Exercise-induced plasma-derived extracellular vesicles increase adult hippocampal neurogenesis
Bibliographic record
Abstract
Aerobic exercise enhances cognition in part by increasing adult hippocampal neurogenesis. One candidate mechanism involves extracellular vesicles (EVs), lipid bilayer particles released during exercise that transport bioactive cargo to distant organs, including the brain. We tested whether plasma-derived EVs from exercising mice (ExerVs) are sufficient to promote hippocampal neurogenesis and vascular coverage in young, healthy sedentary mice. EVs were isolated from the plasma of sedentary or exercising C57BL/6J mice after four weeks of voluntary wheel running, collected during the dark phase, corresponding to peak running activity, and injected intraperitoneally into sedentary recipients twice weekly for four weeks. To evaluate reproducibility, the study was conducted across two independent cohorts using identical procedures. ExerV-treated mice showed an approximately 50 % increase in BrdU-positive cells in the granule cell layer relative to PBS- and SedV-treated controls in both cohorts. Approximately 89 % of these cells co-expressed NeuN, indicating neuronal differentiation, whereas 6 % co-expressed S100β, indicating astrocytic differentiation. No changes were observed in vascular areas across groups. These findings demonstrate that systemically delivered ExerVs are sufficient to enhance hippocampal neurogenesis but not vascular coverage. ExerVs may represent a promising therapeutic strategy for conditions marked by hippocampal atrophy, given their ability to enhance adult neurogenesis. Future studies are needed to elucidate the mechanisms linking peripheral ExerV administration to increased neurogenesis, and to determine whether this enhancement can restore cognitive function under conditions of hippocampal damage.
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 | 0.000 | 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".