Faculty Opinions recommendation of Hierarchically aligned fibrin nanofiber hydrogel accelerated axonal regrowth and locomotor function recovery in rat spinal cord injury.
Bibliographic record
Abstract
Background: Designing novel biomaterials that incorporate or mimic the functions of extracellular matrix to deliver precise regulatory signals for tissue regeneration is the focus of current intensive research efforts in tissue engineering and regenerative medicine. Methods and results:To mimic the natural environment of the spinal cord tissue, a threedimensional hierarchically aligned fibrin hydrogel (AFG) with oriented topography and soft stiffness has been fabricated by electrospinning and a concurrent molecular self-assembling process.In this study, the AFG was implanted into a rat dorsal hemisected spinal cord injury model to bridge the lesion site.Host cells invaded promptly along the aligned fibrin hydrogels to form aligned tissue cables in the first week, and then were followed by axonal regrowth.At 4 weeks after the surgery, neurofilament (NF)-positive staining fibers were detected near the rostral end as well as the middle site of defect, which aligned along the tissue cables.Abundant NF-and GAP-43-positive staining indicated new axon regrowth in the oriented tissue cables, which penetrated throughout the lesion site in 8 weeks.Additionally, the abundant blood vessels marked with RECA-1 had reconstructed within the lesion site at 4 weeks after surgery.Basso-Beattie-Bresnahan scoring showed that the locomotor performance of the AFG group recovered much faster than that of blank control group or the random fibrin hydrogel (RFG) group from 2 weeks after surgery.Furthermore, diffusion tensor imaging tractography of MRI confirmed the optimal axon fiber reconstruction compared with the RFG and control groups.Conclusion: Taken together, our results suggested that the AFG scaffold provided an inductive matrix for accelerating directional host cell invasion, vascular system reconstruction, and axonal regrowth, which could promote and support extensive aligned axonal regrowth and locomotor function recovery.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.041 |
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".