Injectable hydrogels for central nervous system therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it