than 5 min at physiological temperatures. Human umbilical tissue-derived cells encapsulated in HAMC were homogenously and stably distributed over 3 days in culture and extended processes into the scaffold. Cell viability was stable over this period in all but the most concentrated HAMC formulation. Because of its strength-tunability, rapid gelation, and ability to maintain cell viability, HAMC is a promising vehicle for cell delivery and is being tested in ongoing in vivo studies.
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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) | 0.000 | 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".