Multifunctional Core-Shell Structured Manganese Nanozyme Incorporated Hydrogel for Spinal Cord Injury Microenvironment Reconstruction
Bibliographic record
Abstract
Spinal cord injury (SCI) leads to motor and sensory loss due to damage to the spinal cord's nerve fibers. Secondary inflammation and excessive reactive oxygen species (ROS) create a hostile environment for regeneration. To address oxidative stress and inflammation simultaneously, we developed a composite QUE-Mn3O4@PDA-GelMA hydrogel that integrates ROS-scavenging activity of Mn3O4 nanozyme with the anti-inflammatory properties of quercetin within a photocrosslinkable GelMA hydrogel matrix. Mn3O4 nanozymes were synthesized and encapsulated via dopamine polymerization, then functionalized with quercetin to form Mn3O4@PDA-GelMA, which was subsequently embedded in GelMA and crosslinked. Further, structural and physicochemical characterization confirmed successful PDA coating, crystallinity consistent with Mn3O4, and uniform nanoparticle distribution. Additionally, the composite hydrogel exhibited suitable mechanical properties and degradation behavior for spinal tissue mimicry. In vitro assays demonstrated that the hydrogel effectively scavenged ROS, reduced pro-inflammatory macrophage polarization, and maintained neural cell viability at the selected concentration. These combined effects suggest that the hydrogel creates a more favorable microenvironment for neural protection and potential regeneration. Therefore, the QUE-Mn3O4@PDA-GelMA hydrogel provides a suitable environment for neural regeneration that concurrently attenuates oxidative stress and inflammation, positioning it as a promising candidate for enhancing SCI repair.
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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".