Localized and Sustained Delivery of Fibroblast Growth Factor-2 from a Nanoparticle-Hydrogel Composite for Treatment of Spinal Cord Injury
Bibliographic record
Abstract
After traumatic spinal cord injury, grossly injured blood vessels leak blood and fluid into the parenchyma, leading to a large cystic cavity. Fibroblast growth factor-2 (FGF2) can reduce immediate vasoconstriction of vessels in the tissue surrounding the primary injury and promote angiogenesis. A localized delivery system would both achieve restricted delivery of FGF2 to the spinal cord and limit possible systemic effects such as mitogenesis. To enhance the endogenous angiogenic response after spinal cord injury, FGF2 was encapsulated in poly(lactide-co-glycolide) (PLGA) nanoparticles which were embedded in a biopolymer blend of hyaluronan and methylcellulose (HAMC) and then injected into the intrathecal space. Treatment began immediately after a 26 g clip compression spinal cord injury in rats and consisted of intrathecal delivery of FGF2 from the HAMC/PLGA/FGF2 composite. Control animals received intrathecal HAMC loaded with blank nanoparticles, intrathecal HAMC alone or intrathecal artificial cerebrospinal fluid alone. Sustained and localized delivery of FGF2 from composite HAMC/PLGA/FGF2 achieved higher blood vessel density in the dorsal horns 28 days post-injury, due to either greater angiogenesis near the epicenter of the injury or vasoprotection acutely after spinal cord injury. Importantly, delivery of FGF2 from composite HAMC/PLGA/FGF2 did not produce proliferative lesions that had been previously reported for FGF2 delivered locally using a minipump/catheter. These results suggest that localized and sustained delivery with composite HAMC/PLGA/FGF2 is an excellent system to deliver biomolecules directly to the spinal cord, thereby circumventing the blood spinal cord barrier and avoiding systemic side effects.
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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".