Effects of Adipose-Derived Stem Cell Extract on Peripheral Nerve Cells
Bibliographic record
Abstract
Adipose-derived stem cells (ADSCs) can be obtained from adipose tissue, which is considered clinically dispensable. ADSCs have the ability to differentiate not only into adipocytes and osteoblasts but also into various other cell types, such as nerve cells and cardiomyocytes. However, the clinical application of ADSCs in stem cell therapy is hampered by the risk of transplant rejection and the need for facilities for their storage and transportation. In comparison, cell extracts (CEs) obtained from stem cells by freeze-thawing and lysis are less tumorigenic and immunogenic. However, there are currently no studies on the application of ADSC-derived CEs (ADSC-CEs) in peripheral nerve regeneration. Therefore, in this study, we investigated the effects of ADSC-CEs on proliferation and neurite extension in peripheral nerve cells. ADSCs were harvested from the inguinal region of mice, and ADSC-CEs were obtained following repeated freeze-thawing of ADSCs. We examined the effects of the ADSC-CEs, added to the culture medium, on glial fibrillary acidic protein (GFAP) expression and proliferation in Schwann cells. Moreover, we examined the effects of the ADSC-CEs on neurite length in DRG neurons and PC12D cells. ADSC-CEs stimulated the proliferation of Schwann cells, elevated GFAP expression in these cells, and promoted the elongation of DRG neuron and PC12D cell projections. Notably, heat treatment of the ADSC-CEs abolished these effects. Together, these findings suggest that ADSC-CEs may have therapeutic application in peripheral nerve regeneration.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".