Bone marrow‐derived extracellular vesicles modulate the abundance of infiltrating immune cells in the brain and exert an antiviral effect against the Japanese encephalitis virus
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) have regenerative capacity and have reported a beneficial effect on the Japanese encephalitis virus (JEV) in an encephalitis model. However, the MSCs do not cross the blood-brain barrier and have other disadvantages limiting their therapeutic utility scope. Recently, there has been a shift in concept from a cell-based to a cell-free approach using MSCs-derived extracellular vesicles (MSC-EVs). The MSC-EVs retain regenerative and immunomodulatory capacity as their parental cells. However, the role of MSC-EVs in limiting JEV pathology remains elusive. In this study, we have used Bone marrow (BM)-derived EV (BM-EVs) and assessed their effect on JEV replication and pathogenesis in primary neuronal stem cells and a murine model. The in vitro and in vivo studies suggested that BM-derived EVs delay JEV-induced symptoms and death in mice, improve the length of survival, accelerate neurogenesis in primary neuronal stem cells, reduce JEV-induced neuronal death, and attenuate viral replication. BM-EVs treatment upregulated interferon-stimulated genes. Flow cytometry analysis revealed a reduction in the frequency of macrophages. At the same time, CD4+ T cells and neutrophils were significantly augmented, accompanied by the alteration of cytokine expression with the administration of BM-EVs, reinforcing the immunomodulatory role of EVs during JEV-induced encephalitis. In conclusion, our study describes the beneficial role of BM-EVs in limiting JEV pathology by attenuating virus replication, enhancing antiviral response, and neurogenesis in primary neuronal stem cells. However, BM-EVs do not seem to protect BBB integrity and alter immune cell infiltration into the treated brain.
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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".