Collagen/heparin scaffold combined with vascular endothelial growth factor promotes the repair of neurological function in rats with traumatic brain injury
Bibliographic record
Abstract
The objective of this study was to evaluate the therapy effects of a novel biological scaffold containing heparin, collagen and vascular endothelial growth factor (VEGF) in treating traumatic brain injury (TBI). In our research, a functional composite scaffold constituted by collagen, heparin and vascular endothelial growth factor was used to stimulate angiogenesis and improve nerve-tissue regeneration in a rat model of TBI. The composite scaffold possessed excellent mechanical properties and good porosity, and could effectively control the release rate of VEGF. Motor and cognitive functions such as motor evoked potential, Morris water maze test and modified neurological severity score were evidently improved after the scaffold was grafted onto the injury site in the rat TBI model. There was clearly improved restoration of damaged nerve tissue at the injured site. Furthermore, brain edema and inflammatory reactions were significantly alleviated. Newly formed neurons with associated synaptic structures, nerve fibers, myelin sheaths and functional angiogenesis with intact endothelium at the injury site were observed. In conclusion, our data revealed that the collagen/heparin scaffold combined with VEGF could create excellent microenvironment stimuli for damaged nerve-tissue regeneration, providing a potential strategy for treating 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 | 0.000 |
| 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".