Exploiting the impact of the secretome of MSCs isolated from different tissue sources on neuronal differentiation and axonal growth
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it