Transplantation of Human Adipose Tissue-Derived Stem Cells Delays Clinical Onset and Prolongs Life Span in ALS Mouse Model
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that selectively affects motor neurons in the cortex, brain stem, and spinal cord. The precise pathogenic mechanism remains unknown, and there is currently no effective therapy. We evaluated the therapeutic effects of human adipose tissue-derived stem cells (ASCs) in an animal model of ALS. Human abdominal subcutaneous fat tissues were obtained by simple liposuction from donors, and ASCs were isolated from the fat stromal vascular fraction. ASCs were found to differentiate into adipocytes, chondrocytes, osteocytes, and neurons. SOD1G93A ALS mice were divided into three groups: sham, intravenous (IV), and intracerebroventricular (ICV) groups. Human ASCs were transplanted in the ALS mice at 70 postnatal days before the appearance of clinical symptoms. Behavior of transplanted animals was assessed by rotarod test, paw grip endurance (PaGE), and reflex index. Mice in every group were sacrificed after 4 weeks posttransplantation. Transplanted ASCs were identified in the lumbar spinal cords with an antihuman mitochondria antibody and cell type-specific markers for neurons or astrocytes. Delayed onset of clinical symptoms (26 days) and extended survival of animals (24 days) were observed in ALS mice transplanted with ASCs via ICV route. ASCs were found to secrete high levels of neurotrophic factors such as NGF, BDNF, IGF-1, and VEGF. Reduction of apoptotic cell death by these factors was confirmed in cultured CNS cells and in the ALS spinal cord. These results indicate that transplantation of ASCs in ALS mice provides neuroprotective effects by production of cytokines/growth factors, delays disease progression, and prolongs the life span of ALS mice.
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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) | 0.001 | 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".