Injectable hydrogels for central nervous system therapy
Bibliographic record
Abstract
Diseases and injuries of the central nervous system (CNS) including those in the brain, spinal cord and retina are devastating because the CNS has limited intrinsic regenerative capacity and currently available therapies are unable to provide significant functional recovery. Several promising therapies have been identified with the goal of restoring at least some of this lost function and include neuroprotective agents to stop or slow cellular degeneration, neurotrophic factors to stimulate cellular growth, neutralizing molecules to overcome the inhibitory environment at the site of injury, and stem cell transplant strategies to replace lost tissue. The delivery of these therapies to the CNS is a challenge because the blood-brain barrier limits the diffusion of molecules into the brain by traditional oral or intravenous routes. Injectable hydrogels have the capacity to overcome the challenges associated with drug delivery to the CNS, by providing a minimally invasive, localized, void-filling platform for therapeutic use. Small molecule or protein drugs can be distributed throughout the hydrogel which then acts as a depot for their sustained release at the injury site. For cell delivery, the hydrogel can reduce cell aggregation and provide an adhesive matrix for improved cell survival and integration. Additionally, by choosing a biodegradable or bioresorbable hydrogel material, the system will eventually be eliminated from the body. This review discusses both natural and synthetic injectable hydrogel materials that have been used for drug or cell delivery to the CNS including hyaluronan, methylcellulose, chitosan, poly(N-isopropylacrylamide) and Matrigel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".