Proteomic evaluation reveals that olfactory ensheathing cells but not Schwann cells express calponin
Bibliographic record
Abstract
Human clinical trials have begun worldwide that use olfactory ensheathing cells (OECs) to ameliorate the functional deficits following spinal cord injury. These trials have been initiated largely because numerous studies have reported that OECs transform into Schwann Cell (SC)-like cells that myelinate axons and support new growth in adult rats with spinal injury. This phenomenon is remarkable because OECs do not myelinate olfactory axons in their native environment. Furthermore, these myelinating OECs are morphologically identical to SCs, which can invade the spinal cord after injury. One factor that has contributed to a possible confusion in the identification of these cells is the lack of phenotypic markers to distinguish unequivocally between OECs and SCs. Such markers are required to first assess the degree of SC contamination in OEC cultures before intraspinal implantation, and then to accurately identify grafted OECs and invading SCs in the injured spinal cord. Using two-dimensional gel electrophoresis, we have identified calponin, an actin binding protein, as the first definitive phenotypic marker that distinguishes between OECs and SCs in vitro and in vivo. We have also provided ultrastructural evidence that calponin-immunopositive OECs do not transform into myelinating SC-like cells after intraspinal implantation. Rather, the grafted OECs retain their morphological and neurochemical features. These data yield new insight into the phenotypic characteristics of OECs, which together with invading SCs can enhance regeneration of the injured spinal cord.
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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) | 0.000 | 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".