Methods of enhancing mechanical properties of hydrogel tubes used as nerve guidance channels in rat spinal cord injury
Bibliographic record
Abstract
Crosslinked, porous poly(2-hydroxyethyl methacrylate- co-methyl methacrylate) (PHEMA-MMA) tubes used as conduits in transected rat spinal cord, were prepared in cylindrical glass molds by using a new centrifugal casting process developed in our group. It has been shown previously that silane-modified glass molds influence morphology and elastic modulus of hydrogel tubes. We have investigated the effect of mold surface modification on hydrogel tube properties, by treating glass with three alkoxysilanes having either ethoxy, amine, or fluorocarbon end groups. Results have shown that ethoxy and amine-containing silanes had the greatest impact on creating hydrogel tubes with a continuous, biphasic wall structure, and enhanced elastic modulus. In a second approach to improve hydrogel tube mechanical strength, we have proposed that by employing helically wound plastic fibers embedded in the hydrogel tube wall, the transverse compressive strength of tubes will be significantly increased. After successfully synthesizing, and mechanically testing poly(caprolactone) fiber-reinforced hydrogel tubes in compression mode, results have confirmed our hypothesis.
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.
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".