The complexity of extracellular vesicles: Bridging the gap between cellular communication and neuropathology
Bibliographic record
Abstract
Brain-derived extracellular vesicles (EVs) serve a prominent role in maintaining homeostasis and contributing to pathology in health and disease. This review establishes a crucial link between physiological processes leading to EV biogenesis and their impacts on disease. EVs are involved in the clearance and transport of proteins and nucleic acids, responding to changes in cellular processes associated with neurodegeneration, including autophagic disruption, organellar dysfunction, aging, and other cell stresses. In neurodegenerative disorders (e.g., Alzheimer's disease, Parkinson's disease, etc.), EVs contribute to the spread of pathological proteins like amyloid β, tau, ɑ-synuclein, prions, and TDP-43, exacerbating neurodegeneration and accelerating disease progression. Despite evidence for both neuropathological and neuroprotective effects of EVs, the mechanistic switch between their physiological and pathological functions remains elusive, warranting further research into their involvement in neurodegenerative disease. Moreover, owing to their innate ability to traverse the blood-brain barrier and their ubiquitous nature, EVs emerge as promising candidates for novel diagnostic and therapeutic strategies. The review uniquely positions itself at the intersection of EV cell biology, neurophysiology, and neuropathology, offering insights into the diverse biological roles of EVs in health and disease.
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 | 0.001 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 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".