Design of Protein‐Releasing Chitosan Channels
Bibliographic record
Abstract
After traumatic injury to the spinal cord, the neural tissue degenerates, resulting in lost function below the site of injury. Promoting axonal regeneration after injury remains a challenge; however, guidance channels have demonstrated some success when combined with cellular and protein therapies. One of the limitations of current guidance channels is the inability to deliver therapeutically relevant molecules in situ, within the guidance channel, to enhance regeneration. In an effort to provide a system for local and sustained drug release, poly(lactide-co-glycolide) (PLGA) microspheres were embedded into chitosan guidance channels by a novel spin-coating technique. The method was designed to create guidance channels with the appropriate dimensions for implantation into the spinal cord, with special attention paid to the wall thickness. The release and bioactivity of a model protein, alkaline phosphatase, was followed from the channels and compared to those from free-floating microspheres over a 90-day period. Since chitosan formulations often require the use of acidic solutions, careful attention was paid to redesign the process to minimize exposure of PLGA microspheres to acid. This was achieved as demonstrated by release and bioactivity data where alkaline phosphatase released from chitosan/microsphere channels followed a profile and bioactivity similar to those of free floating microspheres.
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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 | 0.001 | 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".