3D printing collagen/heparin sulfate scaffolds boost neural network reconstruction and motor function recovery after traumatic brain injury in canine
Bibliographic record
Abstract
Tissue engineering is considered highly promising for the repair of traumatic brain injury (TBI), and accumulating evidence has proved the efficacy of biomaterials and 3D printing. Although collagen is famous for its natural properties, some defects still restrict its potential applications in tissue repair. In this experimental study, we fabricated a kind of scaffold with collagen and heparin sulfate via 3D printing, which possesses favorable physical properties and suitable degradation rate along with satisfactory cytocompatibility. After implantation, the results of motor evoked potentials (MEPs) showed that the latency and amplitude can both be improved in hemiplegic limbs, and the structural integrity of the cerebral cortex and corticospinal tract can be enhanced significantly under magnetic resonance imaging (MRI) evaluation. Additionally, the results of in situ hybridization (ISH) and immunofluorescence staining also revealed the facilitating role of 3D printing collagen/heparin sulfate scaffolds on vascular and neural regeneration. Moreover, the individuals implanted with this kind of scaffold present better gait characteristics and preferable electromyography and myodynamia. In general, 3D printed collagen/heparin sulfate scaffolds have superb performance in both structural repair and functional improvement and may offer a new strategy for the repair of TBI.
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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 | 0.001 |
| 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 | 0.001 |
| 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".