Technical Strategies to Isolate Olfactory Ensheathing Cells for Intraspinal Implantation
Bibliographic record
Abstract
Over the past few years, the idea of using intraspinal implantations of olfactory ensheathing cells (OECs) as a therapeutic strategy to enhance recovery after spinal cord injury has quickly moved from experimentation with laboratory mammals to surgical approaches for paralyzed humans. Despite this progression, several important issues have yet to be thoroughly addressed: for instance, which of the many methods currently being used best yields enriched populations of OECs, and how such purity can be empirically tested and validated among different mammalian species, including humans. Here we offer an authoritative review of those methods used to isolate OECs from the olfactory mucosa and/or olfactory bulbs of rats, mice, dogs, pigs, non-human primates, and humans. As well, we assess which biomarkers are currently being utilized to determine the relative proportions of OECs and contaminating cells in these glial cultures. Although there have been numerous review articles regarding OECs in vitro, our review is unique in that it offers a critical assessment of the methods currently being used to generate cultures of mammalian OECs. More specifically, we examine the issue of culture contamination by phenotypically similar Schwann cells. This review is timely because recent clinical usage of OECs has come under intense criticism for a number of reasons, including the reliable identification of cultured human OECs. We believe that once these methodological issues of isolation and characterization of OECs have been resolved, this glial population will offer paralyzed individuals a truly viable cellular strategy for intraspinal therapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".