Tissue-mimetic hybrid bioadhesives for intervertebral disc repair
Bibliographic record
Abstract
Intervertebral disc (IVD) degeneration and herniation often necessitate surgical interventions including a discectomy with or without a nucleotomy, which results in a loss of the normal nucleus pulposus (NP) and a defect in the annulus fibrosus (AF). Due to the limited regenerative capacity of the IVD tissue, the annular tear may remain a persistent defect and result in recurrent herniation post-surgery. Bioadhesives are promising alternatives but show limited adhesion performance, low regenerative capacity, and inability to prevent re-herniation. Here, we report hybrid bioadhesives that combine an injectable glue and a tough sealant to simultaneously repair and regenerate IVD post-nucleotomy. The glue fills the NP cavity while the sealant seals the AF defect. Strong adhesion occurs with the IVD tissues and survives extreme disc loading. Furthermore, the glue can match native NP mechanically, and support the viability and matrix deposition of encapsulated cells, serving as a suitable cell delivery vehicle to promote NP regeneration. Besides, biomechanical tests with bovine IVD motion segments demonstrate the capacity of the hybrid bioadhesives to restore the biomechanics of bovine discs under cyclic loading and to prevent permanent herniation under extreme loading. This work highlights the synergy of bioadhesive and tissue-engineering approaches. Future works are expected to further improve the tissue specificity of bioadhesives and prove their efficacy for tissue repair and regeneration.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".