Secretome of Undifferentiated Neural Progenitor Cells Induces Histological and Motor Improvements in a Rat Model of Parkinson's Disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative movement disorder that results from the death of dopamine (DA) neurons. Over recent years, differentiated or undifferentiated neural stem cells (NSCs) transplantation has been widely used as a means of cell replacement therapy. However, compelling evidence has brought attention to the array of bioactive molecules produced by stem cells, defined as secretome. As described in the literature, other cell populations have a high-neurotrophic activity, but little is known about NSCs. Moreover, the exploration of the stem cell secretome is only in its initial stages, particularly as applied to neurodegenerative diseases. Thus, we have characterized the secretome of human neural progenitor cells (hNPCs) through proteomic analysis and investigated its effects in a 6-hydroxidopamine (6-OHDA) rat model of PD in comparison with undifferentiated hNPCs transplantation. Results revealed that the injection of hNPCs secretome potentiated the histological recovery of DA neurons when compared to the untreated group 6-OHDA and those transplanted with cells (hNPCs), thereby supporting the functional motor amelioration of 6-OHDA PD animals. Additionally, hNPCs secretome proteomic characterization has revealed that these cells have the capacity to secrete a wide range of important molecules with neuroregulatory actions, which are most likely support the effects observed. Overall, we have concluded that the use of hNPCs secretome partially modulate DA neurons cell survival and ameliorate PD animals' motor deficits, disclosing improved results when compared to cell transplantation approaches, indicating that the secretome itself could represent a route for new therapeutic options for PD regenerative medicine. Stem Cells Translational Medicine 2018;7:829-838.
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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 | 0.001 |
| 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".