Sciatic Nerve Regeneration in Mice Using A PLGA MicrogroovePatterned Conduit Fills with Microfiber
Bibliographic record
Abstract
Nerve conduit is one of common strategy for peripheral nerve repair in clinical, but the repairing process is slow and complex leading to a poor result of functional recovery.From neuroanatomy inspiration, a nerve conduit combine with micro groove patterned and microfiber for guiding nerve regeneration in animal test was evaluation in this study.The master mold of poly(lactic-co-glycolic acid) microgroove patterned membrane and microfiber were manufactured by photolithography and poly(dimethylsiloxane) casting.A bunch of microfiber was wrapped with micro-patterned membrane forming a filler type conduit (Conduit 2.0).The microgroove pattern only conduit was used as control group (Conduit 2.1).Sample was then implanted into injured FVB mice sciatic nerve for 8 week to emulate the trauma recovery.According to the SEM image, these micro structures were not degradation after 8 weeks implantation.The neurite outgrowth and cell migration on conduit can be modulated by mechanical causes of surrounding environment.However, some fragmentation microfibers were observed to disrupt the cell migrating direction, and interfered the guiding ability of microgroove patterned on inner wall.In Rotarod test, the mouse implanted using the conduit with microfiber showed a worse result compare to micro pattern only conduit at early stage, but did not show difference at final stage.The filling type conduit presented as dramatically poor recovery on the compound muscle action potential measurement after 8 weeks implantation, since the microfiber occupied most space leading to a lower mass transportation rate.In conclusion, microfiber filled conduit is a good tool for guide cell migration and neurite extension, but the degraded fragment and filling density still need to settle up to increase mass transportation inside conduit.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".