Conclusive demonstration of iatrogenic Alzheimer’s disease transmission in a model of stem cell transplantation
Bibliographic record
Abstract
The risk of iatrogenic disease is often underestimated as a concern in contemporary medical procedures, encompassing tissue and organ transplantation, stem cell therapies, blood transfusions, and the administration of blood-derived products. In this context, despite the prevailing belief that Alzheimer's disease (AD) manifests primarily in familial and sporadic forms, our investigation reveals an unexpected transplantable variant of AD in a preclinical context, potentially indicating iatrogenic transmission in AD patients. Through adoptive transplantation of donor bone marrow stem cells carrying a mutant human amyloid precursor protein (APP) transgene into either APP-deficient knockout or normal recipient animals, we observed rapid development of AD pathological hallmarks. These pathological features were significantly accelerated and emerged within 6-9 months post transplantation and included compromised blood-brain barrier integrity, heightened cerebral vascular neoangiogenesis, elevated brain-associated β-amyloid levels, and cognitive impairment. Furthermore, our findings underscore the contribution of β-amyloid burden originating outside of the central nervous system to AD pathogenesis within the brain. We conclude that stem cell transplantation from donors harboring a pathogenic mutant allele can effectively transfer central nervous system diseases to healthy recipients, mirroring the pathogenesis observed in the donor. Consequently, our observations advocate for genomic sequencing of donor specimens prior to tissue, organ, or stem cell transplantation therapies, as well as blood transfusions and blood-derived product administration, to mitigate the risk of iatrogenic diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".