Composite Hydrogel Sealants for Annulus Fibrosus Repair
Bibliographic record
Abstract
Intervertebral disc (IVD) herniation is a leading cause of disability and lower back pain, causing enormous socioeconomic burdens. The standard of care for disc herniation is nucleotomy, which alleviates pain but does not repair the annulus fibrosus (AF) defect nor recover the biomechanical function of the disc. Existing bioadhesives for AF repair are limited by insufficient adhesion and significant mechanical and geometrical mismatch with the AF tissue, resulting in the recurrence of protrusion or detachment of bioadhesives. Here, we report a composite hydrogel sealant constructed from a composite of a three-dimensional (3D)-printed thermoplastic polyurethane (TPU) mesh and tough hydrogel. We tailored the fiber angle and volume fraction of the TPU mesh design to match the angle-ply structure and mechanical properties of native AF. Also, we proposed and tested three types of geometrical design of the composite hydrogel sealant to match the defect shape and size. Our results show that the sealant could mimic native AF in terms of the elastic modulus, flexural modulus, and fracture toughness and form strong adhesion with the human AF tissue. The bovine IVD tests show the effectiveness of the composite hydrogel sealant for AF repair and biomechanics recovery and for preventing herniation with its heightened stiffness and superior adhesion. By harnessing the combined capabilities of 3D printing and bioadhesives, these composite hydrogel sealants demonstrate promising potential for diverse applications in tissue repair and regeneration.
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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".