Induced Pluripotent Stem Cell-Derived Extracellular Vesicles Prevent Neural Stem Cell Senescence to Promote Cognitive Recovery after Traumatic Brain Injury
Bibliographic record
Abstract
Hippocampal neural stem cells (NSCs) have attracted significant attention due to their essential role in maintaining cognitive functions, such as memory and spatial orientation through neurogenesis. Cognitive impairment is a common and debilitating complication of traumatic brain injury (TBI), yet its underlying mechanisms remain poorly understood and effective clinical interventions are lacking. In this study, we observed persistent cognitive deficits in a mouse model of TBI, a phenomenon that has been widely documented in previous studies, and importantly, we found that these impairments were closely associated with increased hippocampal NSCs (H-NSCs) senescence. To investigate the cause of NSCs’ senescence, we analyzed cerebrospinal fluid samples from TBI patients and hippocampal tissues from TBI mice and identified persistently elevated levels of IL-1β post TBI. In vitro, IL-1β successfully induced NSCs’ senescence and suppressed neurogenesis. Induced pluripotent stem cell-derived small extracellular vesicles (iPSC-sEVs) reversed IL-1β-induced senescence and restored neurogenic potential in H-NSCs. In vivo, iPSC-sEVs alleviated cognitive deficits and H-NSC senescence after TBI. Integrated proteomic and NSC cell transcriptomic analyses revealed that the β-catenin/ID2/CDKN2B (p15 INK4b ) signaling axis plays a critical role in regulating H-NSC senescence, which was further validated through inhibitor experiments. In summary, our findings demonstrate that iPSC-sEVs attenuate NSC senescence and improve cognitive function following TBI via modulation of the β-catenin/ID2/CDKN2B (p15 INK4b ) axis.
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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 | 0.001 |
| 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".