Profiling RNA Cargo in Extracellular Vesicles From hiPSC‐Derived Neurons of Alzheimer's Disease Patients
Bibliographic record
Abstract
ABSTRACT Alzheimer's disease (AD) is a major neurodegenerative disorder that affects more than 55 million people, with an incidence that is projected to triple by 2050. Despite continuous advancements in the field, reliable treatment and early detection strategies remain elusive. Extracellular vesicles (EVs) play a major role in cellular communication throughout the body. In this study, we assessed the cargo of neuronal‐specific EVs for their potential as AD biomarkers. We isolated EVs released from iPSC‐derived excitatory glutamatergic neurons generated from eight AD patients (ADiNEVs) and six healthy controls (iNEVs). We performed RNA‐sequencing and identified significant differences in RNA cargo between ADiNEVs and iNEVs. Notably, fewer small nuclear RNAs (snRNAs) were found in ADiNEVs. RNA transcripts significantly more abundant in ADiNEVs included MT‐CO1, PRR32 and IGSF8 messenger RNAs. We also observed fewer XIST long noncoding RNAs and miR‐7‐5p microRNA content in ADiNEVs. These findings suggest that precision medicine approaches, such as characterising the content of EVs from a patient's own cells, could advance early detection and management of AD.
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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) | 0.000 | 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 | 0.000 |
| 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".