Recent advances in stem cell therapy: efficacy, ethics, safety concerns, and future directions focusing on neurodegenerative disorders – a review
Bibliographic record
Abstract
Neurodegeneration refers to the gradual loss of neurons and extensive changes in glial cells like tau inclusions in astrocytes and oligodendrocytes, α-synuclein inclusions in oligodendrocytes and SOD1 aggregates in astrocytes along with deterioration in the motor, cognition, learning, and behavior. Common neurodegenerative disorders are Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), spinocerebellar ataxia (SCA), and supranuclear palsy. There is a lack of effective treatment for neurodegenerative diseases, and scientists are putting their efforts into developing therapies against them. Stem cell therapy has emerged as a hope for neurodegenerative disorders since it is not only the damaged neurons that might be replaced, but other neuromodulators and neuroprotectors are secreted. Stem cell terminal differentiation before implantation ensures the implantation of correct cells and molecular markers like carbonic anhydrase II, CNPase (2',3'-cyclic nucleotide 3'-phosphohydrolase), myelin basic protein (MBP), and myelin oligodendrocyte glycoprotein (MOG) elucidate the differentiation. Secretion of various growth factors like epidermal growth factor (EGF), keratinocyte growth factor (KGF), vascular endothelial growth factor-α (VEGF-α), transforming growth factor (TGF), and macrophage inflammatory protein (MIP) supports cell survival, cell proliferation, blood vessel formation, axon regeneration, and neuroglial functional connection formation at the site of degeneration. Adverse effects of stem cell therapy, like teratogenicity and differentiation in different cells other than the desired one under the influence of microenvironment, are a few key concerns. Post-transplantation improved synaptic plasticity, apoptosis inhibition, and reduction in tau-phosphorylation and amyloid beta (Aβ) production has been observed in Alzheimer's patients. A large number of experimental, preclinical, and clinical studies have been conducted, and encouraging results have been obtained. The present review exhaustively discusses various kinds of stem cells, their usage in treating neurodegenerative disorders, limitations and challenges, and ethical issues related to stem cell therapy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".