Connecting cellular mechanisms and extracellular vesicle cargo in traumatic brain injury
Bibliographic record
Abstract
Traumatic brain injury is followed by a cascade of dynamic and complex events occurring at the cellular level. These events include: diffuse axonal injury, neuronal cell death, blood-brain barrier break down, glial activation and neuroinflammation, edema, ischemia, vascular injury, energy failure, and peripheral immune cell infiltration. The timing of these events post injury has been linked to injury severity and functional outcome. Extracellular vesicles are membrane bound secretory vesicles that contain markers and cargo pertaining to their cell of origin and can cross the blood-brain barrier. These qualities make extracellular vesicles intriguing candidates for a liquid biopsy into the pathophysiologic changes occurring at the cellular level post traumatic brain injury. Herein, we review the most commonly reported cargo changes in extracellular vesicles from clinical traumatic brain injury samples. We then use knowledge from animal and in vitro models to help infer what these changes may indicate regrading cellular responses post traumatic brain injury. Future research should prioritize labeling extracellular vesicles with markers for distinct cell types across a range of timepoints post traumatic brain injury.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".