Human Enteric Glial Cells Alleviate Damaged Adult Sensory Neurons in Rats
Bibliographic record
Abstract
Spinal cord injury affects millions across the globe. Little is known regarding the cellular mechanism of injury and, unfortunately, there are few viable treatment options. One potential option is the transplantation of peripheral nerves into the site of injury. The complicating factor is that the peripheral nervous system is not readily accessible, and thus the procedure introduces the risk of disrupting the function of other areas. However, this risk is minimized if the nerves are extracted from the enteric system, which is embedded in the lining of the gastrointestinal tract. This system bears similarities to the central nervous system, has remarkable plasticity, and releases growth factors that not only facilitate regeneration of neurons but also protect the enteric nervous system from damage. Previous research shows that rat enteric glial cells induce regeneration in rodent neurons in vivo with a crushed spinal cord, and in vitro to dorsal root ganglia treated with semaphorin-3A to mimic spinal cord injury. We examined whether human enteric glia demonstrate similar effects in vitro on rodent dorsal root ganglia. Our experiments to date show that this treatment is viable, emphasizing its use in clinical trials. The success of this technique is largely due to the fact that the donor cells originate from the host, which minimizes the chance of rejection.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".