Design of Protein‐Releasing Chitosan Channels
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
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.
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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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