Neuronal degeneration in striatal transplants and Huntington's disease: potential mechanisms and clinical implications
Bibliographic record
Abstract
Cell therapy offers the possibility of replacing degenerated neurons thereby improving the symptoms of neurodegenerative disorders such as Huntington's disease. However, clinical benefits in patients with Huntington's disease, if any, have been transient and modest. Grafts survived well at 18 months in one patient with Huntington's disease, but graft survival was markedly attenuated by 10 years in three other patients from this transplantation cohort. It is critical to delineate the causes of graft degeneration if such therapies will be utilized in patients with a goal of achieving meaningful clinical benefit. Similar challenges may also accrue to future stem cell therapies. Here we discuss the potential causes of suboptimal long-term graft survival in patients with Huntington's disease, including allograft immunoreactivity, microglial responses targeted to grafted cells and cell-to-cell neurotoxicity. We also discuss similar challenges and unique differences comparing neuronal grafts in patients with Parkinson's and Huntington's diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".