Exploiting the impact of the secretome of MSCs isolated from different tissue sources on neuronal differentiation and axonal growth
Bibliographic record
Abstract
Cell transplantation using Mesenchymal stem cell (MSC) secretome have recently been presented as a possible free-based therapy for CNS related disorders. MSC secretome is rich in several bio-factors that act synergically towards the repair of damaged tissues, thus making it an ideal candidate for regenerative applications. Great effort is currently being made to map the molecules that compose the MSC secretome. Previous proteomic characterization of the secretome (in the form of conditioned media - CM) of MSCs derived from adipose tissue (ASC), bone-marrow (BMSC) and umbilical cord (HUCPVC) was performed by our group, where proteins relevant for neuroprotection, neurogenic, neurodifferentiation, axon guidance and growth functions were identified. Moreover, we have found significant differences among the expression of several molecules, which may indicate that their therapeutic outcome might be distinct. Having this in mind, in the present study, the neuroregulatory potential of ASC, BMSC and HUCPVC CM in promoting neurodifferentiation and axonal outgrowth was tested in vitro, using human telencephalon neuroprogenitor cells and dorsal root ganglion explants, respectively. The CM from the three MSC populations induced neuronal differentiation from human neural progenitor cells, as well as neurite outgrowth from dorsal root ganglion explants. Moreover, all the MSC populations promoted the same extent of neurodifferentiation, while ASC CM demonstrated higher potential in promoting axonal growth.
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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".